FOMITES IN INFECTIOUS DISEASE TRANSMISSION: A MODELING ...cf347cn1097/... · the dissertation investigates virus transfer between surfaces and virus recovery from surfaces, models

FOMITES IN INFECTIOUS DISEASE TRANSMISSION: A

MODELING, LABORATORY, AND FIELD STUDY ON

MICROBIAL TRANSFER BETWEEN SKIN AND SURFACES.

A DISSERTATION

SUBMITTED TO THE DEPARTMENT OF CIVIL AND

ENVIRONMENTAL ENGINEERING

AND THE COMMITTEE ON GRADUATE STUDIES

OF STANFORD UNIVERSITY

IN PARTIAL FULFILLMENT OF THE REQUIREMENTS

FOR THE DEGREE OF

DOCTOR OF PHILOSOPHY

Timothy Ryan Julian

December 2010

http://creativecommons.org/licenses/by-nc/3.0/us/

This dissertation is online at: http://purl.stanford.edu/cf347cn1097

© 2011 by Timothy Ryan Julian. All Rights Reserved.

Re-distributed by Stanford University under license with the author.

This work is licensed under a Creative Commons Attribution-Noncommercial 3.0 United States License.

ii



http://purl.stanford.edu/cf347cn1097

I certify that I have read this dissertation and that, in my opinion, it is fully adequatein scope and quality as a dissertation for the degree of Doctor of Philosophy.

Alexandria Boehm, Primary Adviser


James Leckie


Robert A Canales

Approved for the Stanford University Committee on Graduate Studies.

Patricia J. Gumport, Vice Provost Graduate Education

This signature page was generated electronically upon submission of this dissertation in electronic format. An original signed hard copy of the signature page is on file inUniversity Archives.

iii

Abstract

This dissertation examines the factors that influence fomite-mediated (e.g., indirect

contact) transmission of viral gastrointestinal and respiratory illness. Specifically,

the dissertation investigates virus transfer between surfaces and virus recovery from

surfaces, models human-fomites interactions to estimate exposure and infection risk,

and elucidates causal links between microbial contamination and illness in child care

centers. Indirect contact transmission refers to person-to-person transmission of dis-

ease via an intermediate fomite (e.g., inanimate object acting as a carrier of infec-

tious disease). The role of indirect contact in disease spread is poorly understood in

part because the transmission route of viral pathogens is often difficult to determine.

Transmission of respiratory and gastrointestinal viruses can occur through multiple

routes (e.g., direct contact, indirect contact, airborne, and common vehicle), and the

relative contribution of each route to total disease burden is unclear.

The first study in this dissertation examines virus transfer between skin and sur-

faces, a necessary step in fomite-mediated transmission of viral disease. In the study,

transfer of virus between fingerpads and fomites is explored in a laboratory setting.

Bacteriophage (fr, MS2, and φX174) are used as proxies for pathogenic virus, and

over 650 unique transfer events are collected from 20 different volunteers. The study

concludes that approximately one quarter (23%) of recoverable virus is readily trans-

ferred from a contaminated surface (e.g., a fomite) to an uncontaminated surface

(e.g., a finger) on contact. Using the large data set, the direction of transfer (from

fingerpads-to-fomite or fomite-to-fingerpad) and virus species are demonstrated to

both significantly influence the fraction of virus transferred by approximately 2-5%.

To investigate the relative importance of factors contributing to fomite-mediated

iv

transmission, a child’s risk of illness from exposure to a contaminated fomite is

modeled. Specifically, the model estimates a child’s exposure to rotavirus using a

stochastic-mechanistic framework. Simulations of a child’s contacts with the fomite

include intermittent fomite-mouth, hand-mouth, and hand-fomite contacts based on

activities of a typical child under six years of age. In addition to frequency of contact

data, parameters estimated for use in the model include virus concentration on sur-

face; virus inactivation rates on hands and the fomite; virus transfer between hands,

fomite, and the child’s mouth; and the surface area of objects and hands in contact.

From the model, we conclude that a childs median ingested dose from interacting

with a rotavirus-contaminated ball ranges from 2 to 1,000 virus over a period of one

hour, with a median value of 42 virus. These results were heavily influenced by se-

lected values of model parameters, most notably, the concentration of rotavirus on

fomite, frequency of fomite-mouth contacts, frequency of hand-mouth contacts, and

virus transferred from fomite to mouth. The model demonstrated that mouthing of

fomite is the primary exposure route, with hand mouthing contributions accounting

for less than one-fifth of the childs dose over the first 10 minutes of interaction.

Based on the findings from the model that concentration of virus on a fomite influ-

ences a child’s risk of illness, we investigate methods to recover virus from fomites. In

a literature review and subsequent meta analysis, we demonstrate that the outcome

currently used to describe virus contamination, positivity rate, is biased by the au-

thors’ selected sampling methods. We follow up, in the laboratory, with a comparison

of the identified methods and demonstrate that polyester-tipped swabs prewetted in

1/4-strength Ringer’s solution or saline solution is the most efficient sampling method

for virus recovery tested. The recommended method is compatible with plaque as-

say and quantitative reverse-transcription polymerase chain reaction, two techniques

used to quantify virus.

The link between hand / fomite contamination and infection risk was explored in a

field study at two child care centers over four months. Both respiratory and gastroin-

testinal disease incidence were tracked daily, while hand and environmental surface

v

contamination were monitored weekly between February 2009 and June 2009. Micro-

bial contamination was determined using quantitative densities of fecal indicator bac-

teria (e.g. Escherichia coli, enterococci, and fecal coliform) on hands and fomites as

well as presence/absence of viral pathogens (e.g. enterovirus and norovirus). Health

was monitored daily by childcare staff, who tracked absences, illness-related absences,

and symptomatic respiratory and gastrointestinal illness. The resultant data suggests

that increases in microbial contamination led to increases in symptomatic respiratory

illness four to six days later, in agreement with typical incubation periods for respira-

tory illness. Similarly, respiratory illness led to increases in microbial contamination

on hands during presentation of symptoms, and on fomites in the following three

days.

vi

Acknowledgments

Without the contributions of the people named below, as well as many people un-

named, the following dissertation would not have been possible.

First, to Dr. Alexandria B. Boehm who served as my advisor throughout my

time here at Stanford. It has been the utmost honor to have worked so closely with

such a brilliant and enthusiastic scientist. Dr. Boehm’s immediate understanding

of, and aid in resolving, the many obstacles I encountered along the way drove the

dissertation ever onward. Without her innumerable contributions, the work herein

would not have been possible.

Second, to Dr. James O. Leckie for the many enjoyable meetings over the years.

Our topics of discussion ranged from the intricacies of the experimental design to

Stanford sports, from data analysis to the political system. I never left Dr. Leckie’s

office without being excited by new research avenues and intrigued by his questions.

The projects within would not have been possible without the interest and exper-

tise of my other committee members. I thank Dr. Robert A. Canales for his advice

and mentoring; he lit my interest in exposure assessment modeling and environmen-

tal statistics. I also thank Dr. Lynn M. Hildemann, whose contributions during

the research proposal phase improved the quality of the work, and motivated the

field portion. Finally, I thank Dr. Yvonne A. Maldonado, committee chair, for her

contributions to the refinement of the dissertation through her expertise in pediatric

infectious disease.

Special thanks to Dr. Paloma Beamer, both mentor and friend. While a graduate

student, Dr. Beamer proposed the application of the chemical exposure modeling

framework to biological agents; her work was the impetus for this dissertation.

vii

Much of the brain power and laboratory work embedded in the dissertation was

contributed by research colleagues and friends. Specifically, Willa AuYeung, Daniel

Keymer, Karen Knee, Royal Kopperud, Blythe Layton, Joey McMurdie, Mia Mor-

gan, Allison Pieja, Todd Russell, Alyson Santoro, Lauren Sassoubre, Nick de Sieyes,

Francisco Tamayo, Emily Viau, Sarah Walters, George Wells, Simon Wong, Kevan

Yamahara, and Valentina Zuin. Additionally, Amy J. Pickering contributed signifi-

cantly, especially to the child care center study which would not have been possible

without her seemingly inexhaustible contributions of time and effort. Thanks, also,

to Joell Hamby, Brenda Sampson, and Sandra Wetzel for administrative support.

The decision to attend graduate school at Stanford University was most influenced

by my interactions with undergraduate advisors from Cornell University. Going for-

ward, I hope that I reflect the enthusiasm of Dr. Louis D. Albright, Dr. Rebecca L.

Schneider, and Dr. Michael B. Timmons in my approach to research and teaching.

Thanks to the many friends I am lucky to have made both before and during my

time at Stanford. Our time together, often spent camping, hiking, at dinner parties, at

rock concerts, and sharing never ending pasta bowls, has passed too quickly. Thanks

to Nathan, Naveen, and Sean for their lifelong friendships forged through heated

debates (academic, political, and otherwise) over glasses of Scotch.

To Sara, I cannot thank her enough for everything: from breakfast this morning

to love, calm, and balance every day. From help with statistical modeling to watching

music videos of The Darkness. She is my best friend.

And to my family: Thomas, Eileen, Tommy, Missy, and Dani. For my entire life,

they have provided an endless supply of encouragement, love, patience, and support.

Without them, none of this would be possible. Nor would it have been as enjoyable.

We should all be so blessed as to have such a wonderful family.

The dissertation research was funded by the Shah Family Research Fellowship for

Catastrophic Risk from Stanford University, the United States Environmental Pro-

tection Agency (USEPA) Science to Achieve Results Graduate Fellowship Program

and the UPS Endowment Fund at Stanford University. EPA has not officially en-

dorsed this dissertation and the views expressed herein may not reflect the views of

the EPA.

viii

Contents

Abstract iv

Acknowledgments vii

1 Introduction 1

1.1 Fomites in Infectious Disease Burden . . . . . . . . . . . . . . . . . . 1

1.2 Transmission Routes of Infectious Disease . . . . . . . . . . . . . . . 3

1.2.1 Vectorborne Transmission . . . . . . . . . . . . . . . . . . . . 3

1.2.2 Airborne Transmission . . . . . . . . . . . . . . . . . . . . . . 4

1.2.3 Common Vehicle Transmission . . . . . . . . . . . . . . . . . . 4

1.2.4 Contact Transmission . . . . . . . . . . . . . . . . . . . . . . . 5

1.3 History of Fomite-Related Research . . . . . . . . . . . . . . . . . . . 6

1.4 Quantitative Microbial Risk Assessment . . . . . . . . . . . . . . . . 11

1.5 Dissertation Organization . . . . . . . . . . . . . . . . . . . . . . . . 13

1.6 Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

1.7 Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

1.8 Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

2 Virus Transfer 22

2.1 Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

2.2 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

2.3 Materials and Methods . . . . . . . . . . . . . . . . . . . . . . . . . . 25

2.3.1 Volunteers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

2.3.2 Virus and Preparation of Inoculum . . . . . . . . . . . . . . . 25

ix

2.3.3 Plaque Assay . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

2.3.4 Virus Transfer . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

2.3.5 Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

2.4 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

2.4.1 Virus Transfer . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

2.5 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30


2.7 Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

2.8 Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

3 Rotavirus Exposure Model 39

3.1 Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40

3.2 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41

3.3 Model Description . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

3.4 Methods and Materials . . . . . . . . . . . . . . . . . . . . . . . . . . 45

3.4.1 Parameter Estimation . . . . . . . . . . . . . . . . . . . . . . 45

3.4.2 Model Approach . . . . . . . . . . . . . . . . . . . . . . . . . 49

3.4.3 Sensitivity Analysis . . . . . . . . . . . . . . . . . . . . . . . . 49

3.5 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50

3.5.1 Parameter Estimation . . . . . . . . . . . . . . . . . . . . . . 50

3.5.2 Model Results . . . . . . . . . . . . . . . . . . . . . . . . . . . 53

3.5.3 Sensitivity Analysis . . . . . . . . . . . . . . . . . . . . . . . . 55

3.6 Implications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56


3.8 Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60

3.9 Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63

4 Virus Recovery from Surfaces 72

4.1 Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73

4.2 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74

4.3 Materials and Methods . . . . . . . . . . . . . . . . . . . . . . . . . . 76

4.3.1 Review of Virus Surface Sampling Literature . . . . . . . . . . 76

x

4.3.2 Laboratory–Based Surface Sampling Method Comparison . . . 78

4.4 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82

4.4.1 Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . 82

4.4.2 Laboratory–based Surface Sampling Method Comparison . . . 83

4.4.3 qRT–PCR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84

4.5 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85


4.7 Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89

4.8 Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91

5 Health and Surfaces in Child Care Centers 96

5.1 Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97

5.2 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97

5.3 Methods and Materials . . . . . . . . . . . . . . . . . . . . . . . . . . 100

5.3.1 Sites . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100

5.3.2 Surveys / Demographic Data Collection . . . . . . . . . . . . 101

5.3.3 Sampling Scheme . . . . . . . . . . . . . . . . . . . . . . . . . 101

5.3.4 Health Data Collection . . . . . . . . . . . . . . . . . . . . . . 101

5.3.5 Hand Rinse Sampling . . . . . . . . . . . . . . . . . . . . . . . 102

5.3.6 Environmental Surface Sampling . . . . . . . . . . . . . . . . 103

5.3.7 Microbiological Methods . . . . . . . . . . . . . . . . . . . . . 103

5.3.8 Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105

5.4 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105

5.4.1 Surveys . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105

5.4.2 Health Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105

5.4.3 Hand Rinse Samples . . . . . . . . . . . . . . . . . . . . . . . 107

5.4.4 Environmental Samples . . . . . . . . . . . . . . . . . . . . . . 108

5.4.5 Health Associations with Hand and Surface Contamination . . 108

5.5 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109


5.7 Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114

xi

5.8 Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121

6 Conclusions 126

6.1 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 126

6.2 Future Directions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129

A Supplemental Material for Chapter 3 134

B Supplemental Material for Chapter 4 137

B.1 Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137

C Supplemental Material for Chapter 5 150

C.1 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 150

C.1.1 Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 150

C.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 152

C.2.1 Bivariate Correlations . . . . . . . . . . . . . . . . . . . . . . 152

C.2.2 Hand Contamination and Health Data. . . . . . . . . . . . . . 152

C.2.3 Hand Contamination and Environmental Contamination. . . . 153

C.2.4 Environmental Contamination and Health Data. . . . . . . . . 153

C.2.5 Health Associations with Hand and Surface Contamination . . 154

C.3 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 154

C.3.1 Use of Multiple Comparisons . . . . . . . . . . . . . . . . . . 154

C.4 Tables. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 156

xii

List of Tables

1.1 Gastrointestinal and Respiratory Viruses Transmitted Via Fomites . . 17

1.2 Evidence of Viruses in Fomite Transmission . . . . . . . . . . . . . . 18

2.1 Descriptive Statistics of Fraction Transferred for Each Subset. . . . . 35

2.2 Distribution Parameters for Fraction Transferred by Phage Type. . . 36

3.1 Input Parameters and Estimated Values for Exposure Model . . . . . 61

3.2 Sensitivity Analysis of Exposure Model . . . . . . . . . . . . . . . . . 62

4.1 Eluents Used to Remove Virus from Fomites . . . . . . . . . . . . . . 92

4.2 Implements Used to Remove Virus from Fomites . . . . . . . . . . . . 93

4.3 Comparison of Recovery of Infective Phage . . . . . . . . . . . . . . . 94

4.4 Surface Material, Implement, and Eluent Influence on Recovery . . . 95

5.1 Summary of Environmental Fomites Samples. . . . . . . . . . . . . . 115

5.2 Pathogen Detection PCR Parameters . . . . . . . . . . . . . . . . . . 116

5.3 Child Care Center Population Demographics . . . . . . . . . . . . . . 117

5.4 Child Care Center Population Health and Hygiene Knowledge . . . . 118

5.5 Frequency of Absenteeism and Symptomatic Illness in Child Care Centers119

5.6 Respiratory Illness as Function of Enterococci on Surfaces . . . . . . 120

B.1 Summary of Studies in Literature Review. . . . . . . . . . . . . . . . 147

B.2 Abbreviations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 148

B.3 Positivity Rate Metrics By Virus . . . . . . . . . . . . . . . . . . . . 149

C.1 New Illness Episodes Model . . . . . . . . . . . . . . . . . . . . . . . 157

xiii

C.2 Illness-Related Absences Model . . . . . . . . . . . . . . . . . . . . . 158

xiv

List of Figures

1.1 Infectious Disease Transmission Routes . . . . . . . . . . . . . . . . . 20

1.2 Steps Required for Fomite-Mediated Transmission . . . . . . . . . . . 21

2.1 Histograms of Fraction Virus Transferred . . . . . . . . . . . . . . . . 38

3.1 Schematic Model of Virus Transfer . . . . . . . . . . . . . . . . . . . 64

3.2 Simulated Timing of Contacts . . . . . . . . . . . . . . . . . . . . . . 65

3.3 Simulated Concentration, Exposure, and Dose Profiles . . . . . . . . 66

3.4 Temporal Trends in Concentration and Exposure Profiles . . . . . . . 67

3.5 Dose and Risk Boxplots . . . . . . . . . . . . . . . . . . . . . . . . . 68

3.6 Dose and Infection Risk as Function of Virus Concentration . . . . . 69

3.7 Temporal Trends in Dose . . . . . . . . . . . . . . . . . . . . . . . . . 70

3.8 Temporal Sensitivity Analysis of Fraction Transferred and Contact Fre-

quency . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71

4.1 Fraction of Virus Recovered By Implement Eluent Combinations . . . 90

5.1 Time Series of Absences at Child Care Centers . . . . . . . . . . . . . 122

5.2 Time Series of Reported Symptoms at Child Care Centers . . . . . . 123

5.3 Time Series of Bacteria on Hands in Child Care Centers . . . . . . . 124

5.4 Time Series of Bacteria on Fomites in Child Care Centers . . . . . . . 125

xv

Chapter 1

Introduction

1.1 Fomites in Infectious Disease Burden

Informed and successful disease control choices are made on the basis of understand-

ing infectious agent transmission routes (Hurst, 1996). Perhaps the most well-known

example is also the first: Dr. John Snow’s removal of a water pump handle in Lon-

don in 1854 halted a cholera epidemic. Similar contemporary interventions tailored to

impede transmission include hygiene education (Aiello et al., 2008), improved water

quality at the source and in the home, improved sanitation (Fewtrell et al., 2005),

social distancing (Glass et al., 2006), and respiratory masks (Jefferson et al., 2009).

The success of the interventions relies, in part, on the prior justification that the

transmission route is a major contributor to the overall disease burden. Contem-

porary understanding is that, in particular for respiratory and gastrointestinal virus,

transmission is complex, occurring via multiple, likely interrelated, routes (Goldmann,

2000; Nicas and Sun, 2006).

Understanding transmission of respiratory illness (RI) and gastrointestinal illness

(GI) disease spread, and how to prevent it, will aid reductions in burden. Annually,

the average adult has about 2 to 4 acute upper respiratory illnesses. Children have

approximately 6 to 8 (Heikkinen and Jrvinen, 2003). Per year, there are 400 million

cases of lower respiratory infections, which compared to upper respiratory illnesses,

are more likely to lead to hospitalization and death (Monto, 2002; Mathers et al.,

1

CHAPTER 1. INTRODUCTION 2

2008). Gastrointestinal illness is responsible for 4.5 billion cases annually, leading

to an estimated 1.7 million deaths in children under five every year (Mathers et al.,

2008). Combined, acute respiratory infections and diarrheal disease account for 34%

of the 10.4 million annual deaths among children under five (Mathers et al., 2008).

Viruses, especially those presented in Table 1.1, are commonly responsible for res-

piratory and gastrointestinal illlness. Although medical treatments, like antibiotics,

oral rehydration therapy, and zinc supplementation, are proving very effective (Hahn

et al., 2001; Lazzerini and Ronfani, 2008; Roth et al., 2010), one estimate suggests

that universal implementation of these methods can only reduce child mortality by an

additional 20% (Jones et al., 2003). Combining medical treatments with prevention

of disease spread can further reduce RI and GI morbidity. Reductions through pre-

vention of disease spread require an understanding of transmission routes. However,

the transmission of RI and GI is complex.

Contributing to the complexity is an incomplete understanding of indirect contact

transmission. Indirect contact transmission refers to person-to-person transmission of

disease via an intermediate fomite (i.e., inanimate object acting as a carrier of infec-

tious disease). Indirect contact, or fomite-mediated contact, is poorly understood due,

in part, to the nature of the transmission route. There are a number of ways fomites

can be contaminated with infectious disease, including contact with bodily fluids,

body parts, or other fomites and settling from airborne particles by talking, sneezing,

coughing, or vomiting (Hota, 2004; Boone and Gerba, 2007). Contamination of a

fomite may provide no obvious or visible evidence of infectious disease presence. Ad-

ditionally, the routes by which an infectious agent contaminates a fomite are equally

able to infect a susceptible individual without the intermediate fomite. Therefore, it

is often difficult to determine whether a transmission event occurred directly between

an infected host and a susceptible host, or the event occurred indirectly via a fomite.

Moreover, the factors that influence fomite-mediated transmission, as well as their

relative importance, are poorly understood. Not only is contamination of a fomite

a requisite step in indirect contact transmission, but viral persistence and transfer

to a susceptible individual are also required. To initiate infection via fomites, a

virus must be able to contaminate a fomite, persist on the fomite, come into contact


with a susceptible host, and to initiate infection in the susceptible host. Common

questions concerning RI and GI viruses include whether or not an etiological agent is

capable of fomite-mediated transmission, and with what efficiency relative to other

routes. Characteristics of viruses relevant to transmission via fomites are provided in

Table 1.2.

The dissertation presented here contributes to fundamental knowledge concerning

the factors that influence fomite-mediated transmission for viral disease. To bet-

ter understand fomite-mediated transmission, this chapter provides a background on

infectious disease transmission routes, a history of research relevant to the contem-

porary understanding of fomites, and a description of quantitative risk assessment

modeling. Modeling is a tool frequently used to assess factors that influence risk of

illness in transmission of communicable infectious disease.

1.2 Transmission Routes of Infectious Disease

Fomite-mediated transmission is a subset of contact transmission, one of the major

routes of infectious disease transmission. There are, arguably, four major routes: vec-

torborne, airborne, common vehicle, and contact (See Figure 1.1) (James and David,

2001; Mangili and Gendreau, 2005). The major transmission routes are not mutually-

exclusive. Rather, an etiological agent may utilize multiple routes to transfer between

infected and susceptible hosts. Similarly, the major routes are not necessarily distinct

categories. As an example, indirect contact transmission during preparation may re-

sult in a foodborne (i.e., common vehicle) outbreak.

1.2.1 Vectorborne Transmission

Vectorborne transmission is similar to fomite-mediated transmission only insofar as

to replace the role of inanimate objects with a living vector. Although fomites are

occasionally considered vectors (Lemon et al., 2008), this is not a strictly accurate

definition (James and David, 2001; Mangili and Gendreau, 2005). That is, vector-

borne transmission is the transfer of an infectious agent to a susceptible host via an


arthropod or vermin intermediary. Vectorborne diseases are among the top twenty

most common causes of death, worldwide, due in part to the ubiquity of malaria in

low income countries (Mathers et al., 2008). The vector carriage of the infectious

agent may simply be a case of mechanical transfer (like the role of a fomite), or

the agent may undergo biological transformations during carriage. Examples of the

former include dengue virus, west Nile virus, and yellow fever; of the latter include

malaria and African trypanosomiasis. In research investigating fomites as causative

agents in transmission of vectorborne diseases (specifically dengue virus and yellow

fever), no individual exposed to fomites was infected (Ashburn and Caraig, 1907).

1.2.2 Airborne Transmission

A second route of transmission is airborne, which typically refers to the aerosoliza-

tion and movement, over long distances, of pathogens from an infected individual to

a susceptible host. Airborne transmission often plays an important role in the con-

tamination of fomites, as initially aerosolized particles may settle onto surfaces (Nicas

and Sun, 2006). Similarly, resuspension from contaminated fomites may contribute

to airborne transmission (Nicas and Sun, 2006). A common phenomenon in airborne

viral transmission is the formation of viral droplet nuclei. Viral droplet nuclei are

formed due to water evaporation from expiration by an infected host. At less than

5 µm in diameter, the viral droplet nuclei can remain suspended for long periods

(Lowen et al., 2007). Recent evidence suggests that low humidity leads to increased

formation of viral droplet nuclei, and therefore more efficient transmission (Lowen

et al., 2007). The formation of viral droplet nuclei, however, is not a requirement

for airborne transmission. Bacteria, for example, are capable of being transmitted

via the airborne route. Tuberculosis is an example of an airborne bacteria (Mathers

et al., 2008).

1.2.3 Common Vehicle Transmission

Common vehicle transmission is often intertwined with fomite-mediated transmission,

but refers more specifically to the potential infection of multiple individuals via a


single carrier. Forms of common vehicle transmission include foodborne, waterborne,

and iatrogenic transmission. For common vehicle transmission to occur, the vehicle

needs to be contaminated prior to distribution to susceptible hosts. Food is often

contaminated in the environment prior to harvest, during processing for distribution,

or during preparation (e.g., inadequate hygiene or cooking) (Bresee et al., 2002).

Water, both recreational and drinking, is often contaminated in the environment, as

could occur due to poor sanitation, hygiene, and/or inadequate sewage or storm water

control (Craun et al., 2006). Drinking water, even if it was previously treated, can

be contaminated during delivery and/or storage. Examples of vehicles in iatrogenic

transmission, or transmission during medical procedure, include nonsterile injection

needles or catheters (Khan et al., 2000; Luijt et al., 2001), and/or infected blood or

organs (Iwamoto et al., 2003).

Fomites frequently contribute to infectious disease outbreaks that occur via com-

mon vehicle transmission. As an example, contamination of food during processing

or preparation can occur due to contact with a contaminated surface, like a cutting

board. Similarly, infection of a patient by using a nonsterile injection needle could

also be considered fomite-mediated transmission.

1.2.4 Contact Transmission

Fomite-mediated transmission is most often included in the fourth major mode of

transmission: contact. Contact transmission occurs most often either through direct

physical contact, which includes both casual (e.g., touching, kissing) and sexual con-

tact, or indirect contact via a fomite. Two other forms of contact transmission are

vertical transmission, which is defined as the transfer of disease from a mother to her

fetus either in utero or during child birth, and zoonotic transmission, which is the

transfer of disease between vertebrate animals and humans.

Often, the distinction between contact and other transmission routes is blurred.

Vectorborne transmission, as an example, is sometimes included as a subset of contact

transmission, especially when vector carriage is simply mechanical (i.e., no biological

transformations of the pathogen occur in the host) (Hurst, 1996). Similarly, the


production of large respiratory droplets during expirations from talking, coughing,

or sneezing blurs the distinction between contact and airborne transmission. Large

droplets can be spread over short distances and intercepted by a susceptible host or

fomite while settling. This form of transmission is sometimes considered a unique

transmission route (Friedman and Petersen, 2004), and other times considered a form

of contact transmission (Baron and Jennings, 1991; Hurst, 1996). Large droplet

settling is one way respiratory pathogens contaminate fomites, contributing to the

possibility of transmission via indirect contact (Nicas and Sun, 2006).

Contamination of an inanimate object is only the first step in transmission via

fomites (See Figure 1.2). Another requirement is that the etiological agent must

remain viable on the fomite for a period sufficient for the fomite to come into contact

with a susceptible individual. If it is able to persist long enough, the agent must then

be able to transfer from the fomite to a point of entrance on a susceptible individual.

For respiratory and gastrointestinal diseases, the point of entrance is most often a

mucous membrane, such as through the mouth, nose, ears, or eyes. Once transferred,

the agent must be able to initiate infection. In summary, the characteristics of the

etiological agent, the fomite, the infected individual, and the susceptible host, as

well as the interactions between the individuals and the fomite, influence efficacy of

fomite-mediated transmission.

1.3 History of Fomite-Related Research

Indirect contact via fomites was first identified by Italian physicist and scholar Giro-

lamo Fracastoro, in 1546 (Ravenel, 1931; Clendening, 1960). Fracastero did so in his

description of distinct transmission routes. His description included direct contact,

indirect contact, and a predecessor to aerosolization (specifically, the “transmi[ssion of

a disease] to a distance...merely by looking”) (Clendening, 1960). Fracastero posited

that the etiology of a disease determined the transmission route, using specific ex-

amples of contemporary diseases (e.g., scabies via indirect contact, and smallpox via

aerosolization). He also posited that contaminated fomites may remain so for “two to

three years”, and described porous objects (“linen, cloth, and wood”) as more likely


to act as fomites than nonporous ones (“iron, stone”) (Clendening, 1960). In so do-

ing, Fracastero laid the groundwork for the concept that inanimate objects contribute

to disease. The mechanism by which fomites acted remained elusive as Fracastoro’s

work predated Agostini Bassi’s germ theory (1835) by almost 300 years.

The knowledge that inanimate objects could spark disease outbreaks (in fact, the

Latin definition of “fomes” is “tinder”) provided an opportunity for one of the most

well-known examples of biological warfare in the New World. British colonialists,

during their efforts to combat the Natives in the 18th century, provided the Natives

with blankets and handkerchiefs from hospitals to “innoculate” them with smallpox

(Fenn, 2000). Epidemics that tore through Native populations in 1763 and 1764 likely

resulted from the provision of the blankets (Fenn, 2000).

The knowledge of fomites also aided in the prevention of outbreaks. As an exam-

ple during the height of a plague outbreak in 1835 in Alexandria, Egypt, the British

Privy Council quarantined all ships carrying Egyptian cotton into England (Thomp-

son, 1847; Plunket, 1879). The cotton was described as a “fomes”, referring to the

etiological agent perceived to be the cause of both the Egyptian epidemic and an

earlier epidemic in London that had occurred in 1665. To prevent the epidemic, the

cotton bales were to be “rip[ped] open” to “purify... the cotton” through exposure

“to sunlight and air” (Plunket, 1879).

In the following decades, yellow fever outbreaks on ships led to the incorrect at-

tribution of fomites as the causative transmission route. The prevailing evidence was

the occurrence of outbreaks on ships weeks after they had set sail (Bell, 1901). As no

sailor was symptomatic at launch, or in the days leading up to symptoms, sailors and

scientists attributed the outbreaks to contact with objects such as “personal cloth-

ing and books” (Bell, 1901). The implication of fomites prevailed until randomized

control trials conducted by Dr. Walter Reed (based on work first posited by Car-

los Finlay) proved mosquitoes were the vector for yellow fever (Clendening, 1960).

Specifically, Dr. Reed discovered that a period of approximately 12 days needed to

pass for a mosquito that had consumed blood of an infected individual to be able to

infect another (Bell, 1901). The uncertainty of the route of yellow fever transmission

mirrors contemporary uncertainty in transmission routes. As an example, respiratory


illness was perceived to be transmitted only through airborne transmission as little

as forty years ago.

Among the first work to rigorously test the hypothesis that a respiratory virus

could be transmitted via contact came in the 1970s. J. Owen Hendley and Jack

Gwaltney of the University of Virginia proposed that direct and indirect contact

contributed to transmission of rhinovirus, perpetrator of over one third of the cases

of the “common cold” (Hendley et al., 1973). Hendley and Gwaltney showed that

rhinovirus was shed from an infected patient, capable of surviving outside the host on

an environmental surface, and capable of infecting a susceptible individual who had

contacted the contaminated surface (See Figure 1.2) (Hendley et al., 1973). Their

work was among the first to delineate the steps necessary for a pathogenic agent to

be transmitted via fomites. Laboratory work confirming the ability of rhinovirus to

be transmitted via direct (hand-to-hand) and indirect (hand-to-surface) contact soon

followed (Gwaltney et al., 1978; Gwaltney, 1982).

The work by Hendley and Gwaltney was also among the first to demonstrate, con-

clusively, that fomites were a viable route for a respiratory pathogen. Prior to their

work, symptoms of coughing and sneezing associated with rhinovirus were thought to

contribute to its spread via airborne transmission (Hendley et al., 1973). Other stud-

ies, though, had tried and failed to demonstrate airborne transmission of rhinovirus

(Hendley et al., 1973).

Around the same time, interest in nosocomial infections was on the rise, par-

ticularly for a common respiratory pathogen (respiratory syncytial virus, or RSV).

Caroline Hall and R. Gordon Douglas, Jr. of the University of Rochester, citing the

early work of Hendley and Gwaltney, recognized that fomites may be contributing to

the spread of RSV in hospitals (Hall et al., 1981). In a series of papers investigating

RSV transmission via fomites, Hall et al. (1980); Hall and Douglas Jr (1981); and

Hall (1983) demonstrated that RSV is capable of following the necessary steps to be

transmitted via fomites. That is, RSV survives on surfaces, is readily transferred be-

tween surfaces and hands, and can infect susceptible hosts when a contaminated hand

contacts their nose or eyes. Scaling up from the laboratory to the field, Hall et al.

(1981) also examined the risk of handling infected infants at various levels of contact


and demonstrated that large droplet contact and indirect contact with fomites were

more efficient routes of RSV transmission than small particle aerosolization (Hall

et al., 1981).

The work by Hendley and Gwaltney along with the work of Hall and Douglas

contributed to a renewed interest in fomites in infectious disease transmission as they

demonstrated that fomites were an integral transmission route in diseases previously

perceived to be primarily airborne. In the following decades (1980s-1990s), research

on fomites increased four-fold, with studies examining their role in the transmission

of respiratory (Dick et al., 1987; Brady et al., 1990), gastrointestinal (Butz et al.,

1993; Wilde et al., 1992) and even bloodborne pathogens (Ferenczy et al., 1989).

The work during this decade followed closely the work of Hendley, Gwaltney, Hall,

and Douglas, in that it examined pathogen presence/absence on surfaces (Keswick

et al., 1983; Piazza et al., 1987; Wilde et al., 1992), persistence (Keswick et al., 1983;

Ansari et al., 1988; Abad et al., 1994), transfer (Jennings et al., 1988; Ansari et al.,

1988), and the relative efficiency of the indirect route of transmission (Dick et al.,

1987). The general acceptance of the role of fomites in infectious disease transmission

was highlighted, perhaps, during this period with the First European Meeting of

Environmental Hygiene in Dusseldorf in 1987.

Research on fomites began to abate in the mid-1990s. The focus of most of the

published articles on fomites during this time period continued to be their role in

nosocomial infections (McCluskey et al., 1996; Bures et al., 2000; Neely and Sittig,

2002; Das et al., 2002). Additionally, new work was published investigating the role

of fomites in animal diseases (Pirtle and Beran, 1996; Otake et al., 2002) as well as on

tracking the role of fomites in GI and RI outbreaks (Cheesbrough et al., 2000; Rogers

et al., 2000; Abad et al., 2001; Barker, 2001; Evans et al., 2002; Das et al., 2002). The

latter proved influential in the resurgence of fomites research over the past several

years (2004-2010).

In particular, growing concern over two communicable diseases, norovirus (a gas-

trointestinal virus) and influenza (a respiratory virus), contributed to a resurgence in

fomites research. Norovirus, first idenfied in 1972 (Kapikian et al., 1972), is the most


common cause of gastroenteritis in the United States due, in part, to its extreme con-

tagiousness (as few as 1 viral particles may be needed to cause illness (Teunis et al.,

2008), and there are as many as 14 secondary cases for each primary case (Heijne

et al., 2009)). Evidence suggests that direct and indirect transmission are important

routes for norovirus transmission. For example, the U.S. Centers for Disease Control

and Prevention report that 16% of norovirus cases are caused by person-to-person

spread (Norovirus: Technical Fact Sheet, http://www.cdc.gov/ncidod/dvrd/revb

/gastro/norovirus-factsheet.htm, accessed Sep 2010). Similarly, a series of studies

of outbreaks traced the source to environmental contamination of norovirus (Chees-

brough et al., 2000; Evans et al., 2002). Over the last decade, researchers have sought

to further investigate the relevance of fomites in norovirus outbreaks (Duizer et al.,

2004; Clay et al., 2006; Jones et al., 2007; Girard et al., 2010).

Similarly, outbreaks of influenza have increased interest in research on the poten-

tial role of fomites in transmission. Contemporary thought supports that aerosoliza-

tion of small particles, including viral droplet nuceli formation and large droplet

contact, are the primary transmission routes (Lowen et al., 2007; Tellier, 2009). Like

rhinovirus and RSV, influenza is a respiratory virus. Nevertheless, the contribution

of fomites continues to be debated (Brankston et al., 2007). Research to characterize

the role of fomites, prompted by the concern over a future pandemic, has mirrored

the early work on both rhinovirus and RSV. Specifically, published work has docu-

mented influenza survival on surfaces (Thomas et al., 2008; Sakaguchi et al., 2010),

disinfection (Rudnik et al., 2009; Weber and Stilianakis, 2008), detection on surfaces

(Boone and Gerba, 2005), and the relative efficacy of fomes-mediated transmission

relative to other routes (Brankston et al., 2007; Weber and Stilianakis, 2008; Tellier,

2009). Much of this work has been done in the context of tailoring interventions to

reduce infectious disease burden during a pandemic.

In total, the research dedicated to fomites has concluded that indirect contact is

an important route for transmission of respiratory and gastrointestinal illness. Nev-

ertheless, better quantitative data is needed. Research over the last half century has

delineated the steps required for an etiological agent to be efficiently transmitted

via fomites. Laboratory-scale studies, typically focusing on specific pathogens, have


demonstrated and quantified organisms’ abilities to transfer to and from fomites,

persist on fomites, and to remain infectious. Studies scaling up to the field have

demonstrated that contaminated fomites can initiate infection, and have also assessed

the efficacy of fomite-mediated transmission relative to other routes. Nevertheless, a

better characterization of the factors required for fomite-mediated transmission, and

their relationships, is needed. In fact, in a review of the role of fomites in transmis-

sion of respiratory and enteric viruses, the authors (Boone and Gerba, 2007) noted

the need for “better quantitative data”. Specifically, the Boone and Gerba identified

the need for better data on viral inactivation rates, viral transfer between surfaces,

and viral distribution and concentration on surfaces. The purpose of better data is

to improve and develop “risk assessment models that associate viral infection with

fomite contact” (Boone and Gerba, 2007).

1.4 Quantitative Microbial Risk Assessment

Contributing to the Boone and Gerba (2007) assertion that data are needed for risk

assessment models was the development, in the mid-1990s, of the framework for ap-

plying the reductionist approach of quantitative risk assessment (QRA) to infectious

disease. QRA is the “technical assessment of the nature and magnitude of a risk

caused by a hazard” (Jaykus et al., 1996) where the hazard can include “substances,

processes, action, or events” (Covello and Merkhofer, 1993). QRA was first developed

in the 1970s and later formalized with the benchmark publication “Risk Assessment

in the Federal Government, Managing the Risk”, known colloquially as the Red Book,

by the U.S. National Academy of Sciences in 1983 (NRC, 1983). Among the first ap-

plications of the QRA framework to assess infectious disease risk were assessments of

waterborne transmission (Haas, 1983; Gerba and Haas, 1988; Regli et al., 1991; Rose

et al., 1991), which led to the development of a codified framework for quantitative

microbial risk assessment by the International Life Sciences Institute (ILSI) in 1996

(ILSI, 1996) and revisited in 1999 (ILSI, 1999).

The framework for quantitative microbial risk assessment (QMRA) is adapted

from the QRA paradigm. The ILSI framework for microbial risks consists of three


phases: 1) problem formulation, 2) analysis, and 3) risk characterization (ILSI, 1996,

1999). Problem formulation “identifies the goals, breadth, and focus of the risk as-

sessment, the regulatory and policy context of the assessment, and the major factors”

(ILSI, 1999). Analysis develops exposure and dose-response assessments to work to-

ward risk characterization, which is a quantitative characterization of the likelihood,

type, and magnitude of human health effects (ILSI, 1999). Risk characterization also

incorporates a transparent accounting of uncertainty or variability contributions to

the final risk estimates (ILSI, 1999). A fourth phase (risk management) is occasion-

ally included in the risk assessment paradigm and encompasses the risk mitigation

and communication strategies (Haas et al., 1999; Covello and Merkhofer, 1993). A

major contribution of the ILSI framework for microbial risks was its emphasis on the

“dynamic and iterative process of the risk assessment process”, and that findings in a

later stage (e.g., risk characterization) should be used to refine and improve findings

from an earlier stage (e.g., analysis).

The paradigm for QRA as outlined in the Red Book for human health effects

was developed to account for risk from chemical exposures (NRC, 1983; Haas et al.,

1999). To adapt QRA to microbial hazards, complexities unique to pathogens need

to be considered (ILSI, 1996). The complexities include: 1) growth and/or inacti-

vation of pathogens, 2) non-heterogeneous pathogen distributions in environmental

matrices, 3) naturally or artificially acquired immunity, 4) asymptomatic infection,

5) secondary transmission (e.g., spread from an infected individual), 6) multiple end-

points (e.g., infection, illness, mortality), 6) potential for multiple exposure routes,

and 7) uncertainty in environmental concentration measurements (e.g., accuracy of

detection methods) (ILSI, 1996; Haas et al., 1999). In 1999, many of the first practi-

tioners of QMRA (Haas et al., 1999) summarized and applied the QMRA paradigm to

examples from many of the major transmission routes in the first and only textbook

on the topic, Quantitative Microbial Risk Assessment.

Despite the evidence that fomites play an important role in the transmission of dis-

ease, few studies have applied the framework of QMRA to model risk from fomites. In

those that have, the estimated risk typically relies on simplistic exposure assessments

that model human interaction with fomites based on estimates of the probability


that a contact event occurs (e.g., 10% chance a fomite is contacted by hand), a con-

stant frequency of the contact event (e.g., mouth is contacted by hand 0.08 times per

minute), or a constrained sequence of events (e.g., fomes touches hand, hand then

touches mouth) (Gibson et al., 1999; Chen et al., 2001; Gibson et al., 2002; Haas

et al., 2005; Nicas and Sun, 2006; Nicas and Best, 2008). Examples of fomite-related

quantitative microbial risk assessments include: 1) estimating risk rotavirus infection

from clothes laundering (Gibson et al., 1999), 2) estimating risk from contaminated

surfaces in health care settings (Nicas and Sun, 2006), 3) estimating risk of cross

contamination during food preparation (Chen et al., 2001), and 4) estimating risk

reductions acheived through hand hygiene (Gibson et al., 2002). Although the stud-

ies provide an important first step toward understanding the factors that influence

fomite-mediated transmission, they function as simplified models and do not fully

account for complexities of human-fomites interaction in field settings.

1.5 Dissertation Organization

This dissertation consists of six chapters devoted to furthering knowledge of the fac-

tors that contribute to fomite-mediated infectious disease transmission. This intro-

duction chapter (Chapter 1) provides background on the role of fomites in disease

transmission. The four middle chapters (Chapters 2-5) present original research in

the form of stand-alone manuscripts, each with its own introduction, methods, results,

and discussion sections. The final chapter (Chapter 6), provides general conclusions

and areas for future research. The references used throughout the dissertation are

merged and appear at the end. Co-authors, along with their contributions to each

chapter, are listed at the beginning in an introductory paragraph. I am first author

on all publications that have been or will be generated from the work included in this

dissertation as I was the primary person responsible for planning, conducting, and

writing each project.

If fomites play a significant role in viral disease transmission through hand contact,

virus must transfer from contaminated fingers to fomites and transfer from fomites


to fingers of a susceptible host. This sequence of events was explored through a lab-

oratory experiment using three bacteriophage species as proxies for pathogenic virus.

In Chapter 2 we demonstrate that approximately one quarter (23%) of recoverable

virus is readily transferred from a contaminated surface (e.g., a fomite) to an un-

contaminated surface (e.g., a finger) on contact. The chapter demonstrates, using a

robust data set, that the direction of transfer (from fingerpads-to-fomite or fomite-to-

fingerpad) and bacteriophage species both influence the fraction of virus transferred

by approximately 2-5%. The study also suggests that hand washing reduces the frac-

tion of virus transferred on contact due, potentially, to altered skin properties. This

mechanism may explain decreases in illness during handwashing interventions, along

with the current explanation that handwashing reduces pathogenic virus and bacteria

on the hands. In addition to implications concerning hand hygiene effectiveness, the

developed data set contributes to work on quantitative microbial risk assessments

examining fomites in disease transmission.

In Chapter 3 we combine data sets from the previous chapters with a literature

review to create a novel exposure and risk assessment model. The model, based on

a stochastic-mechanistic framework using a simulation of a child’s interaction with a

fomite, is among the first to incorporate detailed descriptions of sequential time se-

ries data modeling human-environment interaction in a microbial risk assessment. A

combined sensitivity and uncertainty analysis identifies the factors that most signifi-

cantly influence risk of infection. Although the analysis demonstrates that parameters

describing human interaction are influential, uncertainty of, and variability in, virus

concentration on fomites is shown to dominate risk of exposure, and therefore infec-

tion.

To improve estimates of microbial contamination on surfaces, we compare methods

used to recover virus from fomites in Chapter 4. The literature review and subse-

quent meta analysis demonstrate that the outcome currently used to describe virus

contamination, positivity rate, is biased by the authors’ selected sampling methods.

In the review, we identify the most promising virus recovery methods. We follow up,

in the laboratory, with a comparison of the identified methods and demonstrate that

polyester-tipped swabs prewetted in 1/4-strength Ringer’s solution or saline solution


should be the standardized method for virus recovery. The recommended method is

compatible with two common techniques used to quantify virus from the environment,

plaque assay and quantitative reverse-transcription polymerase chain reaction. Quan-

tification of virus from fomites is an important direction for future research, as few

identified papers on virus surface contamination have quantified virus, or indicators

of virus, contamination.

Chapter 5 examines the relationship between microbial contamination on surfaces

and adverse health outcomes in child care centers in Northern California. For four

months in 2009, we quantified fecal indicator bacteria on hands and surfaces twice

weekly at two child care centers. We simultaneously collected data on child absences

and observable symptoms of gastrointestinal and respiratory infection. Using statis-

tical modeling, we demonstrate that increased surface contamination both leads and

lags observable respiratory symptoms. The study is among the first to infer, using

longitudinal data, a causal link between indoor microbial contamination and health

outcomes.

The research presented in the dissertation addresses the role of fomites in infectious

disease transmission. The dissertation also contributes to the development of ideas for

future research directions. In Chapter 6, new hypotheses generated over the course

of the dissertation are discussed. Also in the final chapter is a general conclusion on

the role of fomites in disease transmission.

1.6 Acknowledgments

The author acknowledges Sara J. Marks and the Stanford University School of En-

gineering Technical Communication Program for suggestions to improve the chapter,

as well as to the website www.dezignus.com for hosting the royalty-free vector images

of people used in Figure 1.1 and Figure 1.2.


1.7 Tables


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1.8 Figures


Infectious Disease Transmission RoutesVectorborne Airborne

Common Vehicle Contact

Pathogens: malaria, yellow fever, dengue African trypansimiasisInterventions: insecticides, environmental mitigation, bed nets, window screens, insect repellents

Pathogens: in�uenza, measles, rhinovirus, respiratory syncytial virusInterventions: respiration masks, social distancing, closing public locations, blocking expirations, mechanical �ltration, ultraviolet radiation.

Pathogens: norovirus, enterovirus, rotavirus, poliovirus, rhinovirus, hepatitis AInterventions: water and food quality standards, hand and environmental hygiene, donor blood and organ screening, equipment sterilization

Pathogens: rotavirus, rhinovirus, norovirus enterovirus, hepatitis, human immunoviruss,Interventions: hand and environmental hygiene, pharmaceuticals, prophalxysis.

Direct

Indirect

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Foodborne

Waterborne

Iatrogenic

infected host

vector

susceptible host

susceptible hostsusceptible host

infected host

expiration

susceptible hostinfected host

infected host susceptible host

fomite

infected host (mother)

susceptible host (prenatal child)

susceptible hostcontaminated medicaldevice, blood, or tissue

susceptible host

contaminated foodstu�s

drinking water bathing

water recreationalwater

Figure 1.1: Infectious disease transmission routes as grouped into four common cat-egories with examples of common interventions used to reduce burden from examplepathogens. Arrows represent movement or transfer of pathogen


Fom

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Chapter 2

Virus transfer between fingerpads

and fomites

The results presented in this chapter originally appeared as a research article in

the December 2010 volume of the Journal of Applied Microbiology (Julian et al.,

2010). James O. Leckie and Alexandria B. Boehm appear as co-authors, for their

contributions to study design, data interpretation, and manuscript improvement.

22

CHAPTER 2. VIRUS TRANSFER 23

2.1 Abstract

Aims Virus transfer between individuals and fomites is an important route of trans-

mission for both gastrointestinal and respiratory illness. The present study examines

how direction of transfer, virus species, time since last handwashing, gender, and titer

affect viral transfer between fingerpads and glass.

Methods and Results Six hundred fifty-six total transfer events, performed

by twenty volunteers using MS2, φX174, and fr indicated 0.23 ± 0.22 (mean and

standard deviation) of virus is readily transferred on contact. Virus transfer is sig-

nificantly influenced by virus species and time since last handwashing. Transfer of

fr bacteriophage is significantly higher than both MS2 and φX174. Virus transfer

between surfaces is reduced for recently washed hands.

Conclusions Viruses are readily transferred between skin and surfaces on con-

tact. The fraction of virus transferred is dependent on multiple factors including

virus species, recently washing hands, and direction of transfer likely due to surface

physicochemical interactions.

Significance and Impact of Study The study is the first to provide a large data

set of virus transfer events describing the central tendency and distribution of fraction

virus transferred between fingers and glass. The data set from the study, along with

the quantified effect sizes of the factors explored, inform studies examining role of

fomites in disease transmission.

Keywords Virus transfer, surfaces, fomites, hand hygiene, environmental hy-

giene, quantitative microbial risk assessment, bacteriophage.


2.2 Introduction

To better understand transmission routes for viral disease and develop more refined

quantitative microbial risk assessment models (Atkinson and Wein, 2008; Nicas and

Jones, 2009; Julian et al., 2009) additional information on the importance of fomites

in the transmission of viruses is needed (Boone and Gerba, 2007; Brankston et al.,

2007). Insight into the role of fomites in the transmission of infectious disease can be

obtained by studying the transfer of viruses between skin and surfaces.

Virus transfer between skin and surfaces can be described quantitatively by the

fraction of virus on a contaminated (donor) surface that is transferred on contact to

a recipient surface (Reed, 1975; Gwaltney, 1982; Ansari et al., 1988; Mbithi et al.,

1992; Rusin et al., 2002). This fraction could be modulated by a number of factors

including the donor / recipient surfaces and the virion surface.

Previous studies have reported a wide range of transfer fractions (0.0001 to 0.67)

for transfer of a single bacteriophage (e.g., PRD-1) or pathogenic virus (e.g., rotavirus,

hepatitis A, human parainfluenza virus-3, rhinovirus) between skin and various sur-

faces (Reed, 1975; Ansari et al., 1988; Mbithi et al., 1992; Rusin et al., 2002; Bidawid

et al., 2004). The range of transfer fractions is significantly influenced by the type of

surface (porous or non-porous) contacted by the skin, with transfer between porous

and food (e.g., cloth, lettuce, ham, beef, and carrots (Rusin et al., 2002; Bidawid

et al., 2004)) surfaces generally lower than transfer to non-porous (e.g., stainless steel

and plastic (Reed, 1975; Rusin et al., 2002; Bidawid et al., 2004)) surfaces.

Only one published study has examined the transfer between skin and surface

of more than one virus. In particular, Ansari et al. (1991) reported a difference in

fraction transferred for rhinovirus and human parainfluenza virus-3 between fingers

and metal disks. However, the small sample size of the study presumably precluded

statistical analysis.

The present study explores how viral species and factors including inoculum size,

direction of transfer, and skin condition affects virus transfer. We quantify the transfer

of three different viruses, MS2, fr, and φX174, between fingerpads and a glass surface.

Additionally, we applied experimental treatments to isolate the effects of the following


on virus transfer: (1) inoculum size, (2) direction of transfer, and (3) skin condition

defined by the gender and time since last hand washing. Inoculum size may influence

fraction of virus transferred as the phenomenon was demonstrated in bacterial transfer

by Montville and Schaffner (2003). Direction of transfer refers to the direction that

virus is transferred, such as from skin-to-fomite versus from fomite-to-skin. Gender

may influence virus transfer because men typically have a significantly lower skin pH

(van de Vijver et al., 2003). Similarly, hand washing shifts the biological and chemical

characteristics of the skin by decreasing organic and inorganic constituents (e.g.,

sebum, sweat, microflora), increasing pH, and decreasing hydrophobicity (Elkhyat

et al., 2001; Kownatzki, 2003; Barel et al., 2009). To our knowledge, this is the first

study to examine the effects of virus species, inoculum size, and skin condition on

virus transfer between skin and a surface.

2.3 Materials and Methods

2.3.1 Volunteers

Permission of the Stanford University Research Compliance Office for Human Sub-

jects Research was obtained prior to the study. Volunteers included 8 males and 12

females, with an age range of 20-32 years. To standardize unwashed state of volun-

teers’ hands, volunteers washed their hands for 15 seconds using soap and water at

least 1.5 hours before the experiment, and avoided eating or going to the restroom

within that time frame. No brand or type of soap was recommended or provided, and

no effort was made to account for residual effects of soap products used before the

experiment.

2.3.2 Virus and Preparation of Inoculum

This study quantifies transfer of three different bacteriophage (MS2, fr, and φX174)

obtained from the American Type Culture Collection (ATCC). MS2 (ATCC #15597-

B1), fr (ATCC #15767-B1), and φX174 (ATCC #13706-B1) strains were chosen

because they have similar size (19-27 nm) and shape (icosahedral, no tail) to several


human viruses, such as norovirus (Abbaszadegan et al., 2007). MS2 and fr bacterio-

phage are both +-sense RNA viruses of the Leviviridae family, with similar surface

characteristics but different isoelectric points (3.9 and 8.9, respectively) (Gerba, 1984;

Liljas et al., 1994; Dowd et al., 1998; Herath et al., 1999). φX174 is a single stranded

DNA virus of the Microviridae family with an isoelectric point of 6.6 (Gerba, 1984;

Dowd et al., 1998).

The inoculum used in the study was prepared by propagating the model viruses

to a concentration of 108-1010 plaque forming units (PFU)/ml in phage buffer (Reddy

et al., 2006). The propagated virus was then enumerated and diluted to approximately

105 to 106 PFU/ml using tryptic soy broth (TSB, pH of 7.2 ± 0.2) to be used as virus

stock. TSB is an organic-rich media intended to act as a model for the broad range of

matrices in which respiratory and gastrointestinal viruses contaminate fomites (e.g.

vomitus, urine, feces, mucus, and saliva). Use of homogeneous and well-characterized

TSB was intended to reduce variability introduced by use of natural media such as

fecal suspensions, mucus, or saliva. The virus stock was enumerated during every

experiment to confirm titer.

2.3.3 Plaque Assay

The double agar layer procedure was used to enumerate virus (USEPA, 2001). The

hosts were Escherichia coli K12-3300 (ATCC #19853) for fr, E. coli HS(pFamp)R

(ATCC #700891) for MS2, and E. coli CN-13 (ATCC #700609) for φX174. The

double agar layer procedure was chosen to estimate the fraction of infective virus

transferred on contact.

2.3.4 Virus Transfer

To determine the amount of virus transferred on contact between a fingerpad and

a nonporous glass surface, we used a protocol adapted from Ansari et al. (1991).

Specifically, we inoculated either between 100 and 600 or between 1000 and 6000

PFU diluted in TSB on the donor surface in an aliquot of 5 µl to represent low

and high titers, respectively. Borosilicate coverslips are uniform, smooth, and clean


surfaces providing a proxy for non-porous fomites with consistent characteristics. All

surfaces, including the fingerpads, were subsequently allowed to visibly dry while

supervised by the technician before contact between surfaces was made to mimic

drying after natural contamination events. To verify that the inoculate remained on

the fingerpads, the volunteer was supervised during the visible drying. All samples

from which no virus could be recovered from either the donor or recipient surface

following the transfer event were removed from analysis.

The volunteer placed the donor and recipient surfaces in contact for 10 ± 1 s

with an average constant pressure of 25 kPa (range of 16 kPa-38 kPa) controlled by

counterbalancing a triple beam balance weighted to 500 g. 25 kPa is comparable to the

pressure exerted by a child while gripping an object, the pressure exerted locally on

the fingerpads for adults using handtools, and the pressure used in studies examining

transfer of soil from surfaces to skin (Link et al., 1995; Hall, 1997; Ferguson et al.,

2009). We used a cotton-tipped swab applicator, wet in 500 µl of phosphate buffer

saline (PBS, 1 mM potassium phosphate monobasic, 155 mM sodium chloride, and 3

mM sodium phosphate dibasic, pH of 7.4 ± 0.05, from Invitrogen, Carlsbad, CA), to

remove virus from the surfaces. The applicator was wiped firmly against the surface

in a sweeping, rotating, motion for 10 s before being placed back into the remaining

PBS and vortexed for 10 seconds. We used separate swabs to remove virus from the

donor and recipient surfaces. Samples were aliquoted into 100 µl of 100, 10−1, and

10−2 dilutions in PBS; the dilutions were assayed using the double agar layer method

(USEPA, 2001). The range of detection for this method is 10 PFU to 200000 PFU.

If virus was unrecoverable from a surface, the lower detection limit of 10 PFU was

used as an estimate for the virus recovered. The fraction transferred (f) is defined

as PFU recovered from the recipient surface (RR), relative to PFU recovered from

the sum of the donor (RD) and the recipient surfaces, as previously described (Rusin

et al., 2002):

f =RR

(RR +RD)(2.1)

Dessication, or the drying of the inoculum on the surface, results in a loss of virus

titer (Ansari et al., 1988, 1991; Rusin et al., 2002). Because the surface is dried prior


to the transfer event, the seeded inoculum is higher than the sum of virus recovered

from donor and recipient surfaces after the transfer event. We chose to calculate f

using just recoverable virus from the donor and recipient surfaces (Equation 2.1) so

that the virus inactivated by dessication is not included in the fraction of virus trans-

ferred estimated by Equation 2.1. We assume that the relatively short time of the

contact event and subsequent hand and surface sampling does not contribute to loss in

titer due to inactivation. The experimental design varied factors including low/high

titer and direction of transfer, with blanks and replicates across the 10 fingerpads.

Four randomly chosen fingerpads were assigned the following four titer/direction-of-

transfer factor combinations: (1) low titer/glass-to-fingerpad, (2) low titer/fingerpad-

to-glass, (3) high titer/glass-to-fingerpad, and (4) high titer/fingerpad-to-glass. Four

additional fingerpads were assigned the same factor combinations. As all factor levels

of the fingerpads of the first set were identical to the factor levels of the fingerpads

on the second set, the second set of contact events are defined as replicates for the

contact events from the first set. In this manner, every contact event had a corre-

sponding replicate contact event. The remaining two fingerpads (one on each hand)

were selected to act as blanks. A blank is defined as a transfer event where fingerpad

or glass was inoculated with TSB that did not contain any virus. After the initial

10 transfer events were completed, the volunteers washed their hands for 15 s us-

ing Softsoap c© antibacterial liquid hand soap (Colgate-Palmolive, New York, NY),

rinsed in tap water, and dried with a Kleenex c© scientific cleaning wipe (Kimberly-

Clark, Irving, TX) under the technician’s instruction. We then used the same factor

assignments for each fingerpad to measure transfer for the ’washed’ hands. Twenty

volunteers performed the experiment using MS2 bacteriophage, thirteen of the twenty

volunteers repeated the experiment using φX174 bacteriophage, and ten of the thir-

teen repeated a third time using fr bacteriophage. Ten volunteers completed all 3

experiments. Temperature and relative humidity were recorded from a thermometer

and hygrometer (Springfield Precision Instruments, Wood Ridge, NJ) kept at the

sampling location.


2.3.5 Statistics

All statistics were performed using the R statistical software package (R: A Language

for Statistical Computing, version 2.9.0, R Foundation for Statistical Computing, Vi-

enna, Austria). Where appropriate, descriptive statistics (mean, median, and stan-

dard deviation) are reported. Statistical significance was assessed using a significance

level of α = 0.05. The significance of experimental factors (direction of transfer,

gender, virus species, time since last handwash, and titer) on percent of virus trans-

ferred was assessed using n-way ANOVA on untransformed data. Tukey’s post-hoc

test assessed significant differences between the transfer of each phage type. Distri-

bution parameters for normal, lognormal, and Weibull distributions are reported for

the data on fraction virus transferred between surfaces (f) stratified by phage type.

These distributions are used to describe microbial and/or chemical transfer (Chen

et al., 2001; Beamer, 2007; Perez Rodrıguez et al., 2007). Five-fold cross-validation

and Kolmogov-Smirnoff methods were used to determine distribution parameters and

goodness-of-fit.

2.4 Results

2.4.1 Virus Transfer

f was quantified for 656 transfer events. Eleven transfer events (<2% of total trans-

fers) of the original 688 were excluded because of a laboratory error (e.g. mislabeling

and failure to add host) involving at least one of the two samples (donor or re-

ceipient surface). An additional twenty-one transfer events (<3% of total transfers)

were excluded because virus could not be recovered from both donor and recipient

surfaces after the transfer. All blanks were negative, implying fingerpads were not

contaminated prior to study and no cross-contamination occurred during inoculation.

Aggregating data for all three virus species, ranged from 0.001 to >0.999 with a me-

dian, mean, and standard deviation of 0.18, 0.23, and 0.22, respectively. Median,

mean, and standard deviation of f were 0.32, 0.31, and 0.20, respectively, for fr; 0.18,

0.23, 0.21, respectively, for MS2; and 0.09, 0.19, 0.24 for φX174.


An n-way ANOVA investigated treatment effects on f . Gender (p = 0.42) and titer

(p = 0.79) were not significant. Direction of transfer (p = 0.01) and time since last

hand wash (p = 0.002) were significant, with glass-to-fingerpad and unwashed hands

transferring a greater fraction than fingerpad-to-glass and washed hands, respectively.

Additionally, virus species was significant (p < 0.001). f was larger for fr than for

both MS2 (Tukey’s test p < 0.001) and φX174 (p < 0.001). f was not significantly

different between MS2 and φX174 (p = 0.16). The mean, median, and standard

deviation of f are presented in Table 2.1 grouped by significant factors (e.g., glass-

to-washed finger transfer of MS2 bacteriophage, unwashed finger-to-glass transfer of

fr bacteriophage, etc).

Parameters describing the distribution of f were determined for normal, lognor-

mal, and Weibull distributions and are available, with estimates of goodness-of-fit,

separated by virus species, in Table 2.2. Virus species impacts not only mean f , but

also the best-fit distribution; MS2 and φX174 are right-skew while fr bacteriophage

has a more left-skew distribution. As evidence, histograms of the data with corre-

sponding best fit probability density functions are provided in Figure 2.1, separated

by virus species and direction of transfer.

Temperature and relative humidity ranged from 20-22◦C and 45-60%, respectively,

over the course of the study. No statistically significant correlation (using Spearman’s

correlation coefficient) between temperature and f was found for fr (ρs = 0.06, p =

0.47), MS2 (ρs = 0.05, p = 0.65), or φX174 (ρs = −0.02, p = 0.75) or between relative

humidity and f for fr (ρs = 0.08, p = 0.31), MS2 (ρs = −0.06, p = 0.57), or φX174

(ρs = 0.04, p = 0.59).

2.5 Discussion

We demonstrate that viruses are readily transferred between skin and a model fomite

surface. Aggregating 656 viral transfer events, the mean fraction of virus transferred,

f , is 0.23 ± 0.22 (mean and standard deviation), consistent with previous studies

on virus transfer (Ansari et al., 1991; Mbithi et al., 1992; Rusin et al., 2002) and

may be applicable as transfer estimate for viruses of similar size and shape, such as


norovirus. The relatively large sample sizes of volunteers and contact events provide

robust data to estimate distributions to describe f , an important parameter needed

for quantifying microbial risk (Gibson et al., 1999; Nicas and Sun, 2006; Wein and

Atkinson, 2009), especially in models that utilize activity data (Julian et al., 2009). f

is influenced by the virus species, the direction the virus is transferred (i.e., fingerpad-

to-surface or surface-to-fingerpad), and the characteristics of an individual’s skin, in

particular whether or not the hands have recently been washed. Although statistically

significant, the factors we identified as influential may change the fraction of virus

transferred by, at most, only 5-10%. This is small relative to the effect of changing

the porosity of the fomite surface which has been shown to shift f by as much as 2

orders of magnitude (Scott and Bloomfield, 1990; Rusin et al., 2002). Although the

contribution of fomites relative to other transmission routes in perpetuating disease

burden remains uncertain, the present study suggests it is specific to the etiological

agent and ameliorated through frequent hand washing.

Virus species affects both the mean and distribution of f . Our work expands

on the work of Ansari et al. (1991) who observed transfer differences between two

human viruses using 18 total transfer events, by measuring over 600 transfer events

with three different viruses. Our high number of observed transfers allowed rigorous

statistical testing of treatments. Our results also demonstrate that f is influenced

by the interaction of virus species and direction of transfer (Table 2.1). In other

words, f depends on the direction of transfer, but precisely how well depends on

viral species. This is consistent with observations described in the literature. Ansari

et al. (1991) demonstrated human parainfluenza type 3 virus transfer is greater from

fomite-to-fingers than fingers-to-fomite, while Mbithi et al. (1992), using hepatitis A

virus, demonstrated the reverse: greater transfer from fingers-to-fomite than fomite-

to-fingers.

Washing fingerpads prior to a virus transfer event reduces f . The reduction in

virus transfer due to washing is greater for fingerpad-to-glass transfer than glass-

to-fingerpad transfer. Changes in moisture level and pH on skin from handwashing

(Gfatter et al., 1997), or other residual effects from the soap may contribute to this

effect. To investigate the causal mechanism of reductions in f due to hand washing,


future studies could incorporate moisture and pH measurements of the volunteers’

fingerpads.

The impact of hand washing with soap and water on reduction of gastrointesti-

nal and respiratory illness is well documented (Aiello et al., 2008), and is generally

attributed to the reduction of pathogenic bacteria and virus on the hands (Curtis

et al., 2000; Pickering et al., 2010). The results suggest that reduced viral trans-

fer during hand-surface contacts could also contribute to illness reduction. Further

study of virus transmission may elucidate whether or not this finding extends to field

conditions.

The influence of virus species on f could be due to the physicochemical proper-

ties of the virus. The surfaces, suspension media, and contact mechanics were kept

constant throughout the study, and the experiments were carried out in ambient

laboratory conditions such that temperature and humidity varied over small, but re-

alistic, ranges. Because the viruses were the same shape (icosahedral), we attribute

the observed differences in f between virus species to the different sizes (19-27 nm)

and chemical properties of the virus capsids. In this experiment, the bacteriophage

studied (MS2, φX174, and fr) have different net surface charge, as evidenced by the

different isoelectric points (3.9, 6.6, and 8.9, respectively) (Dowd et al., 1998; Herath

et al., 1999) and different hydrophobicities. Specifically, φX174 was identified as the

most hydrophilic and MS2 as the most hydrophobic in a study of 13 virus species

by Shields and Farrah (2002); fr was not tested. Further research in this area is

warranted.

Neither gender, inoculum size, temperature, nor humidity significantly influenced

f . Significant differences in skin characteristics due to gender, such as pH, have

previously been documented but the differences are small (pH of male skin was 4.7,

female skin was 5.0) (van de Vijver et al., 2003). This difference in pH was not

large enough to affect viral transfer in the present study. Inoculum size also did

not significantly influence f , in contrast to previous work with bacteria that showed

inoculum size significantly influenced bacterial f over multiple orders of magnitude

(Montville and Schaffner, 2003). Perhaps the range of titer we explored (one order of

magnitude) was too low to observe an effect. Similarly, as neither temperature nor


relative humidity were explicitly investigated in this study, the range in temperature

(20-22◦C) and relative humidity (45-60%) may have been too small to observe an

effect on f .

There are several limitations to our study design. We minimized inter-trial vari-

ability by using glass surfaces, controlling for duration and pressure of contact, and

using the same group of volunteers. In field conditions, such as when an individual

contacts a virus-contaminated surface, variation may be greater as transfer events oc-

cur between a wide range of surfaces over a range of durations and contact pressures.

The use of an infectivity assay (the double agar layer method) does not provide infor-

mation on non-infective virus particles transferred on contact. Similarly, one plaque

forming unit may be more than one infective viral particles (Galasso and Sharp, 1962).

Accounting for the presence of non-infective virus particles or multiple infective virus

particles in one plaque may alter the fraction of infective virus transferred. Future

studies could incorporate molecular methods to better understand transfer influence

of non-infective particles and multiple virus per plaque forming unit on transfer.

2.6 Acknowledgments

This work was supported, in part, by the Shah Research Fellowship of Stanford

University and by the United States Environmental Protection Agency (EPA) under

the Science to Achieve Results (STAR) Graduate Fellowship Program. EPA has not

officially endorsed this publication and the views expressed herein may not reflect the

views of the EPA. The authors acknowledge the volunteers who participated in this

study. Additionally, the authors thank the Boehm Lab, Robert Canales, Francisco

Tamayo, and the anonymous reviewers who assisted with the work and/or provided

suggestions for improving the manuscript.


2.7 Tables


Phage Direction Handwash n µ median σMS2 Finger-to-glass Unwashed 75 0.24 0.18 0.24

Washed 75 0.15 0.10 0.16Glass-to-finger Unwashed 75 0.25 0.19 0.23

Washed 80 0.26 0.21 0.19φX174 Finger-to-glass Unwashed 49 0.26 0.16 0.28


Washed 47 0.11 0.04 0.18fr Finger-to-glass Unwashed 36 0.28 0.25 0.21


Washed 40 0.39 0.40 0.11

Table 2.1: The number of trials (n), mean (µ), median, and standard deviation (σ)of f for data subset by factors determined to be significant via n-way ANOVA (virusspecies, direction of transfer, and skin condition as determined by time since lasthandwash)


Normal Lognormal Weibull

Phage Type µ σ p-value µ σ p-value shape scale p-value

MS2 0.23 0.22 0.09 -2.1 1.4 0.18 0.96 0.22 0.12φX174 0.19 0.24 0.03 -2.6 1.5 0.43 0.77 0.16 0.84fr 0.31 0.20 0.45 -1.6 1.1 0.14 1.4 0.34 0.66All Phage 0.23 0.22 <0.01 -2.1 1.4 <0.01 0.94 0.23 0.09

Table 2.2: The parameters (mean (µ), standard deviation (σ), shape, and scale) andgoodness-of-fit for fitting normal, lognormal, and Weibull distributions to the frac-tion of virus transferred as determined by 5-fold cross validation. Parameters andgoodness-of-fit are determined for each bacteriophage individually, and all bacterio-phage aggregated


2.8 Figures


Fraction Transferred0.0 0.2 0.4 0.6 0.8 1.0

n= 95 n= 155

0.0 0.2 0.4 0.6 0.8 1.0

n= 80 n= 330

Normal Weibull Lognormal

n= 99

φX174

n= 150

MS2

n= 76 n= 325

ALL

Gla

ss-to

-Fi

nger

pad

Fing

erpa

d-to

-Gla

ss

Density

12345

12345

0.0 0.2 0.4 0.6 0.8 1.00.0 0.2 0.4 0.6 0.8 1.0

fr(a) (b)

(e) (f) (g) (h)

(c) (d)

Figure 2.1: Histogram of f for (a) φX174 fingerpad-to-glass, (b) MS2 fingerpad-to-glass, (c) fr fingerpad-to-glass, (d) all bacteriophage fingerpad-to-glass, (e) φX174glass-to-fingerpad, (f) MS2 glass-to-fingerpad, (g) fr glass-to-fingerpad, and (h) allbacteriophage glass-to-fingerpad. The probability density function is overlaid on eachhistogram using the parameters reported in Table 2.2

Chapter 3

A Model of Exposure to Rotavirus

from Nondietary Ingestion Iterated

by Simulated Intermittent

Contacts

The results presented in this chapter originally appeared as a research article in

the May 2009 issue of the journal Risk Analysis (Julian et al., 2009). Robert A.

Canales contributed extensively to the modeling and statistical analysis presented

and is a co-author on the publication. James O. Leckie and Alexandria B. Boehm

also appear as co-authors, for their contributions to study design, data interpretation,

and manuscript improvements.

39

CHAPTER 3. ROTAVIRUS EXPOSURE MODEL 40

3.1 Abstract

Existing microbial risk assessment models rarely incorporate detailed descriptions

of human interaction with fomites. We develop a stochastic-mechanistic model of

exposure to rotavirus from nondietary ingestion iterated by simulated intermittent

fomes-mouth, hand-mouth, and hand-fomes contacts typical of a child under six years

of age. This exposure is subsequently translated to risk using a simple static dose-

response relationship. Through laboratory experiments, we quantified the mean rate

of inactivation for MS2 phage on glass (0.0052/s) and mean transfer between finger-

tips and glass (36%). Simulations using these parameters demonstrated that a childs

median ingested dose from a rotavirus-contaminated ball ranges from 2 to 1,000 virus

over a period of one hour, with a median value of 42 virus. These results were heavily

influenced by selected values of model parameters, most notably, the concentration

of rotavirus on fomes, frequency of fomes-mouth contacts, frequency of hand-mouth

contacts, and virus transferred from fomes to mouth. The model demonstrated that

mouthing of fomes is the primary exposure route, with hand mouthing contributions

accounting for less than one-fifth of the childs dose over the first 10 minutes of inter-

action.


3.2 Introduction

Viral agents transmitted primarily via the fecal-oral route, including enteric aden-

ovirus, astrovirus, norovirus, and rotavirus, are responsible for 35% of hospitalizations

for gastroenteritis, accounting for almost 5% of all child hospital visits in the United

States (Malek et al., 2006). Enteric viruses have been detected on indoor surfaces and

fomites in hospitals, day care centers, hotels, and houseboats (Keswick et al., 1983;

Green et al., 1998; Cheesbrough et al., 2000; Jones et al., 2007). Evidence of the

role of fomites in disease transmission includes the ability of the etiological agents to

transfer between hands and fomes (Ansari et al., 1988) and between fomes and mouth

(Rusin et al., 2002), and their ability to persist on fomes and hands (Hall et al., 1980;

Casewell and Desai, 1983; Ekanem et al., 1983; Butz et al., 1993; Abad et al., 1994;

Cheesbrough et al., 1997; Das et al., 2002; Clay et al., 2006). Despite evidence of

the importance of fomites in the spread of disease, few quantitative microbial risk

assessment models have examined their role in transmission of disease (Gibson et al.,

1999; Nicas and Sun, 2006).

The sporadic and sequential nature of multiple individual contacts between hands

and fomites, hands and mouth, and fomites and mouth has generally not been con-

sidered in microbial exposure assessments. Instead, human interaction with fomites

is modeled using estimates of the probability that a contact event occurs (e.g., 10%

chance a fomes is contacted by hand), a constant frequency of the contact event (e.g.,

mouth is contacted by hand 0.08 times per minute), or a constrained sequence of

events (e.g., fomes touches hand, hand then touches mouth) (Gibson et al., 1999;

Chen et al., 2001; Gibson et al., 2002; Haas et al., 2005; Nicas and Sun, 2006; Nicas

and Best, 2008). In the latter, frequently used in quantitative risk assessment as it

pertains to food handling, researchers assume that contacts are inevitable, only one

contact event of each type occurs, and the temporal sequence is static. In the present

study, we further the understanding of the role of human temporal sequence is static.

In the present study, we further the understanding of the role of human interaction

with fomites on exposure to infectious agents by incorporating modeled sequential,

intermittent contacts to encompass a wide array of activity levels. Previous work


in chemical risk modeling has suggested that using modeled sequential, intermittent

contact events reduces uncertainty in estimates of exposure to fomites (Ferguson,

2003).

Chemical risk models have been developed that account for an individuals sequen-

tial contacts with fomites using micro-level activity data. These data are obtained

typically through the use of videotapes by transcribing the timing and sequence of

an individuals interactions with environmental surfaces that can act as fomites (Fer-

guson et al., 2006). With these data in hand, along with concentrations of chemical

residues on objects and knowledge of the ability of chemicals to transfer from objects

to hands and mouth, a modeler is able to estimate chemical dose (Zartarian et al.,

1995; Ferguson, 2003; Ferguson et al., 2006).

The present study draws on this chemical risk model framework to conduct a

microbial risk assessment of a childs interaction with a rotavirus contaminated ball

in an indoor environment (e.g., a child care center). Using micro-level activity data

allows us to examine the influence of sequential contact events on a childs exposure

to rotavirus and subsequently estimate the risk of infection. After experimentally

determining inactivation rates of virus on a surface and the transfer efficiencies of virus

between a surface and human hands, we formulate a stochastic-mechanistic model of

risk from nondietary ingestion of rotavirus resulting from fomes-mouth, hand-mouth,

and hand-fomes contacts. The model is novel in that it uses variable and sequential

microlevel activity data to quantify exposure to a contaminated fomes. One of the

overarching goals of this study is to determine which model parameters need to be

further studied so that more precise exposure assessments can be performed.

3.3 Model Description

The stochastic-mechanistic model was developed with MATLAB (version 7.0; The

Mathworks, Inc., Natick, MA, USA). The model estimates an individual’s viral dose

over the specified time period by incorporating both direct contact between the mouth

and a contaminated fomes and indirect contact between the mouth and the fomes via


intermittent hand-fomes/hand-mouth contacts. Discrete, mechanistic equations iter-

ated by contact event relate an individual’s micro-level activity data (e.g., fomes-hand,

fomes-mouth, and hand-mouth contact frequency) to virus-specific exposure factors

(e.g., surface area contacted, hand-fomes viral transfer efficiency) and dose-response

parameters to estimate exposure, dose, and risk of adverse health outcomes. A Monte

Carlo sampling generates model parameter inputs from defined probability distribu-

tions, allowing the incorporation of parameter uncertainty. Parameter uncertainty is

defined here to include both uncertainty and variability. The model output includes

temporal concentration profiles for the fomes, left hand, and right hand and charac-

terizes an individual’s cumulative and iterative risk from continued interaction with

the fomes.

Equations used in the model define an infectious virus (hereafter referred to as

“virus”) as being in one of five states, as depicted in Figure 3.1: (1) located on the

fomes, (2) located on the right hand, (3) located on the left hand, (4) irreversibly

inactivated, and (5) absorbed in the facial membrane as dose. At the start of each

model simulation, the fomes is contaminated with a uniform surface concentration of

virus. Additionally, the individual’s hands and mouth are assumed free of virus. The

movement of virus between the states occurs either through inactivation (states 1 →4, 2 → 4, and 3 → 4) or through transfer of virus via contact (states 1 ↔ 2, 1 ↔ 3,

1 → 5, 2 → 5, 3 → 5).

Viral inactivation is assumed to decay exponentially with time, causing virus to

move from states 1, 2, and 3 to state 4:

Cx(tx) = Cx0e(−kxtx) (3.1)

where Cx(tx) with units virus/cm2 is the concentration of virus on surface x (e.g.,

fomes or hand) at time t, Cx0 is the initial concentration of virus on the surface

(virus/cm2), kx is the inactivation rate of the virus on the surface (s−1), and tx is the

elapsed time(s).

The transfer of virus between surfaces upon contact is modeled by assuming that


transfer is driven by a concentration gradient:

CX = CX0 − AXY TEXY (CX0 − CY 0) (3.2)

where CX is the concentration of virus on surface X after contact with surface

Y (virus/cm2), CX0 and CY 0 are the virus concentrations on surfaces X and Y ,

respectively, prior to contact (virus/cm2), TEXY is the percentage of virus transferred

from surface X to surface Y (%), and AXY is the ratio of surface area of the contact

event between surfaces X and Y to the total surface area of surface X (cm2/cm2).

The transfer is assumed to occur instantaneously and uniformly, and the duration

of contact is assumed to not affect transfer. The latter is based on the work of

Cohen Hubal et al. (2008), who found that duration does not increase the amount of

both lipophilic uvitex and nonlipophilic riboflavin tracer residues transferred between

surfaces on contact (Cohen Hubal et al., 2008). It is assumed that, after transfer,

virus is distributed evenly over the entire surface.

Dose (D) is the number of virus that transfer from a surface to the mouth and

depends on contact area between the surface and the mouth, as follows:

D = SxTExfCx (3.3)

where Sx is the contact area between object x and the mouth (cm2), TExf is

the percentage of virus transferred from the object to the mouth, and Cx is the

concentration of virus on object x (virus/cm2). The mouth is assumed to be an

absorbing state, as previously described (Nicas and Sun, 2006) so virus in contact

with the mouth is instantly absorbed into the body.

A dose-response curve for rotavirus is used to determine the likelihood of adverse

health outcome (Haas et al., 1999; Teunis et al., 1999). Because our model results

in multiple ingested doses from subsequent fomes-mouth and hand-mouth contacts,

we assume that the likelihood of an adverse health outcome is determined from the

additive effect of multiple subsequent exposures, as follows (Haas et al., 1999):

RTOT = f

I∑i=1

Di +J∑j=1

Dj +K∑k=1

Dk

(3.4)


f(D) = 1−(

1 +D

N50

(21α − 1)

)−α(3.5)

Here, RTOT is the likelihood of adverse health outcome(%), f is the dose-response

function (Equation 3.5), which is unique for rotavirus (Haas et al., 1999). I, J , and

K, are the total number of fomes-mouth, right hand-mouth, and left hand-mouth

contacts resulting in a dose event, respectively. Di, Dj, and Dk, are the doses (in

units of virus) resulting from the ith fomes-mouth, jth right hand-mouth, and kth

left hand-mouth contacts, respectively. 3.5 is a beta-Poison function with shape (α)

and scale (N50) parameters equal to 0.265 and 5.597 plaque-forming units (PFU),

respectively (Haas et al., 1999).

The model accounts for both direct and indirect transmission routes. Direct trans-

mission describes mouth-fomes contacts that transfer virus from the fomes to the

mouth (Equation 3.3). Indirect transmission describes hand transfer as intermedi-

ary between the fomes and the mouth, with hand-fomes contacts transferring virus

to the hand (Equation 3.2), and subsequent hand-mouth contacts resulting in dose

(Equation 3.3). Viral transfers are modeled as discrete contact events occurring at

intervals tFM , tRM , tLM , tRF , and tLF (s) describing subsequent fomes-mouth, right

hand-mouth, left hand-mouth, right hand-fomes, and left hand-fomes contacts, re-

spectively. Viral inactivation continuously occurs on surfaces and hands, albeit at

different rates (kf and kh, respectively).

3.4 Methods and Materials

3.4.1 Parameter Estimation

The model parameters used to estimate a child’s dose due to interaction with a con-

taminated fomes include the initial concentration of virus on surface (Ci), inactivation

rates of virus on surfaces (kf , kh), percentage of virus transferred between contacted

surfaces (TEom, TEoh, TEhm), length of time between contact events (tFM , tRM , tLM ,

tRF , tLF ), surface area of fomes and hands (Af , Ah), and surface area of contacts (Sf

, Sm, Sh). For each parameter, we provide estimates, with justification, of values in


the form of approximated distributions and associated parameters. The distributions

and range of associated parameter values are summarized in Table 3.1.

Initial Concentration of Virus on Surface

Though multiple studies have demonstrated the presence of rotavirus genomic RNA

on indoor environmental surfaces using assays that require at least 100 rotavirus per

sample to detect, no work has quantitatively determined the concentration of rotavirus

on surfaces (Keswick et al., 1983; Wilde et al., 1992; Butz et al., 1993; Soule et al.,

1999) To reflect the uncertainty in the initial concentration of virus on a fomes and

the potential variability of the severity of contamination events, we used a uniform

distribution with minimum and maximum parameters of 0.001 and 10 virus/cm2.

Inactivation Rates on Surfaces

Two inactivation rate parameters are required: rate of viral inactivation on dry en-

vironmental surfaces, kf , and rate of viral inactivation on hands, kh. Experimental

studies using MS2 phage as a surrogate for pathogenic virus were performed to es-

timate inactivation rates on environmental surfaces, kf . Glass slides (1 × 2.5 cm2)

were inoculated with 107 PFU MS2 phage suspended in tryptic soy broth (TSB).

Borosilicate glass was chosen to represent a nonporous material and was prepared by

washing in soap and water, wiping with 70% ethanol, rinsing in distilled water, and

air-drying. TSB was used as the suspension media to include potential effects of par-

ticle shielding, though previous studies have demonstrated no significant difference in

the persistence of virus on fomites due to suspension media (Abad et al., 1994). After

inoculation, the surface samples were kept in 6-well plates at 20◦C with 65% humidity

in the dark to provide a conservative estimate for viral inactivation on typical indoor

environments. The surfaces were swabbed with cotton-tipped applicators wetted in

500µl of phosphate buffered saline (PBS) at increasing intervals for a period of 50

days. The wetted cotton-tipped applicators were vortexed in microcentrifuge tubes

for 15 seconds in 500µl PBS, and 100µl of this was assayed using the double agar layer

method (USEPA, 2001). After swabbing, the surfaces were rinsed in 5 mL of PBS


for 15 minutes, and 100µl of the rinse was assayed to determine the concentration of

recoverable infective phage that were not removed by the cotton-tipped applicator.

The log of the total recovered (C) PFU over the log of initial count (C0) was plotted

as a function of time (t). Linear regression was used to determine the inactivation

rate (kf ) such that the concentration of viable, infective phage removable from the

surface of a fomes follows exponential decay (Equation 3.1).

We assume that the viral inactivation rate on hands, kh, follows an exponential

decay similar to previously reported inactivation rates for viruses on surfaces (Boone

and Gerba, 2007). However, kh has been shown to be greater than inactivation rates

on surfaces, possibly due to temperature and moisture or chemicals on the skin (Ansari

et al., 1988). Ansari et al. (1988) demonstrated a 93% reduction in rotavirus titer on

the surface of the skin over more than four hours, and this was used to estimate kh.

Percent Viral Transfer Between Surfaces

Three parameters describing viral transfer between surfaces are used: transfer be-

tween fomes and mouth (TEFM), fomes and hand (TEFH), and hand and mouth

(TEHM). To estimate percent transfer during fomes and hand contacts, MS2 phage

was used as a surrogate virus in laboratory studies. Borosilicate glass and fingertips

were used as proxy surfaces. Four fingertips from each of 10 volunteers were inocu-

lated with low (∼2×103 PFU) or high (∼2×104 PFU) titers of MS2 phage suspended

in TSB using a micropipettor. After the inoculation was allowed to dry, the fingertips

were placed against a glass surface for 10 seconds with an average constant pressure

of 25 kPa (range 16-38 kPa). The process was repeated with four glass surfaces in-

oculated to represent surface-to-hand transfer. For both directions of transfer, a fifth

surface (either finger or glass) was inoculated with PBS, representing a blank control.

Cotton-tipped swab applicators wetted in 500 µL of PBS were used to remove phage

from both the glass surface and the fingertip. The samples were stored at 4◦C and,

within 48 hours, were vortexed for 20 seconds and enumerated using the double agar

layer technique (USEPA, 2001). This resulted in a total of 80 samples, not including

blanks, in four categories: low titer hand-to-surface, high titer hand-to-surface, low

titer surface-to-hand, and high titer surface-to-hand. The transfer of phage between


surfaces was quantified as the count of recoverable infective phage from the recipient

surface as a percentage of the total recoverable infective phage from both the recipient

and the donor surfaces (Rusin et al., 2002).

Micro-Level Activity Data

Five parameters are used in the model to describe a child’s discrete contact events

during his/her interactions with a toy: the time intervals between subsequent fomes-

mouth, right hand-mouth, left hand-mouth, right hand-fomes, and left hand-fomes

contacts (tFM , tRM , tLM , tRF , and tLF , respectively). A recent study determined that

the Weibull distributions best describe the frequency of contact event data (Xue et al.,

2007). As such, the time intervals in this study are described using the Weibull dis-

tributions (Law and Kelton, 1997). To determine the durations between subsequent

hand-toy (tRF and tLF ) and mouth-hand contacts (tLM and tRM), we use data of

children’s interactions with their environment, as previously collected and described

(AuYeung et al., 2006; Ferguson et al., 2006). The data were collected by videotaping

one- to six-year-old children in both indoor and outdoor environments for 2-hour time

periods. The videotapes were translated, using VideoTraq c© software (SamaSama

Consulting, Sunnyvale, CA, USA), into second-by-second accounts of the contact

events between a child’s right hand, left hand, and mouth and into 36 object cate-

gories for 20 children. The data set was subjected to quality control, as previously

described (Ferguson et al., 2006). The resulting micro-level activity data provide de-

tailed descriptions of a child’s object contacts necessary for modeling the complexities

of intermittent contaminant loading and removal (Beamer, 2007). We assumed that

the time intervals between a child’s hand contacts with the object “Hard Toy” (de-

fined as any hard, nonporous toy) could serve as a valid proxy for repetitive contacts

with a toy ball. In total, 1,340 right-hand and 1,433 left-hand contact events of 15 of

the 20 children were used to develop the Weibull distributions for tRF and tLF . The

time intervals for the remaining five children were used to cross-validate the Weibull

distributions using the Kolmogorov-Smirnoff test for goodness of fit.

The same method was used to determine the time intervals between hand-mouth

(tRM and tLM) and fomes-mouth (tFM) contacts. The number of data points used


to create the Weibull distribution were 129, 127, and 43 for right hand-mouth, left

hand-mouth, and fomes-mouth contacts, respectively.

3.4.2 Model Approach

The model is a discrete-time model, iterated by contact event. First, a sequence

of events describing fomes-mouth, right hand-mouth, left hand-mouth, right hand-

fomes, and left hand-fomes contacts is simulated using a Monte Carlo sampling from

the interval distributions described by the respective parameters. An example of a

simulated sequence is shown as a time series in Figure 3.2, with solid vertical lines

representing contacts between the specified hand and the fomes, unfilled circles repre-

senting contacts between the hand and mouth, and filled circles representing contacts

between the fomes and mouth. Once a sequence of contacts is generated, the initial

and final concentrations of virus on each surface (left hand, right hand, and fomes)

are determined for each contact event using sampled virus-specific exposure factors,

dose-response parameters, and the specific equations in Appendix A, formulated from

Equations 3.1, 3.2, and 3.3. From this information, temporal exposure, dose, and risk

profiles are generated and metrics of interest are recorded. Examples of exposure and

dose profiles as a function of time are presented in Figure 3.3. The illustration of

the left hand in Figure 3.3 was omitted for simplicity. Vertical dashed lines represent

the timing of hand-fomes contacts, and vertical solid lines represent the timing of

hand-mouth contacts.

3.4.3 Sensitivity Analysis

The sensitivity analysis method used in this study was previously described by Xue

et al. (2006) Briefly, the model is run using single-point parameter values to investigate

the sensitivity of the model to variations in a given parameter. The model is run

twice, with the value of one specified parameter set first to the 25th percentile (p25)

and then to the 75th percentile (p75) of its probability distribution while all other

parameters remain set to their median values, and the model output is calculated. The

median, p25, and p75 values are used as normative values to describe distributions


because the model relies on multiple, different probability distribution functions. The

effects of parameter variation on output are investigated by calculating a ratio of the

cumulative dose resulting from the use of the p75 to the cumulative dose resulting

from the p25. The ratio of the results (p75:p25) quantifies the sensitivity of the model

to the parameter over the middle 50% of the probability distribution.

If p75:p25 is equal to 1, then the model output is unaffected by variations of

the parameter. A ratio greater than 1 demonstrates that increases in parameter

value, either from the p25 to the median or from the median to the p75, increase

the cumulative dose by a factor equal to the ratio. A ratio less than 1 demonstrates

a decrease in the cumulative dose by a factor equal to the inverse of the ratio. To

track the changes in model sensitivity to a given parameter as the length of time of

child-fomes interaction increases, we investigate the temporal change in the p75:p25

ratio for each parameter. The parameters are ranked by the order of influence on the

cumulative dose by comparing the absolute values of the log of the p75:p25 ratio.

3.5 Results

3.5.1 Parameter Estimation

Inactivation

The inactivation rate, kf , with 95% confidence interval was determined to be 0.0052

± 0.0014/h for the representative nonporous surfaces at ambient conditions (20◦C

and 55-65% relative humidity). This value is within an order of magnitude of previ-

ously reported viral inactivation rates for rotavirus p13, astrovirus (serotype 4), and

hepatitis A (Boone and Gerba, 2007).

From Ansari et al. (1988), the inactivation rate of rotavirus on hands, kh, with

95% bootstrapped confidence interval was estimated to be 0.27 ± 0.03/h. This value

is within an order of magnitude of other studies investigating microbial inactivation

on the skin for other organisms (Musa et al., 1990; Traore et al., 2002) and is used in

this study as an estimate for viral inactivation on the skin.


Transfer Efficiency

The transfer from glass to hand was determined to be 36%, with a standard deviation

(SD) of 26%, and the transfer from hand to glass was determined to be 27%, with

a SD of 23%. The Kruskal-Wallis test showed no statistical difference (p > 0.05) in

these populations, demonstrating insufficient evidence that the direction of transfer

influences percent transferred. Thus, our model assumes that the percentage of viral

transfer is direction-independent (Nicas and Sun, 2006). Additionally, there is insuffi-

cient evidence that virus transferred between surfaces is dependent on the initial viral

titer, as demonstrated by a Kruskal-Wallis test for significance (p > 0.05). Pooling

the data, TEFH used in this model is represented by a normal distribution, with a

mean of 32% and a SD of 25%. Although the mean is similar to reported values for

viral transfer (Ansari et al., 1988; Mbithi et al., 1992; Rusin et al., 2002), the spread

of this distribution is greater than previously reported for viral transfer, but is similar

to the values for lipophilic and nonlipophilic compounds (Cohen Hubal et al., 2008).

The transfer of virus between hand and mouth (TEHM) was estimated using a

study by Rusin et al. (2002) examining PRD-1 phage transfer from fingertips to

lips (Rusin et al., 2002). Laboratory experiments investigating the transfer from 20

volunteers resulted in a mean transfer of 41% of recoverable phage onto the lips,

with no SD reported. We assume that the distribution spread (SD) for each percent

transfer parameter is similar to our experimentally determined distribution for TEFH ,

and therefore we assume a SD of 25% for TEHM as well. To our knowledge, no work

has yet investigated the amount of virus that transfers directly between a fomes and

mouth (TEFM). We assume that this transfer (TEFM) has a similar distribution as

the transfer of virus between hand and mouth: a normal distribution with a mean of

41% and a SD of 25%.

Micro-Level Activity Data

Using data from the videotapes, the time between subsequent right and left hand-

fomes contact events, tRF and tLF , is modeled as aWeibull distribution with scale and

shape parameters 33 seconds and 0.62 for the right hand and 32 seconds and 0.63 for


the left hand, respectively. The Kolmogorov-Smirnoff goodness-of-fit test indicates

that the Weibull distribution is sufficient for describing 17 of 20 childrens right hand-

fomes contacts and 16 of 20 childrens left hand-fomes contacts (p > 0.001). The

times between subsequent right and left hand-mouth contact events, tRM and tLM ,

are modeled as a Weibull distribution with scale and shape parameters of 270 s and

0.47 for the right hand (p > 0.05 for 20 of 20 children) and 420 s and 0.61 for the

left hand (p > 0.05 for 20 of 20 children), respectively. The time between subsequent

fomes-mouth contact events, tFM , is modeled as a Weibull distribution with scale and

shape parameters 140 s and 0.41 (p > 0.05 for 20 of 20 children), respectively.

Surface Area

Parameters describing the total surface area of the hand (AH) and the fomes (AF )

are required. Additionally, surface areas of contact between the fomes and mouth

(SF ), the fomes and the hand (SH), and the hand and mouth (SM) are needed. We

estimated total surface area of a childs hand (AH) as uniformly distributed with a

range of 270–390 cm2, based on calculations using data available from the Child-

Specific Exposure Factors Handbook (interim report) (Tulve et al., 2002; USEPA,

2006). The toy ball is given a diameter of 9–11 cm, resulting in a uniform distribution

for surface area of the fomes (AF ), with a range of 250–380 cm2. The literature values

were used for both surface area of contact between a fomes and a hand (SH) and

surface area of contact between a hand and mouth (SM) for children playing with

toys outdoors. The surface area between a fomes and a hand on contact were observed

to fall within the range of 8–27% of total hand surface area (AuYeung, 2007). For this

case study, a uniform distribution using the 5th and 95th percentiles (13–24%) of that

range as endpoints is used, and we assume that surface area of childs contact with

outdoor toys is a sufficient proxy for surface area of childs contact with indoor toys

(AuYeung, 2007). Similarly, the 5th and 95th percentiles of SM were estimated as a

range of 6–33% of total hand surface area (AuYeung, 2007). We assume a uniform

distribution using these values as endpoints,and that hand-mouth contacts outdoors

act as a sufficient proxy for similar childs contacts indoors.

To our knowledge, no data are available on the percentage of surface area of a


round ball contacted during fomes-mouth contacts. Therefore, we make a conservative

estimate that the surface area is similar to the surface area of childs contacts between

hand and mouth (6–33%). This estimate is conservative because, though we assumed

similar surface areas for the round ball fomes and the hand, less surface area will

likely be contacted during a mouthing event for a large round ball than for small,

irregularly shaped fingers and hands.

3.5.2 Model Results

Temporal Exposure, Dose, and Risk Estimates

Prototypical profiles of right- and left-hand exposures and virus concentration on

the fomes are provided in Figure 3.4. Virus concentration decreases on the fomes

as inactivation and continued hand-fomes and mouth-fomes contacts remove virus.

Conversely, virus concentration initially increases on the hands, as the presumed

original state of the hands is virus free. As described by Equation 3.2, the difference

in the concentration of virus between the fomes and hands drives the transfer of virus

between the two surfaces, forcing an eventual pseudoequilibrium between the surfaces.

Once this is reached, hand- and fomes-mouth contacts, combined with inactivation,

remove the virus from the fomes and hands, causing the concentrations to decrease

gradually.

Figure 3.5 displays the estimated cumulative dose and corresponding risk as a

function of time. The median cumulative dose increases approximately linearly with

the length of time the child interacts with the contaminated fomes, ranging from a

median dose of 13 virus (corresponding risk of infection (RI) of 60%) during 10 minutes

of interaction to 42 virus (RI 70%) during one hour of interaction. As rotavirus

has a low median infectious dose, or dose at which half of individuals exposed will

experience adverse health effects, of 5.6 PFU (Haas et al., 1999), the majority of risk

occurs within the first 10 minutes. This would not be true for other pathogenic agents

transmitted via the fecal-oral route such as Shigella and enteropathogenic Escherichia

coli, which have higher median infectious doses of 103 and 107, respectively (Haas

et al., 1999), The 5th and 95th percentiles of dose, using the specified probability


distributions for each parameter, are 0 (RI = 0%) and 430 (RI = 84%) rotavirus with

10 minutes of child-toy interaction to 2 (RI = 35%) and 1,000 (RI = 87%) rotavirus

with one hour.

The virus concentration on the surface of the fomes is reflective of the degree of

severity of the contamination event, so we explored the dose and risk of illness as

a function of initial concentration on the fomes (Figure 3.6). As demonstrated in

the results of 1,000 model simulations of 10 minutes of child-fomes interaction with

best-fit functions (Figures 3.6a and 3.6B), there is large variability in the resulting

dose and risk of illness for a given initial virus concentration.

Despite this variability, the median dose linearly increases with the initial concen-

tration on the fomes (Figures 3.6a and 3.6c). The dose and corresponding risk as a

function of initial concentration on the fomes for simulations between 10 and 60 min-

utes of child’s interaction with the fomes fits the beta-Poisson function (Figures 3.6b

and 3.6d), with a shape parameter (α) similar to that used for the beta-Poisson dose-

response model (Equation 3.5). The beta-Poisson scale parameter (equivalent to N50)

describes the concentration on the fomes for which there is a 50% risk of illness (Haas

et al., 1999). The scale parameter decreases with increasing child-fomes interaction

time (from 0.3 PFU/cm2 at 10 minutes to 0.04 PFU/cm2 at 60 minutes), suggesting

the risk of illness from a given virus contamination increases the longer a child plays

with the toy, consistent with the previous observation given in Figure 3.5.

The relative importance of direct (fomes → mouth) and indirect (fomes → hand

→ mouth) transmission of viral pathogens was explored (Figure 3.7). As a child

initially interacts with the fomes, the fomes-mouth contacts contribute more than

80% of dose, demonstrating that a child’s direct mouthing of a toy is the most likely

route of viral transmission. As a child continues playing with a toy, the virus on

the fomes is transferred to the hands, and indirect transmission via mouthing hands

contributes more to the child’s total dose. Therefore, the proportion of total dose

from mouthing a toy decreases, and the proportion from mouthing hands increases.

Because the majority of the ingested dose occurs within the first 10 minutes, fomes-

mouth contacts contribute to the majority of a child’s risk of adverse health effects.

This may not be true for other pathogenic agents with higher median infectious doses


than rotavirus.

3.5.3 Sensitivity Analysis

Model-estimated dose relies on the stated assumptions concerning input parameter

values and distributions. Thus, we emphasize the relative importance of input pa-

rameters over absolute model output values (Zartarian et al., 2000) by examining the

parameter’s influence on model output through a sensitivity analysis.

The sensitivity analysis (Table 3.2) demonstrated that the parameters that most

influence a cumulative dose after 10 minutes of interaction are, in order: (1) initial

virus concentration on the surface of the fomes (Ci), (2) frequency of fomes-mouth

contacts (tFM), (3) frequency of right hand-mouth contacts (tRM), (4) transfer of

virus between fomes and mouth (TEFM), (5) frequency of left hand-mouth contacts

(tLM), (6) percentage of the fomes that contacts the mouth on fomes-mouth contacts

(SF ), (7) surface area of fomes (AF ), and (8) percent transfer of virus between hand

and mouth (TEHM). The commonly used Spearman correlation sensitivity analysis

(Gibbons, 1985; Siegel, 1988) supports these findings (data are not shown).

The sensitivity analysis method allows investigation of the changing influence of

each parameter over time (Table 3.2). As the child continues to interact with the

toy, the ratio of the dose resulting from the p75 to the p25 (p75:p25) value of a

parameter changes to reflect the parameters changing influence on resulting dose.

For example, the importance of fomes-mouth viral transfer decreases as child-fomes

interaction increases (Figure 3.8A). This further supports the finding that direct

contact between the fomes and mouth is the primary exposure route within the first

10 minutes, but that the indirect contacts between the hand and fomes and the hand

and mouth become increasingly important as the child continues interacting with the

toy. Similarly, the influence of the frequency of fomes-mouth contacts on resulting

dose decreases temporally (Figure 3.8B). The model also demonstrates that hand-

fomes viral transfer, examined over the duration of the child-fomes interaction from

10 to 60 minutes, has little influence over resulting dose. When the percentage of

virus transferred on hand-fomes contacts is varied between 18% and 54%, the range


between the p25 and the p75, the change in dose is low. Presumably, this is because

the expected value of handfomes contact frequency (0.05/s) is an order of magnitude

larger than the median frequency of hand-mouth contacts (0.006/s). That is, there

are almost 10 hand-fomes contacts made for each hand-mouth contact. Because

multiple hand-fomes contacts occur, the virus concentration on the hands and fomes

reach equilibrium, regardless of the percentage of virus transferred on each individual

contact.

3.6 Implications

We use micro-level activity patterns in a mechanistic-stochastic model of dose to

more fully understand the role of fomites in pathogen transmission. We simulated

a single individuals interactions with a contaminated fomes, demonstrating the abil-

ity to model pathogen transmission on a contact-by-contact basis. Previous work

incorporating human-environment interactions has demonstrated the importance of

sequential contacts in understanding microbial exposure and risk from specific activ-

ities (Gibson et al., 1999; Chen et al., 2001; Gibson et al., 2002; Haas et al., 2005;

Nicas and Sun, 2006; Atkinson and Wein, 2008). We further this work by demon-

strating the importance of modeling a wider range of activity level by incorporating

stochastic simulations of activity into microbial exposure assessment. Together with

previous studies investigating bacterial and viral transmission via contacts, this study

provides a foundation for incorporating human-environment interaction in dynamic

infectious disease models used to describe population-based pathogen transmission

for fecal-oral diseases (Elveback et al., 1971; Nasell, 2002; Stone et al., 2007).

An overarching goal of our work is to identify model parameters that require fur-

ther study to improve future risk assessments. The model parameter most strongly

linked to estimated dose is, unsurprisingly, the concentration of virus on the fomes.Our

use of a uniform distribution for the initial concentration bounded by parameters

differing by over four orders of magnitude was motivated by not only a lack of quan-

titative data describing distributions of viral contamination on indoor surfaces but

also a desire to examine model output over the full range of plausible values. As


Laborde et al. (1994) found, the concentrations of indicator bacteria on surfaces in

child care centers regularly differed by over four orders of magnitude, emphasizing the

need to understand the exposure and risk resulting from interactions with surfaces

over a range of contamination levels (Laborde et al., 1994). Clearly, as the initial

concentration increases, one would expect the dose and risk of illness to increase as

well (Figure 3.6). Nevertheless, there is a need for further evaluation of the quantity

and spatial distributions of viral contamination on indoor surfaces, particularly for

surfaces such as toys that are most likely to act as fomites. This finding also reflects

the importance of reducing the presence of virus on surfaces to reduce or eliminate

fomes-mediated disease transmission.

The average time between the fomes and mouth contacts was a significant con-

tributor to model output and should be investigated further. The importance of this

parameter decreased temporally as the child continued interacting with the toy. This

is explained by the increased contribution of right hand-mouth and left hand-mouth

contacts to dose (Figure 3.7). Implementing an intervention to reduce fomes-mouth

contacts would result in a reduction in an individuals dose. A decrease of 50% of the

rate of fomes-mouth contacts, with all other parameters unchanged in the stochastic

model, reduced the median dose by 31%, with a corresponding reduction of risk for

rotavirus of 4%, after 10 minutes of child-fomes interaction. However, modifications

in a childs behavior may be difficult or impossible to implement.

The amount of viral transfer between surfaces may be an example of a parameter

that could be readily modified, as different toy surface properties or environmen-

tal conditions may influence the amount of virus transferred between surfaces. As

demonstrated, the transfer of virus between fomes and mouth is the fourth most in-

fluential parameter in determining dose and risk. Reduction in the transfer between

fomes and mouth by 50%, without any changes in the values or distributions of the

other variables in the stochastic model, reduces the median dose of the simulations

by 22%, with a corresponding reduction of risk of 2%, after 10 minutes of interaction.

This highlights the importance of better understanding the effect of environmental

conditions on viral transfer between surfaces to reduce the incidence of disease, as

environmental conditions are easily modified via the use of humidifiers/dehumidifiers


and thermostats. Additionally, the use of toys with surface properties that inhibit

or reduce viral transfer during periods when viral outbreaks are most likely to occur

may reduce or slow the transmission of disease (Nicas and Sun, 2006).

Finally, the model demonstrates that viral inactivation plays little or no role in

determining an individuals dose over the time scale of our model. Reduction of viral

inactivation on both fomes and hands by 100% (i.e., assuming no viral inactivation)

resulted in a change in the median dose of one viral particle, with corresponding

difference in risk of only 0.5% after 10 minutes of interaction. After 60 minutes of

interaction, assuming no viral inactivation, the median dose changed by only four

viral particles, with corresponding difference in risk of 0.5%. This result applies

only to ambient environmental conditions and the time scale of this model, which

focuses on child’s interaction with the fomes at a temperature of 20◦C and relative

humidity of 65% over a period of at most 60 minutes. Changes in environmental

factors significantly alter inactivation rates of virus on surfaces, with rapid increases in

inactivation rates at a higher relative humidity and temperature (Ansari et al., 1991).

Viral persistence on surfaces likely influences transmissibility via fomites on a time

scale more closely aligned with the time scale of inactivation rates, for example, days

or weeks (Boone and Gerba, 2007), and at more extreme environmental conditions,

for example, relative humidity >85% and temperature >30◦C (Ansari et al., 1991).

The results of the model rely on assumptions and simplifications. For example,

we assumed uniform distribution of viral agents on both fomes and hands, before

and after contacts. This assumption, common in chemical exposure modeling and in

investigation of aerosol dispersion of pathogenic agents (Nazaroff et al., 1998; Zartar-

ian et al., 2000; Liao et al., 2008), clearly impacts the resulting simulated risk of

adverse health effects. Future work should examine uneven pathogen distribution

on surfaces. Additionally, the model does not explore the effects of environmental

conditions, such as temperature and humidity, on the transmission and inactivation

of virus. As temperature and humidity have recently been implicated in the seasonal

patterns of influenza transmission (Lowen et al., 2007), future work could focus on

discerning the effect of temperature and humidity on inactivation and transfer to

elucidate their role in the transmission of fecal-orally transmitted viruses.


Examination of an individual’s interactions with his/her environment to assess ex-

posure to infectious disease lays the groundwork for incorporating activity and virus-

specific exposure factors into broader, secondary transmission models. Although the

model incorporates individual’s contact events and virus-specific exposure factors such

as transfer of virus between surfaces and viral inactivation rates in disease transmis-

sion, the model is limited to assessing a static examination of a single individual’s

risk. The dose-response model used to determine an individual’s risk was based on

data obtained from healthy adults; we did not account for the uncertainty associ-

ated in applying this model to children to determine the risk of illness (Ward et al.,

1986). Similarly, the model does not account for immunity or interaction between

multiple children, which would enable the dynamic modeling of secondary transmis-

sion via person to person or person to fomes to person (Abbey, 1952; Elveback et al.,

1971; Nasell, 2002; Lawniczak et al., 2006; Giraldo and Palacio, 2008). Despite the

limitations of the model, we elucidate the roles of an individual’s contact events, vi-

ral inactivation, and viral transfer on an individual’s risk of adverse health effects.

This mechanistic-stochastic model of microbial dose incorporates contact-by-contact

human-environment interactions and can therefore serve as a basis for future high-

resolution microbial risk assessment.

3.7 Acknowledgments

The authors acknowledge the volunteers, members of the Boehm and Leckie research

groups, and anonymous reviewers who assisted with the work and/or provided sug-

gestions for improving the article. This publication was supported by the Stanford

University Shah Research Fellowship and the STAR Research Assistance Agreement

No. F07D30757 awarded by the U.S. Environmental Protection Agency (EPA). It

has not been formally reviewed by the EPA. The views expressed in this article are

solely those of the authors, and the EPA does not endorse any products or commercial

services mentioned in this article.


3.8 Tables


Var

iab

leD

escr

ipti

onS

ym

bol

Un

its

Dis

trib

uti

on

(Para

met

ers)

Sou

rce/

Ref

eren

ce

VirusConcentration

Init

ial

Con

centr

atio

nC

ivir

us/

cm2

Un

iform

(0.0

01,1

0)

Ass

um

pti

on

InactivationRate

Fom

eskf

1/s

Norm

al

(1.4×

10−6,

2.0×

10−

7)

Th

isS

tud

yH

and

kh

1/s

Norm

al

(7.5×

10−5,

4.3×

10−

6)

An

sari

etal.

(1988)

AreaofObject

Fom

esA

Fcm

2U

nif

orm

(250,3

80)

Ass

um

pti

on

Han

dA

Hcm

2U

nif

orm

(270,3

90)

US

EP

A(2

006)

Contact

Frequen

cyF

omes

and

Mou

tht F

Ms

Wei

bu

ll(1

40,

0.4

1)

Th

isS

tud

yR

ight

Han

dan

dF

omes

t RF

sW

eib

ull

(33,

0.6

2)

Th

isS

tud

yL

eft

Han

dan

dF

omes

t LF

sW

eib

ull

(32,

0.6

3)

Th

isS

tud

yR

ight

Han

dan

dM

outh

t RM

sW

eib

ull

(420,

0.5

9)

Th

isS

tud

yL

eft

Han

dan

dM

outh

t LM

sW

eib

ull

(270,

0.4

7)

Th

isS

tud

y

PercentofObjectContacted

Fom

es(F

omes

-Mou

thC

onta

ct)

SF

%U

nif

orm

(0.0

6,0

.33)

Ass

um

pti

on

Han

d(F

omes

-Han

dC

onta

ct)

SH

%U

nif

orm

(0.1

3,0

.24)

Au

Yeu

ng

(2007)

Han

d(H

and

-Mou

thC

onta

ct)

SM

%U

nif

orm

(0.0

6,0

.33)

Au

Yeu

ng

(2007)

PercentTransferred

Bet

wee

nF

omes

and

Mou

thTE

FM

%N

orm

al

(0.4

1,

0.2

5)

Ass

um

pti

on

Bet

wee

nF

omes

and

Han

dTE

FH

%N

orm

al

(0.3

6,

0.2

6)

Th

isS

tud

yB

etw

een

Han

dan

dM

outh

TE

HM

%N

orm

al

(0.4

1,

0.2

5)

Ru

sinet

al.

(2002),

Ass

um

pti

on

Tab

le3.

1:P

aram

eter

sU

sed

inM

odel

,w

ith

Cor

resp

ondin

gD

istr

ibuti

ons

and

Med

ian

Val

ues

for

Det

erm

inin

gE

xp

osure

and

Dos

eD

istr

ibuti

ons


Inp

ut

Valu

esp

75:p

25

Ran

k

Vari

ab

leD

escr

ipti

on

p25

p50

p75

10

min

20

min

30

min

40

min

50

min

60

min

Virusconcentration

Init

ial

con

centr

ati

on

0.0

01

0.1

10

10000

10000

10000

10000

10000

10000

1

Inactivationrate

Fom

es1.2

7×

10−6

1.4

0×

10−6

1.5

3×

10−6

1.0

1.0

1.0

1.0

1.0

1.0

Han

d7.2

1×

10−5

7.5

0×

10−5

7.7

9×

10−5

1.0

1.0

1.0

1.0

1.0

1.0

Areaofobject

Fom

es283

315

348

1.2

1.3

1.3

1.3

1.3

1.3

7H

an

d300

330

360

1.0

1.0

1.0

1.0

1.0

1.0

Contact

frequen

cyF

om

esan

dm

ou

th6.5

0×

10−4

1.6

0×

10−3

3.2

0×

10−3

3.0

2.3

2.1

2.0

1.9

1.8

2R

ight

han

dan

dfo

mes

5.1

0×

10−3

1.2

5×

10−2

2.5

0×

10−2

1.0

1.0

1.0

1.0

1.0

1.0

Lef

th

an

dan

dfo

mes

5.5

0×

10−3

1.3

2×

10−2

2.6

0×

10−2

1.0

1.0

1.0

1.0

1.0

1.0

Rig

ht

han

dan

dm

ou

th4.8

0×

10−4

1.2

0×

10−3

2.4

0×

10−3

1.2

1.4

1.4

1.4

1.4

1.4

5L

eft

han

dan

dm

ou

th4.6

0×

10−4

1.1

0×

10−3

2.2

0×

10−3

1.2

1.3

1.3

1.4

1.4

1.4

6

Percentofobjectcontacted

Fom

es(f

om

es-m

ou

thco

nta

ct)

0.1

30.2

00.2

61.6

1.5

1.4

1.4

1.3

1.3

4H

an

d(f

om

es-h

an

dco

nta

ct)

0.1

60.1

90.2

11.0

1.0

1.0

1.0

1.0

1.0

Han

d(h

an

d-m

ou

thco

nta

ct)

0.1

30.2

00.2

61.2

1.3

1.3

1.3

1.4

1.4

Percenttransferred

Bet

wee

nfo

mes

an

dm

ou

th0.2

40.4

10.5

81.9

1.6

1.5

1.5

1.4

1.4

3B

etw

een

fom

esan

dh

an

d0.1

80.3

60.5

40.9

0.9

0.9

1.0

1.0

1.0

Bet

wee

nh

an

dan

dm

ou

th0.2

40.4

10.5

81.3

1.3

1.4

1.4

1.5

1.4

8

Tab

le3.

2:Sen

siti

vit

yA

nal

ysi

sR

esult

sfo

rC

um

ula

tive

Dos

e(N

um

ber

ofV

irus)

Duri

ng

Incr

easi

ng

Len

gth

ofT

ime

ofC

hild-F

omes

Inte

ract

ion


3.9 Figures


1: Fomes

3: Left Hand

2: RightHand

4: Inactivated

5: Facial Membrane/Dose

- Transfer by Contact - Viral InactivationLegend

Figure 1

Figure 3.1: The relationships between the five potential reservoirs for virus repre-sented by this model. At time 0, the fomes is the only contaminated object, and theright and left hands are free of virus. The arrows represent the possible pathwaysbetween the states.


0 100 200 300 400 500 600 700 800 900 1000Time (s)

FomesLeft Hand

Right Hand

- Hand-Mouth Contact - Hand-Fomes ContactLegend - Fomes-Mouth Contact

Figure 2

Figure 3.2: Example of timing for randomly generated sequence of contact eventsbetween left hand, right hand, fomes, and mouth. Vertical solid lines represent hand-fomes contacts, unfilled circles represent hand-mouth contacts, and filled circles rep-resent fomes-mouth contacts.


0 0

20 40 60 80

100

50

100

150

200

0

20 40 60 80

100

50

100

150

Fom

es C

on

c. (C

f )R

igh

t H

and

Co

nc.

(Ch

)D

ose

(D)

- Hand-Fomes Contact

Time

Legend

- Dose- Hand-Mouth Contact

Figure 3

Figure 3.3: Example of trends of concentration, exposure, and dose over time, sim-ulated from randomly generated sequence of contact events, describing interactionbetween right hand, fomes, and mouth. Vertical dashed lines represent right hand-fomes contacts, and vertical solid lines represent right hand-mouth contacts. Eachhand-fomes contact results in virus transfer between fomes and hand. Each hand-mouth contact results in virus transfer from hand to mouth. Different inactivationrates (kh and kf ) continuously decrease viral concentration on hand and fomes.


0 10 20 30 40 50 600

2

4

6

8

10

2

Figure 4

Fomes

Right Hand

Left Hand

Length of Time of Child-Fomes Interaction (min)

Vir

us

Co

nce

ntr

atio

n (v

iru

s/cm

)

0 10 20 30 40 50 60

10

8

6

4

2

0

Figure 3.4: Example of modeled concentration and exposure profiles for fomes, righthand, and left hand, demonstrating the temporal change in concentrations.


Figure 5

Cu

mu

lati

ve In

ges

ted

Do

se (v

iru

s)R

isk

of I

llnes

s (%

)

Time of Child-Fomes Interaction (min) 10 20 30 40 50 60

10 20 30 40 50 60

100

80

60

40

20

0

1200

1000

800

600

400

200

0

Figure 3.5: Modeled distributions of (top panel) dose and (bottom panel) risk ofinfection from 10,000 simulations of child-fomes interaction after specified interac-tion time. Boxes depict the median, 25th percentile, and 75th percentile. Whiskersrepresent the 5th and 95th percentiles.


-3.0 -2.5 -2.0 -1.5 -1.0 -0.5 0.0 0.5 1.00.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

Initial Virus Concentration on Fomes (Log(virus/cm ))

Ris

k o

f Illn

ess

2

-3.0 -2.5 -2.0 -1.5 -1.0 -0.5 0.0 0.5 1.00

200

400

600

800

1000

1200

Ing

este

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us)

50 min40 min

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-3.0 -2.5 -2.0 -1.5 -1.0 -0.5 0.0 0.5 1.00

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Figure 6

Figure 3.6: Dose and risk of illness as a function of initial concentration of virus onfomes. Best-fit lines for (a) and (c) are calculated using the linear relationship ofresulting dose from the initial concentration on fomes from 10,000 model simulations.Best-fit lines for (b) and (d) are calculated using the beta-Poisson distribution result-ing from the risk of illness as a function of initial concentration on fomes from 10,000model simulations. (a) Dose as a function of initial concentration resulting from 1,000model simulations plotted with the best-fit line for 10 minutes of child-fomes inter-action demonstrates the variability of modeling results and distribution around thebest-fit line. (b) Risk of illness as a function of initial concentration resulting from1,000 model simulations plotted with the best-fit line for 10 minutes of child-fomesinteraction. (c) Dose increases as a function of child-fomes interaction as well as ini-tial concentration on fomes, as shown in 10-minute increments for 10–60 minutes ofchild-fomes interaction. (d) Risk of illness exhibits increases similar to those of doseas a function of initial concentration on fomes, as shown in 10-minute increments for10–60 minutes of child-fomes interaction.


0

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Left Hand - Mouth Contacts

Right Hand - Mouth Contacts

Fomes - Mouth Contacts

56%

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Figure 7

Figure 3.7: Dose contributions over time from left hand-, right hand-, and fomes-mouth contacts increase as child-fomes interaction increases, as demonstrated by theline graph. The pie charts demonstrate the percentage of dose contribution fromleft hand-, right hand-, and fomes-mouth contacts at the specified time, with thecontribution from fomes-mouth contacts decreasing as a percentage of the whole aschild-fomes interaction increases.


0

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Figure 8

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Length of Time of Child-Fomes Interaction (min)

Rat

io o

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lati

ve D

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)

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0

Figure 3.8: Sensitivity analysis examining the effect of parameter variation on modeloutput as it changes over time. The ratio of the cumulative dose calculated usingthe 75th percentile value of the probability distribution to the cumulative dose cal-culated using the 25th percentile value of the specified parameter. The ratio of thecumulative dose refers to the factor by which it changes when the parameter valueis increased for: (a) viral transfer between surfaces, where TEFomes−Mouth is the per-centage of virus transferred from fomes to mouth per contact, TEHand−Mouth is thepercentage of virus transferred from hand to mouth per contact, and TEHand−Fomesis the percentage of virus transferred between hand and fomes per contact, and (b)the frequency of contact events, where tFomes−Mouth is the frequency of fomes-mouthcontacts, tHand−Mouth is the frequency of right hand-mouth contacts, and tFomes−Handis the frequency of fomes-right-hand contacts.

Chapter 4

Surface Sampling Methods for

Virus Recovery From Fomites

The results presented in this chapter will be submitted to a peer reviewed journal in

Winter 2011. Francisco J. Tamayo contributed to the experimental design and data

collection, and will be co–author on the resulting publication. James O. Leckie and

Alexandria B. Boehm will also appear as co–authors, for their contributions to study

design, data interpretation, and manuscript improvements.

72

CHAPTER 4. VIRUS RECOVERY FROM SURFACES 73

4.1 Abstract

The role of fomites in infectious disease transmission relative to other exposure routes

is difficult to discern due, in part, to the lack of information on the level and distribu-

tion of virus contamination on surfaces. Comparison of outcomes of studies intending

to fill this gap is difficult because multiple different sampling methods are employed

and authors rarely report their method’s lower limit of detection. In the present

study, we demonstrate that the sampling method significantly influences virus recov-

ery from surfaces, and therefore influences study outcomes. We compare sampling

methods chosen from a literature review to identify the most efficient method for re-

covering virus from surfaces in a laboratory trial using MS2 bacteriophage as a model

virus. Recovery of virus is determined using both plaque assay and quantitative poly-

merase chain reaction. From this, we conclude that polyester-tipped swabs prewetted

in either 1/4–strength Ringer’s solution or saline solution most effectively remove

virus from nonporous fomites. Our results also demonstrate that the recommended

sampling method is an appropriate method for quantifying virus on surfaces.


4.2 Introduction

Preclusion of infection is the most effective method to combat the respiratory and

gastrointestinal diseases that cause over 6 million annual deaths, worldwide (Boone

and Gerba, 2007; Mathers et al., 2008). Successful interventions to reduce disease

burden include hand and environmental hygiene (Siegel et al., 2007; Bell, 2006), but

the impact of these interventions is difficult to quantify because the importance of

contact with contaminated surfaces, or fomites, relative to other transmission routes

is uncertain (Mubareka et al., 2009; Brankston et al., 2007).

Evidence of the importance of fomites comes from both laboratory and field stud-

ies. Laboratory studies have demonstrated that handling either artificially–or nat-

urally–contaminated fomites by susceptible hosts indoors results in subsequent in-

fection (Gwaltney, 1982; Hall et al., 1980). Additionally, virus can be transferred

between hands and fomites on contact, and survive on fomites for hours or days

(Bean et al., 1982; Rusin et al., 2002; Abad et al., 1994). In a field study, environ-

mental hygiene as an intervention significantly reduced illness–related absenteeism in

classrooms (Bright et al., 2009). Additionally, fomites, such as carpets (Osterholm

et al., 1979; Evans et al., 2002), towels, and medication cart items (Morens and Rash,

1995) have been implicated as the primary cause of multiple outbreaks. Despite this

evidence, questions remain regarding relative efficacy of fomite–mediated transmis-

sion relative to other exposure routes (Jennings and Dick, 1987; Atkinson and Wein,

2008) and likelihood of virus transfer from fomites to hosts (Pappas et al., 2009).

Surface contamination is most often described by the positivity rate, defined as

the fraction of total samples collected on which the organism is detectable (Butz

et al., 1993). However, the positivity rate does not provide an indicator of infection

risk, which depends on exposure magnitude (Haas et al., 1999) and therefore requires

information about the quantity of virus on the surface. Virus quantity on a surface,

expressed as number of virus or virus equivalents per unit area, has only been mea-

sured in a few studies (Bellamy et al., 1998; Russell et al., 2006; Piazza et al., 1987).

Moreover, positivity rate is influenced by the sampling method and detection assay:


more sensitive sampling methods and detection assays will yield increases in posi-

tivity rates even though the actual level of virus contamination may be unchanged.

Use of a sensitive, standard method would limit bias introduced by various sampling

methods.

Two previous studies have compared virus surface sampling methods and sug-

gested that implement type (the tool used to collect the sample, such as a swab) and

eluent type (the liquid used to aid in removal, such as saline solution) significantly

influence virus recovery. Carducci et al. (2002) recovered a greater fraction of hepati-

tis C virus from a seeded surface using beef extract than using bovine serum albumin

when swabbing with a cotton–tipped applicator. The study demonstrated that elu-

ent type can significantly impact virus recovery from surfaces. Similarly, Taku et al.

(2002) demonstrated the impact of implement type by comparing calicivirus recovery

from food surfaces for four sampling methods. Rinsing a surface in 0.05 M glycine

buffer, rubbing with a cell scraper, then aspirating the buffer was recommended over:

1) rinsing surface in buffer then aspirating, 2) swabbing surface with cotton–tipped

applicator, or 3) swabbing surface with a nylon filter. However, Taku et al. (2002)

recommended method is not easily adapted to the geometry of most fomites. Fur-

ther research is needed to refine implement and eluent choice for sampling fomites to

maximize virus recovery.

In the present study, we systematically review the literature on virus sampling

of fomites and use an extensive laboratory–based trial to compare methods of virus

detection on surfaces. We identify, summarize, and analyze 45 articles that include

unique data sets on virus detection on surfaces. The most commonly used and most ef-

fective sampling methods identified from the meta–analysis are compared in a labora-

tory–based study for removal of bacteriophage MS2, as measured using culture–based

and quantitative reverse–transcription polymerase chain reaction (qRT–PCR), from

plastic and stainless steel surfaces. Using both the literature review and experimental

results, we identify polyester–tipped swabs prewetted in either 1/4 strength Ringer’s

solution (heretofore referred to as “Ringer’s”) or saline solution as the most effective

buffer and implement combination to remove virus from nonporous fomites.


4.3 Materials and Methods

4.3.1 Review of Virus Surface Sampling Literature

Relevant articles were identified by searching the PubMed database on 5 February

2010 with keywords: “virus” and one of the following: 1) “fomite(s)”, 2) “environmen-

tal contamination”, 3) “environmental surface(s)”, or 4) “environmental sample(s)”

and “surface(s)”.

Only articles written in English were considered. Of those identified, the articles

included in the review fit the following criteria: 1) included original data collected

from environmental surfaces, where clinical (e.g. skin, bodily fluids) and food (e.g.

meat, vegetables) surfaces were not considered, and 2) tested samples for human

pathogenic virus or fecal indicator bacteriophage (e.g. somatic, F+ bacteriophage).

To identify articles not included in PubMed, the citations of the articles fitting the

criteria were also reviewed.

For analysis, data from the articles were separated into data sets according to the

virus and the presence/absence of a clinically infected individual. That is, articles

that reported positivity rates for multiple viruses were split into separate data sets for

each virus. Similarly, articles that sampled surfaces during periods that encompassed

both presence and absence of clinically infected individuals were split into separate

data sets for each time period. Samples collected two weeks before and after at least

one individual was identified as clinically infected were considered separately than

samples collected where no clinically infected individual was present. In this manner,

seventy–four data sets from forty–five articles were obtained.

Positivity rate was determined, as the outcome variable, for each data set. Positiv-

ity rate was the only feasible outcome variable as most (96%) of the studies identified

reported presence/absence of virus on surfaces. Only a few (3 of 74, or 4%) reported

quantitative data. If the authors included clinical or food samples, those samples

were removed. To allow for logit–transformation, the positivity rate for studies that

detected the virus on none or all of the samples was adjusted to detection limits of 1/n

or (n − 1)/n, respectively, where n is the study’s total number of samples collected.

The positivity rate is an inherently biased outcome variable because the lower limit


of detection (LLOD) likely varies across studies for reasons described previously. As

few studies (21%) reported either the quantitative concentration of the virus or the

LLOD of the sampling method, the positivity rate could not be adjusted to account

for the bias.

We assessed the influence of the implement and eluent used to collect and analyze

the samples on positivity rate. Similar implement and eluents were grouped for data

analysis. Polyester and Dacron swabs were both categorized as polyester. The eluent

used was categorized into one of four groups: media (defined here as any eluent with a

carbon source, and includes Amies medium, beef extract, brain heart infusion broth,

Letheen broth, minimum essential medium, RPMI–1640, and tryptose phosphate

broth with 0.5% gelatin), saline (defined as any isotonic eluent without a carbon

source, and includes phosphate buffered saline, 0.8% saline, and Ringer’s solution),

water, or unreported. Additives and constituents of eluents, such as antibiotics,

were ignored for data analysis with the lone exception of calcium. We examined

whether or not the presence of calcium in the eluent influenced positivity rate, where

calcium is present in Ringer’s solution, Amies medium, minimum essential medium,

and RPMI–1640.

Statistics

The positivity rate for each study was logit–transformed, and normality of trans-

formed data was assessed using the Shapiro–Wilk test to support use of parameteric

statistics. Two bivariate linear models were used to determine the significance of im-

plement and eluent choice, separately, on transformed positivity rate; positivity rate

was weighted by total number of samples in each study. To determine effect of trace

calcium in the eluent on positivity rate, a χ2 test for equal proportions was used.

All statistics were performed using R (version 2.9.0, R Foundation for Statistical

Computing, Vienna, Austria).


4.3.2 Laboratory–Based Surface Sampling Method Compar-

ison

In a laboratory–based trial, we compared fraction of virus recovered from surfaces

using a subset of the implement and eluent choices identified in the literature as most

commonly used and most effective.

Virus and preparation of inoculum

MS2 bacteriophage was obtained from the American Type Culture Collection

(ATCC). MS2 (ATCC #15597–B1) is a +–sense RNA virus with a icosahedral,

tailless, capsid of 27 nm in diameter. The isoelectric point (pI) of MS2 is 3.9. MS2

bacteriophage was chosen because of its prior use a surrogate for human viruses,

such as norovirus, (Dawson et al., 2005) and the availability of plaque assay and

qRT–PCR methods to enumerate both infective phage and copies of nucleic acids

(USEPA, 2001; O’Connell et al., 2006). E. coli HS(pFamp)R (ATCC #700891) was

used to propagate and enumerate MS2.

The inoculum used in the study was prepared according to the polyethylene gly-

col precipitation method (Pecson et al., 2009). The propagated virus was then enu-

merated using the double agar layer method and diluted in dilution buffer (5 mM

NaH2PO4, 10mM NaCl, pH = 7.4) to 1× 104 PFU/ml to be used as virus stock. Im-

mediately before being seeded on the surface, the virus stock was mixed with tryptic

soy broth to form a 50% solution.

Implement and Eluents Tested

The two most commonly used, and the single most effective (highest mean positivity

rate) implements and eluents were identified from the literature for use in the labora-

tory study. The implements tested included the cotton–tipped and polyester–tipped

swabs as the most commonly used (used in 39 and 12, respectively, of the 74 studies)

and antistatic cloth as the most effective (positivity rate of 0.408). Similarly, the elu-

ents tested included saline and viral transport media (used in 18 and 9, respectively,


of the 74 studies) as the most common and Ringer’s solution (used in the same article

as the antistatic cloth) as the most effective.

A fourth eluent, termed acid/base, was added to assess a novel combination

adapted from a method to concentrate virus from environmental water samples

(Katayama et al., 2002). The acid/base eluent relies on knowledge of the virus

surface charge to improve recovery from surfaces. This study is the first use of the

eluent combination to remove virus from surfaces. Briefly, a weakly acidic (0.5 mM

dihydrogen sulfate (H2SO4)) eluent is used to wet the implement prior to sampling.

Viruses with low isoelectric points adsorb to negatively–charged surfaces (like cotton)

under acidic conditions (Katayama et al., 2002). After sampling, the implement is

placed into a weakly basic (1 mM sodium hydroxide, pH 10.5–10.8) eluent which

reverses the surface charge of the virus to elute the virus from the implement.

Surfaces Tested

To determine the method most effective in removing virus from surfaces, we com-

pared recovery from both high temperature polyvinyl chloride (PVC) plastic (Part No.

8748K21) and type 304 stainless steel with a mirror–like finish (Part No. 9785K11),

both obtained from McMaster–Carr (Santa Fe Springs, CA, USA). Many of the sur-

faces identified in the literature review that were frequently contaminated (e.g. door

knobs, faucet handles, drains, medical instruments, toys, playmats, computer parts,

telephones) were composed of plastic or metal. PVC plastic and stainless steel, in

930cm2 square sheets, were chosen as representative samples as it was infeasible to

test every potential surface. 10 replicates for each eluent and implement combination

were tested on both surfaces. In total, 240 samples were collected (3 implements, 4

eluents, 2 surfaces, and 10 replicates). All were tested using the double agar layer

plaque assay method, and a subset using qRT–PCR.

Study Design

For both plastic and stainless steel surfaces, a 5 µl inoculum of approximately

4900–5200 PFU bacteriophage was seeded in the center of 120 5 cm × 5 cm surface


swatches. The seeded aliquot was dried for 45 ± 1 minutes under ambient con-

ditions (temperature 20–22◦C and relative humidity of 45–60%, determined by a

thermometer and hygrometer (Springfield Precision Instruments, Wood Ridge, NJ)).

The order of implement and eluent combinations used to recover bacteriophage

from the surfaces was randomized prior to the start of the study. The polyester and

cotton–tipped swabs were obtained from Fisher Scientific (Thermo Fisher Scientific,

Waltham, MA, USA). The antistatic cloths were obtained from Bel–Art Products

(Pequannock, NJ, USA) and cut into single–ply square swatches of approximately 9

cm2. The eluents used were Ringer’s solution (EMD Chemicals, Inc, Gibbstown, NJ,

USA), 0.85% saline solution, virus transport media (Copan Diagnostics, Murietta,

CA, USA), and acid/base.

Centrifuge tubes (15ml, BD Biosciences, San Jose, CA) were filled with 1.5 ml of

0.85% saline, viral transport media, Ringer’s solution, or 1 mM sodium hydroxide. To

sample, the polyester or cotton–tipped swabs were wetted in the eluent (or in 0.5 mM

dihydrogen sulfate, for acid/base) and then rubbed with moderate and consistent

pressure across the surface first horizontally, then vertically, then diagonally for a

total of 10 s. The swab was then placed into the centrifuge tube, and the tube was

capped and stored on ice for 4 hours to mimic typical transportation time. Antistatic

cloth, otherwise following the same procedure, was not wetted prior to sampling.

After storage, the samples were vortexed for 60 s. An aliquot of 100 µl was used

to assay the samples for infective bacteriophage using the double agar layer method

(USEPA, 2001). The remaining sample was stored at –80◦C.

qRT–PCR

Viral recovery was determined using qRT–PCR from plastic and stainless steel sur-

faces for only two implement/eluent combinations: cotton–tipped swab in saline

solution and polyester–tipped swab in Ringer’s. Cotton/saline was the most com-

mon implement/eluent combination used in studies reviewed in the meta–analysis;

polyester/Ringer’s was the combination with highest efficacy of recovery measured us-

ing the culture–based assay (to be shown). Twenty–eight samples were assayed using

qRT–PCR: seven samples for each combination of cotton/saline or polyester/Ringer’s


and plastic or stainless steel surface. RNA was extracted and quantified from 200 µl

of sample volume, after storage at –80◦C for 15–20 days following sample collection.

qRT–PCR was performed on the extracts within 6 hours.

To extract viral RNA, we used the Invitrogen PureLink Viral RNA/DNA extrac-

tion kit (Invitrogen, Carlsbad, CA) according to manufacturer’s instructions using

200 µl samples eluted in 20 µl of DNase/RNase–free water. Genomic RNA was

enumerated using the Taqman quantitative reverse–transcption polymerase chain re-

action (qRT–PCR) with reagents, primers, and cycling conditions of O’Connell et al.

(2006) for a 25 µl reaction with 5 µl template (O’Connell et al., 2006). The location

in the MS2 genome of the primers, probe, and target (the sequence of the qRT–PCR

amplicon), is the RNA replicase β chain. The forward primer (5’–GCTCTGA-

GAGCGGCTCTATTG–3’), reverse primer (5’–CGTTATAGCGGACCGCGT–3’),

and probe (5’–[FAM]–CCGAGACCAATGTGCGCCGTG–[TAMRA]–3’) were ob-

tained from Eurofins MWG Operon (Huntsville, AL) (O’Connell et al., 2006). RNA

standards were created from total genomic RNA extracted without aid of transfer

RNA from a high titer of purified MS2 bacteriophage, enumerated as 20 ng/µl using

a NanoDrop ND–1000 Spectrophotometer (Thermo Fisher Scientific, Waltham, MA,

USA) and diluted to four standards, at 10–fold dilution, between 0.1 fg/µl (50

genome equivalents / µl) and 10000 fg / µl (50000 genome equivalents / µl). A

genome of 3569 nucleotides with average molecular mass of 330 Da per nucleotide

was assumed to convert RNA concentration to genome equivalents (O’Connell et al.,

2006). qRT–PCR was performed using a StepOne Plus RealTime PCR System

(Applied Biosystems, Carlsbad, CA) and all samples and standards were run in

triplicate.

Statistics

Descriptive statistics (mean, median, standard deviation) are provided for recovery

of infective bacteriophage from each surface using each method. To determine the

implement and eluent choice that most effectively removes bacteriophage from sur-

faces, an n–way ANOVA with post–hoc Tukey’s test was performed on untransformed


data. Surface sampled, implement, and eluent are the independent variables; frac-

tion of recovered infective bacteriophage is the dependent variable. Significance was

determined if the p–value ≤ 0.05. Fraction of target RNA recovered was defined as

target copies of RNA enumerated for each sample divided by the number of target

copies seeded. Linear regression was used to model the relationship of the number

of target copies estimated from qRT–PCR as a function of the number of infective

bacteriophage estimated using the double agar layer method, with an intercept set at

0. Variables for surface swabbed (plastic or stainless steel) and for implement/eluent

combination were included in the regression model.

4.4 Results

4.4.1 Literature Review

310 articles were identified in the keyword search using PubMed. Of those, 40 fit the

specified criteria. Review of the citations revealed an additional 5 relevant articles. In

total, 45 relevant articles were identified. Eleven of the articles sampled for multiple

pathogens and/or during time periods where a clinically infected individual was and

was not present. As a result, the articles are treated as 74 separate studies. A

summary of the articles, including the separation into studies, implement, eluent,

and assay used, positivity rate, and locale are provided in Table B.1. Definitions of

abbreviations used in Table B.1 are provided in Table B.2.

Authors measured environmental contamination of 20 different etiologic agents,

including causative agents of gastrointestinal, respiratory, bloodborne and/or sexu-

ally–transmitted diseases. A division of studies by virus, including the number of stud-

ies for each, total samples collected, number of samples with detectable virus, and frac-

tion of samples with detectable virus are provided in Table B.3. The positivity rates

of the studies, when logit–transformed, were normally–distributed (Shapiro–Wilk test

for normality, W = 0.98, p =0.28). In total, 6804 samples were collected with de-

tectable virus on 1105 for an overall positivity rate of 0.162.


In total, twelve different eluents (excluding additives) were used in the 74 identi-

fied studies. In 7 (9%) of the studies, the authors did not identify the eluent. Table 4.1

provides a summary of the studies, aggregated by eluent type, and includes the num-

ber of samples collected, number with detectable virus, and fraction with detectable

virus for each eluent. The authors of thirty–four of the studies (46%) used media,

while 29 (39%) used a saline solution. Studies where eluent was unreported were

grouped into a “not–reported” category and included in the linear model. The lin-

ear model demonstrated no signficant influence of eluent category on positivity rate,

when positivity rate was weighted by total samples collected (R2 = 0.05, p =0.33).

Additionally, eluents with calcium were not associated with a significantly different

fraction of samples with detectable virus (p > 0.05).

Four implement types were used in the studies. Studies where implement was

unreported or reported as unspecified swab type (23% of identified studies) were

grouped into a “not reported” category and included in the linear model. A division

of studies by implement type, including the number of studies for each, the total

samples collected, number with detectable virus, and fraction with detectable virus for

each implement type are provided in Table 4.2. Implement type explained 23% of the

variation in positivity rate (R2 = 0.23, p < 0.001) according to the linear model using

logit–transformed positivity rate weighted by total sample number as the dependent

variable. Compared to cotton–tipped swabs, positivity rate was significantly higher

for polyester swabs (p = 0.01) and significantly lower for rayon–tipped swabs (p =

0.02). We found no significant difference between cotton–tipped swabs and antistatic

cloths, although antistatic cloths had the highest positivity rate (0.408), likely due to

the small sample size.

4.4.2 Laboratory–based Surface Sampling Method Compar-

ison

Results of the recovery of MS2 bacteriophage from stainless steel and plastic for each

implement/eluent combination are provided in Table 4.3 and Figure 4.1. The mean

(µ) and standard deviation (σ) of the fraction recovered from stainless steel was 0.29


and 0.17, respectively. Recovery from plastic was similar, with a mean and standard

deviation of 0.30 and 0.24, respectively.

As demonstrated by n–way ANOVA (Table 4.4), the surface sampled did not sig-

nificantly influence the recovery of MS2 bacteriophage (p =0.63). Implement choice,

however, was significant (p < 0.001). Post–hoc Tukey’s test revealed that antistatic

cloths, with overall fraction of recovery of 0.09, were significantly lower than both

polyester swabs (mean recovery fraction = 0.40, p < 0.001) and cotton swabs (mean

recovery fraction = 0.38, p < 0.001). Recovery using polyester and cotton swabs were

not significantly different (p =0.32).

Similarly, eluent significantly influenced fraction of bacteriophage recovered (p

=0.01). The largest fraction was recovered using Ringer’s solution, with a mean

fraction recovered of 0.24, followed by saline (mean recovery fraction = 0.20) and

acid/base (mean recovery fraction = 0.19). Viral transport media recovered the lowest

fraction of virus (mean recovery fraction = 0.17). According to Tukey’s post–hoc test,

the fraction recovered was significantly different only between Ringer’s solution and

viral transport media (p =0.005).

The interaction effect of implement and eluent combination was not significant (p

=0.39). The combination of polyester swab and Ringer’s resulted in the largest mean

fraction recovered (mean recovery fraction = 0.48), though it was not significantly

different (p < 0.05) from polyester and any other eluent or Ringer’s and any other

implement besides antistatic cloth.

4.4.3 qRT–PCR

The surface inoculum, approximately 5×103 PFU, was determined to be approxi-

mately 2.9×105 target copies (58 target copies per plaque).

Twenty–eight samples using implement/eluent combinations of cotton/saline and

polyester/Ringer’s were assayed, and five (18%) were removed from analysis because

the quantified target RNA exceeded 100% recovery, or 2.9×105 copies, likely due to

laboratory error as suggested by high standard deviations in the triplicate samples.

Mean and standard deviation of the fraction of target RNA recovered was 0.25 and


0.22, respectively. No product was detected in the blanks and no template controls

used in the study.

Linear regression of recovered target copies as a function of recovered infective

bacteriophage, surface, and implement/eluent combination demonstrated no signifi-

cant effect due to surface and sampling method on qRT–PCR recovery. Surface did

not significantly influence recovery using qRT–PCR (p =0.62). Similarly, although

polyester/Ringer’s recovered approximately 18000 greater target copies per sample

than cotton/saline, the difference was not significant (p =0.62).

The linear model also elucidated the relationship between recovery using

qRT–PCR and recovery using a plaque assay. The linear relationship between

recovered infective bacteriophage and target copies was significant (p < 0.001) and

explained 79% of the variability (R2 = 0.79). Specifically, the ratio of recovered

target copies to infective bacteriophage after a dessication step of 45 ± 1 minute

was 59.8. This is consistent with previous estimates of the ratio of target copies to

infective MS2 bacteriophage as determined using a plaque assay (O’Connell et al.,

2006).

4.5 Discussion

Indoor surface sampling is necessary to understand the role of fomites in disease

transmission. However, studies employ many different sampling methods to recover

virus from surfaces. In the present study, we demonstrate through a combined lit-

erature review and laboratory trial, that the sampling method significantly impacts

virus recovery. In fact, sampling method may contribute to the wide range in pos-

itivity rates reported across studies. Standardization of a sampling method to the

polyester–tipped swab and 1/4–strength Ringer’s solution or saline solution, may

reduce variability and facilitate cross–study comparisons.

To reduce influence of sampling method on positivity rate, polyester–tipped

swabs should be used for virus detection on surfaces. Both the meta–analysis

and laboratory trial demonstrated that polyester–tipped swabs improved recovery

relative to other implements. In the meta–analysis the implement choice explained


24% of the variability in the positivity rate, a strong relationship that suggests that

implement choice affects study outcomes. The recommendation to use polyester

swabs is consistent with the recommendation of the United States Centers for

Disease Control and Prevention to use synthetic fibers for clinical sample collection

(http://www.cdc.gov/h1n1flu/specimencollection.htm). Cotton–tipped swabs are

known to contain trace contaminants (Ellner and Ellner, 1966; Pollock, 1947) with

demonstrated bacterial inhibition (Pollock, 1947). Similar interference with virus

detection may be possible. Furthermore, the irregular arrangement of cotton fibers

reduces elution of bacteria (Osterblad et al., 2003), and could contribute to the

observed reduced virus recovery relative to polyester. Antistatic cloths recovered the

lowest fraction of seeded virus in the methods comparison study. Antistatic cloths

were not prewetted in this study, which likely contributed to the low recovery. As

antistatic cloth is composed of synthetic fiber, prewetting may provide fractional

recovery similar to polyester–tipped swabs. A potential benefit of using antistatic

cloth is that larger surface areas can be sampled. However, this was not specifically

addressed in our study.

Ringer’s or saline solution should be used as an eluent for virus detection on

surfaces. Although the meta–analysis demonstrated no significant differences in pos-

itivity rate attributable to eluent category, the laboratory trial demonstrated that

Ringer’s recovered the greatest fraction of seeded virus using culture–based method,

followed closely by saline solution. The laboratory trial is not meant to be all inclusive

as testing all possible implement and eluent combinations, including additives to the

eluent such as lecithin or Tween 80, is infeasible. However, based on the combinations

tested, Ringer’s or saline solution should be used as eluent in future studies.

Trace calcium in the eluent does not impact virus recovery on surfaces. Calcium

impacts virus adsorption to surfaces (Fuhs et al., 1985) and reduces nucleic acid

detection using PCR by inhibiting the polymerase enzyme (Bickley et al., 2008).

However, based on the meta–analysis, there was no significant influence on positivity

rate attributable to calcium. Similarly, Ringer’s solution (which differs from saline

solution by the addition of potassium and calcium chloride) did not significantly differ

from saline in recovery of infective bacteriophage using plaque assay or target RNA


using qRT–PCR.

Use of the acid/base eluent method relied on the knowledge of the MS2 bacterio-

phage isoelectric point (3.9) to aid in recovery. Future studies intending to replicate

this method need to consider the isoelectric point of the virus prior to assay develop-

ment.

When assessing infection exposure and risk from environmental contamination,

the sampling method’s LLOD is needed (Herzog et al., 2009). Only 13 of the 45

articles reviewed included a quantitative assessment of the LLOD of their sampling

method. The lack of a reported LLOD and the reliance on presence/absence data

makes cross–comparison of studies and relating positivity rates to risk infeasible. In

the present study, the mean and range of fractional recovery of infective bacteriophage

using polyester/Ringer’s was 0.48 and (0.20, 0.98), respectively. Using the mean and

range of fractional recovery, along with the assumption that the bacteriophage double

agar layer method enumerates ≥ 1 PFU, the lower limit of detection is 2.1 with range

(1.0, 5.0) PFU per area sampled. Similarly, assuming the qRT–PCR method has a

lower limit of quantification of ≥ 250 genome equivalents (O’Connell et al., 2006),

consistent with our standard curves, then the theoretical quantification limit is 892

with range (431, 2.5×104) genome equivalents based on a mean and range fractional

recovery for RNA of 0.28 and (0.01, 0.58). In the future, reporting the LLOD would

allow authors to combine dose–reponse curves and positivity rates to exposure and

risk estimates (Haas et al., 1999; Julian et al., 2009).

Although a standardized sampling method is recommended to allow cross–comparison

of studies reporting positivity rates, there may be limitations. The recommendation

to use polyester swabs in Ringer’s or saline solution is based on results from both

a laboratory–scale study and a review of literature. The laboratory–scale study

was based on recovery of one virus (MS2 bacteriophage) and two surfaces (high

temperature PVC plastic and type 304 stainless steel with a mirror–like finish).

Pathogenic viruses, however, have wide variation in physicochemical properties (such

as size, shape, and isoelectric point) that may influence recovery by a standardized

method. Similarly, the morphology and composition of the fomites’ surfaces may

also influence recovery. PVC plastic and stainless steel are representative samples


of many potential fomites, as both are widely used in consumer products (Heudorf

et al., 2007; Adams, 2009). As not all potential fomites are made of PVC plastic or

stainless steel, the method recommended here may not be the most efficient recovery

method for every virus / surface combination sampled. Nevertheless, a standardized

method is recommended for cross–comparison of studies reporting positivity rates.

Our findings suggest polyester–tipped swabs with Ringer’s or saline solution perform

best.

A priority in future research is linking surface contamination to adverse health

outcomes. There is currently limited evidence that virus contamination on fomites

is linked to increased risk of adverse health outcomes. To address this, longitudinal

studies could simultaneously track health outcomes and surface contamination, sim-

ilar to the work of (Gallimore et al., 2006; Bright et al., 2009; Boxman, Dijkman,

Verhoef, Maat, van Dijk, Vennema and Koopmans, 2009), using the recommended

sampling method. Additionally, quantifying virus concentrations on surfaces is a pri-

ority. Knowledge of virus quantity is an important step toward linking fomites to

health risk, as exposures to greater concentrations result in greater risk of infection

(Haas et al., 1999). Sampling surfaces with polyester/Ringer’s or polyester/saline, as

evidenced by this study, is compatible with quantification of virus using plaque assay

or qRT–PCR. Use of a standard method with a known recovery fraction will facilitate

extrapolation of measured surface quantities to exposure and risk estimates.

4.6 Acknowledgments

We thank the members of the Boehm Lab, who assisted with the work and/or pro-

vided suggestions for improving the manuscript.

The research has been funded, in part, by the UPS Foundation and the United

States Environmental Protection Agency (EPA) under the Science to Achieve Results

(STAR) Graduate Fellowship Program, Assistance Agreement No. F07D30757. FT

was supported by NSF awards BES–0641406 and SES–0827384. EPA has not officially

endorsed this publication and the views expressed herein may not reflect the views of

the EPA.


4.7 Figures


Fra

ctio

n R

ecov

ered

0.00.20.40.60.81.0

A C P

●

Acid/Base

A C P

●

●

●

Ringer's

A C P

●●

Saline

A C P

●

●●

●

VTM

Figure 4.1: Fraction of seeded MS2 bacteriophage recovered by implement/eluentcombination using the double agar layer method to enumerate plaque forming units.Abbreviations used include: “VTM” for viral transport medium, “A” for antistaticcloth, “C” for cotton, and “P” for polyester–tipped swabs. Boxes depict the 25th, me-dian, and 75th quartiles. Whiskers represent the 10th and 90th percentiles. Outliersare denoted by “•”


4.8 Tables


No.

No.

No.

Pos.

Fra

c.E

luen

tS

tud

ies

Sam

p.

Sam

p.

Pos.

Ref

.

Med

ia34

3643

629

0.1

73

TP

Bge

l5

730

120.0

16

Akhte

ret

al.

(1995)

BE

293

30.0

32

Fis

cher

etal.

(2008);

Card

ucc

iet

al.

(2002)

BH

IB8

424

100

0.2

36

Wil

deet

al.

(1992);

Win

ther

etal.

(2007);

Pap

paset

al.

(2009);

Gw

alt

ney

(1982)

VT

M9

1418

332

0.2

34

Ch

eesb

rou

ghet

al.

(2000);

Gre

enet

al.

(1998);

Gold

ham

mer

etal.

(2006);

Ch

enet

al.

(2004);

Booth

etal.

(2005);

Ru

ssel

let

al.

(2006);

Dow

ellet

al.

(2004)

ME

Mc

353

837

0.0

69

Kes

wic

ket

al.

(1983);

Gu

rley

etal.

(2007);

Sou

leet

al.

(1999)

RP

MI1

640c

299

290.2

93

Asa

noet

al.

(1999);

Yosh

ikaw

aet

al.

(2001)

LB

221

883

0.3

81

Boon

ean

dG

erb

a(2

005)

Am

iesc

312

333

0.2

68

Jon

eset

al.

(2007);

Bri

ghtet

al.

(2009)

Sal

ine

Sol

uti

ons

2927

6336

70.1

33

Sal

ine

1822

1822

80.1

03

Gall

imore

etal.

(2006,

2008);

Wuet

al.

(2005);

Pia

zzaet

al.

(1987);

Fro

ioet

al.

(2003);

Boon

ean

dG

erb

a(2

005);

Fer

encz

yet

al.

(1989);

Led

erm

an

etal.

(2009);

Bel

lam

yet

al.

(1998);

Les

saet

al.

(2009)

PB

S9

420

880.2

10

Gall

imore

etal.

(2005);

Str

au

sset

al.

(2002);

Bu

tzet

al.

(1993);

Kaw

ah

ara

an

dY

osh

ida

(2009);

Ku

usiet

al.

(2002);

Wid

dow

sonet

al.

(2002);

Bau

sch

etal.

(2007);

Lop

ezet

al.

(2008)

Rin

ger’

sc2

125

510.4

08

Box

man

,D

ijkm

an,

Ver

hoef

,M

aat,

van

Dij

k,

Ven

nem

aan

dK

oop

man

s(2

009);

Box

man

,D

ijkm

an

,te

Loek

e,H

agel

e,T

ilb

urg

,V

enn

ema

and

Koop

-m

an

s(2

009)

Wat

er4

120

110.0

92

Ru

nn

er(2

007)

Not

Rep

orte

d7

278

980.3

53

Gir

ou

etal.

(2008);

Lym

an

etal.

(2009);

Ham

ad

aet

al.

(2008);

CD

C(2

008)

Tot

al74

6804

1105

0.1

62

Tab

le4.

1:Sum

mar

yof

the

eluen

tsuse

din

the

revie

wed

arti

cles

,in

cludin

gnum

ber

ofst

udie

s(“

No.

Stu

die

s.”)

,num

ber

ofsa

mple

sco

llec

ted

(“N

o.Sam

p.”

),num

ber

ofsa

mple

sw

ith

det

ecta

ble

vir

us

(“N

o.P

os.

Sam

p.”

),an

dfr

acti

onof

sam

ple

sw

ith

det

ecta

ble

vir

us

(“F

rac.

Pos

.”).

Elu

ents

wit

htr

ace

calc

ium

are

den

oted

by

’c’


No.

No.

No.

Pos.

Fra

c.Im

ple

men

tS

tud

ies

Sam

p.

Sam

p.

Pos.

Ref

.

Cot

ton

3935

3250

20.1

42

Ch

eesb

rou

gh

etal.

(2000);

Gall

imore

etal.

(2005,

2006,

2008);

Gre

enet

al.

(1998);

Kes

wic

ket

al.

(1983);

Wild

eet

al.

(1992);

Win

ther

etal.

(2007);

Gu

rley

etal.

(2007);

Gold

-h

am

mer

etal.

(2006);

Wuet

al.

(2005);

Ch

enet

al.

(2004);

Str

au

sset

al.

(2002);

Pap

paset

al.

(2009);

Asa

no

etal.

(1999);

Bu

tzet

al.

(1993);

Fis

cher

etal.

(2008);

Gw

alt

ney

(1982);

Kaw

ah

ara

an

dY

osh

ida

(2009);

Ku

usi

etal.

(2002);

Sou

leet

al.

(1999);

Wid

dow

sonet

al.

(2002);

Pia

zzaet

al.

(1987);

Yosh

ikaw

aet

al.

(2001);

Card

ucc

iet

al.

(2002);

Fro

ioet

al.

(2003)

Pol

yest

er12

1411

388

0.2

75

Bau

schet

al.

(2007);

Boon

ean

dG

erb

a(2

005);

Dow

ellet

al.

(2004);

Fer

encz

yet

al.

(1989);

Led

erm

anet

al.

(2009);

Lop

ezet

al.

(2008);

Ru

ssel

let

al.

(2006)

Ray

on4

571

360.0

63

Bel

lam

yet

al.

(1998);

Bri

ghtet

al.

(2009);

Jon

eset

al.

(2007)

Anti

stat

ic2

125

510.4

08

Box

man

,D

ijkm

an

,V

erh

oef

,M

aat,

van

Dij

k,

Ven

nem

aan

dK

oop

man

s(2

009);

Box

man

,D

ijkm

an,

teL

oek

e,H

agel

e,T

ilb

urg

,V

enn

ema

an

dK

oop

man

s(2

009)

Not

Rep

orte

d17

1165

128

0.1

10

Akhte

ret

al.

(1995);

Gir

ou

etal.

(2008);

Ham

ad

aet

al.

(2008);

Les

saet

al.

(2009);

Lym

an

etal.

(2009);

Ru

nn

er(2

007)

Tot

al74

6804

1105

0.1

62

Tab

le4.

2:Sum

mar

yof

the

imple

men

tty

pes

use

din

the

revie

wed

arti

cles

,in

cludin

gth

enum

ber

ofst

udie

s(“

No.

Stu

die

s.”)

,th

eto

tal

num

ber

ofsa

mple

s(“

No.

Sam

p.”

),th

esa

mple

sw

ith

det

ecta

ble

vir

us

(“N

o.P

os.

Sam

p.”

),an

dth

efr

acti

onof

sam

ple

sw

ith

det

ecta

ble

vir

us

(“F

rac.

Pos

.”)

are

also

pro

vid

ed


Stainless Steel PlasticImplement Eluent µ median σ µ median σCotton Saline 0.38 0.38 0.15 0.39 0.45 0.16

Ringer’s 0.33 0.34 0.11 0.54 0.56 0.16VTM 0.35 0.38 0.13 0.36 0.34 0.07Acid/Base 0.32 0.32 0.12 0.37 0.33 0.15

Polyester Saline 0.39 0.38 0.17 0.39 0.41 0.12Ringer’s 0.39 0.38 0.13 0.59 0.56 0.21VTM 0.29 0.30 0.13 0.39 0.37 0.13Acid/Base 0.39 0.38 0.17 0.48 0.48 0.11

Antistatic Saline 0.15 0.13 0.15 0.007 0.003 0.01Ringer’s 0.16 0.10 0.18 0.032 0.003 0.08VTM 0.10 0.07 0.12 0.009 0.003 0.01Acid/Base 0.23 0.24 0.14 0.003 0.003 0.001

Table 4.3: Summary of the fraction of MS2 bacteriophage recovered using each imple-ment/eluent combination from stainless steel and plastic surfaces. The mean, median,and standard deviation are reported


Effects d.f. Sum of Squares Mean Square F–value p–value

Surface 1 0.01 0.01 0.23 0.63Implement 2 4.76 2.38 111.5 <0.001Eluent 3 0.24 0.08 3.73 0.01Implement:Eluent 6 0.13 0.02 1.05 0.40Residuals 217 4.6 0.02

Table 4.4: Fraction of virus recovered from a seeded surface as a function of thesurface’s material and the implement and eluent used, based on statistical results ofn-way ANOVA

Chapter 5

Evidence for Causal Links between

Respiratory Illness and Indicator

Bacteria on Surfaces in Child Care

Centers

.

The results presented in this chapter will be submitted to a peer reviewed journal

in Winter 2011. Amy J. Pickering contributed extensively to the experimental design,

data collection, data analysis, and manuscript preparation, and will be co-author

on the resulting publication. James O. Leckie and Alexandria B. Boehm will also

appear as co-authors, for their contributions to study design, data interpretation,

and manuscript improvements.

96

CHAPTER 5. HEALTH AND SURFACES IN CHILD CARE CENTERS 97

5.1 Abstract

BACKGROUND: The link between microbial contamination on surfaces and

health outcomes has not been fully established. Investigating temporal trends in

health and environmental contamination may provide evidence for causal links

between surface contamination and adverse health outcomes.

OBJECTIVES: The objective is to investigate causal relationships between con-

tamination on hands and surfaces and health in child care centers.

METHODS: The present study tracks both respiratory and gastrointestinal dis-

ease incidence while monitoring weekly hand and environmental surface contamina-

tion over four months in child care centers. Microbial contamination was determined

using quantitative densities of fecal indicator bacteria as well as presence/absence of

viral pathogens. Health was monitored daily by childcare staff, who tracked adverse

health outcomes, including respiratory illness.

RESULTS: Symptomatic respiratory illness is significantly and positively asso-

ciated with hand contamination and with environmental contamination. Detection of

enterovirus on hands provides further support of the importance of surfaces in disease

transmission.

CONCLUSIONS: Symptomatic respiratory illness is both caused by, and causes

increases in microbial contamination on hands. Specifically, increases in microbial

contamination led to increases in symptomatic respiratory illness four to six days

later, in agreement with typical incubation periods for respiratory illness. Respiratory

illness also led to increases in microbial contamination on hands during presentation

of symptoms.

5.2 Introduction

Over 4.6 million children under the age of five years old are enrolled in center-based

child care in the United States (Laughlin, 2010). Children attending center-based

child care suffer 1.5-3 times more respiratory and 2-3.5 times more gastrointestinal


episodes per year than those in home-based child care (Fleming et al., 1987; Alexan-

der et al., 1990; Lu et al., 2004). Attendees of center-based care are not the only

ones with increased risk of illness. Evidence of the role of children in disseminating

disease through communities abounds. Examples include the documented persistence

of hepatitis A infections in areas with child care centers (Hadler et al., 1980; Desenc-

los and MacLafferty, 1993), increased disease prevalence in households with children

attending child care (Garrett et al., 2006), and reductions in prevalence within com-

munities following the vaccination of children (Dagan et al., 2005; Loeb et al., 2009).

Therefore, reductions in infectious disease transmission within child care centers may

influence burden in the community-at-large.

Reductions in disease burden are often achieved through interventions tailored to

interrupt known transmission routes. Studies have shown that in child care centers,

the introduction of hygiene programs significantly reduce gastrointestinal disease by

interrupting direct and indirect contact transmission (Krilov et al., 1996; Roberts,

Jorm, Patel, Smith, Douglas and McGilchrist, 2000; Lennell et al., 2008; Sandora

et al., 2008). However, the efficacy of hygiene programs in reducing respiratory ill-

ness is less certain. Hygiene intervention studies report conflicting results (Roberts,

Smith, Jorm, Patel, Douglas and McGilchrist, 2000; Sandora et al., 2008) despite the

documented importance of contact transmission for common respiratory pathogens

like rhinovirus and respiratory syncytial virus (Gwaltney et al., 1978; Hall et al.,

1980). Nevertheless, the success of hygiene programs is perceived to result from a re-

duction of the role of surfaces (e.g. hands and fomites) as mediators in transmission

(Lennell et al., 2008). In support of this perception, multiple studies have demon-

strated presence of pathogenic agents on hands and fomites during disease outbreaks,

and suggest that the presence of agents is an indicator of infection risk (Green et al.,

1998; Cheesbrough et al., 2000; Boone and Gerba, 2005; Wu et al., 2005).

In fact, the link between microbial contamination on surfaces and health outcomes

has not been fully established. Indoor environmental contamination may be endemic,

or it may be an outcome from existing infectious disease while not contributing to

further transmission. Among the first studies to establish a relationship between mi-

crobial contamination on surfaces and diarrheal illness is the work by Laborde et al.


(1993), which showed a significant association of hand and surface contamination

with reported diarrheal illness in 37 child care centers. Contamination was quantified

during a single survey for fecal coliform and was used as a representative sample for

the child care center. Conversely, a similar study by Soule et al. (1999) focusing on

rotavirus gastroenteritis in pediatric wards found no significant difference in rotavirus

on surfaces when comparing rooms occupied by patients with symptomatic rotavirus

gastroenteritis to those of uninfected patients. Although the cross–sectional anal-

yses demonstrate potential relationships between environmental contamination and

health, they can not elucidate causal links.

Previous research has provided limited evidence of causal links between environ-

mental contamination and adverse health outcomes. For example, laboratory studies

have demonstrated that infected individuals handling fomites increase rhinovirus pres-

ence (Gwaltney, 1982; Winther et al., 2007). Additionally, in child care centers, Butz

et al. (1993) demonstrated that presence of rotavirus contamination on surfaces fol-

lowed two of five observed diarrheal outbreaks. However, none of the aforementioned

studies investigated the role of surface contamination in precipitating outbreaks, as all

investigated increases in fomite contamination after illness. In two other studies (Van

et al. (1991) and Bright et al. (2009)), the authors tracked health and the presence of

organisms on fomites and hands in child care centers, but did not incorporate tem-

poral lags in analyses. Therefore, the studies acted more as cross–sectional analyses.

Neither study found significant associations between illness and surface contamina-

tion (Van et al., 1991; Bright et al., 2009), although Van et al. (1991) supported

findings that bacteria on hands is linked to diarrheal illness.

In the present study, we investigated the temporal relationship between health

and contamination of fomites and hands. The study is the first, to our knowledge, to

track both respiratory and gastrointestinal disease incidence while monitoring weekly

hand and environmental surface contamination. Microbial contamination in two child

care centers was determined using quantitative densities of fecal indicator bacteria

(e.g. Escherichia coli, enterococci, and fecal coliform) on hands and fomites as well

as presence/absence of viral pathogens (e.g. enterovirus and norovirus). Health was


monitored daily by childcare staff, who tracked absenteeism, illness-related absen-

teeism, and symptomatic respiratory and gastrointestinal illness. Based on the data

set, we investigate relationships between increases in symptomatic respiratory illness

and increases in microbial contamination on hands. By incorporating temporal lags,

we assess whether increases in hand or environmental contamination levels result

in, or are caused by, increases in respiratory illness when accounting for incubation

periods typical for common respiratory illness.

5.3 Methods and Materials

5.3.1 Sites

Permission from the Stanford University Research Compliance Office for Human Sub-

jects Research was obtained prior to the study. 80 individuals were enrolled in the

study at two child care centers in Northern California, USA: 8 child care center staff

(100% female) and 72 children (36% female) aged 3-5 years. Hereafter, the sites

are referred to as sites A and B. Each of the child care centers has a morning class

from 8:00-11:30 with 17-20 enrolled children and a separate afternoon class from

12:30–16:00 with 13-17 enrolled children. The children are assigned one of the class

times, and did not change times. Three staff members are on site at each facility.

Child care staff or the children’s parents/guardians provided written consent at the

start of the study. The child care centers were chosen because of similarities in: 1)

geographic location, 2) admission requirements, 3) class schedule, 4) enrollment size,

5) facility layouts, 6) staff size, 7) janitorial service, and 8) food vendor. Additionally,

the cleaning and hygiene regimens at the two centers were similar. The same jani-

torial service cleaned each facility nightly. Children were encouraged to wash their

hands with soap and water upon arrival at the facility, following breakfast or lunch

(at 8:45 or 13:15) and before snack (at 10:45 or 15:30). At Site B, staff encouraged

children to use alcohol based hand sanitizer (ABHS) in addition to soap and water.

Specifically, children upon arrival, before breakfast and lunch, before playtime, and

when coming in from the outside.


5.3.2 Surveys / Demographic Data Collection

In-person or telephone surveys lasting approximately 10-15 minutes were given to the

child care center staff and the parents or guardians of the enrolled children to obtain

information on study population demographics. The survey also included questions

on hygiene habits (e.g., frequency of handwashing for staff or parent and child), and

perceived health of the child such as previous gastrointestinal and respiratory illness,

likelihood of future illness, and perception of overall health as rated on a scale of

1-10, with 10 being extremely healthy (referred to as the “healthy child index”). The

survey was given at the beginning of the study; a shorter follow-up survey lasting

approximately 5-10 minutes including questions on hygiene habits, knowledge, and

perceived health of the child was administered at the end of the study as well.

5.3.3 Sampling Scheme

Between 5 February 2009, and 1 June 2009, the morning and afternoon classes of

both child care centers were visited by the research team weekly. A total of 64

sample events occurred over 16 weeks. Each visit lasted approximately one hour,

typically starting between 8:30-10:30 for the morning class and 13:00-15:00 for the

afternoon class. The visits were scheduled so as not to interrupt the children’s snacks

or learning activities. During each visit, 2-3 research team members collected 8-12

hand rinse samples, collected 5 environmental surface samples, and verified that the

health chart (see below) was filled out appropriately.

5.3.4 Health Data Collection

Attendance and symptoms of infections among the children and staff were recorded,

daily, by the child care center staff. The staff used standardized health charts that

included check boxes for symptoms of respiratory and gastrointestinal illness including

“stomach pain”, “3 or more bowel movements”, “vomit”, “bloody stool”, “diarrhea”,

“runny/stuffy nose”, “fever”, “sore throat”, “cough” and “headache” as well as a

comments section to allow for elaboration on reasons for absenteeism or descriptions of

“other” symptoms. The staff were not aware of any of the microbiological laboratory


results until the end of the study. The research team examined completion of the

health charts during sampling trips twice weekly, and collected the charts on the first

sampling trip of the following week.

Individuals with no recorded symptoms on a given day were classified as “non-

symptomatic”, otherwise the individual was classified as “symptomatic”. Symp-

tomatic illness was further classified as “respiratory” (“runny nose”, “headache”,

“cough”, or “sore throat”) or “gastrointestinal” (“stomach pain”, “diarrhea”, “bloody

stool”, “more than 3 bowel movements”, or “recent vomiting”) illness. Consecutive

days of symptomatic illness were classified as new episodes if they were preceded by

six symptom-free days, similar to the description of new episodes of illness described

elsewhere (Payment et al., 1991; Colford Jr et al., 2002). New episodes are hereafter

referred to as “new illness episodes” and are described by the first day of symptomatic

illness. The first day of illness was counted as the first observation of symptoms by

child care staff or a reason provided by parents for an absence.

The duration of an episode was calculated as the number of consecutive days

with reported symptoms or illness-related absenteeism. As no data were collected

on weekends, an episode was assumed to end on a Friday if no symptoms or illness-

related absenteeism were reported on the following Monday, whereas if symptoms or

illness-related absenteeism were reported on the following Monday, the episode was

assumed to include the weekend.

5.3.5 Hand Rinse Sampling

Between 8 and 12 hand rinse samples were collected during each visit, for a total

of 616 samples over the duration of the study. Eight to ten children were sampled

during each visit, and the three child care staff at each facility were sampled once

per week. The children were asked to participate in an order determined through

randomization of the list of enrolled children. If a child declined, the next child on

the list was approached. Once assent was obtained, the researcher recorded whether or

not there were visible signs of a runny nose, dirt on hands, and dirt under fingernails.

The researcher then asked the subject how s/he was feeling, and the response was


recorded and later reclassified as “sick”,“fine”, or “no response”. Hand rinse sampling

was performed using a modified glove-juice method as previously described (Pickering

et al., 2010). The subject was asked to place first one hand, and then the other, into

the same 69 oz. Whirl-pak bag (Nasco, Fort Atkinson, WI, USA) filled with 350

ml autoclaved Milli-Q grade water. The subject was encouraged to shake the hand

vigorously for 15 s; the researcher then massaged the hand through the bag for an

additional 15 s. After both hands were rinsed, the subject was provided a clean paper

towel to dry her/his hands. The sample was placed on ice and transported to the

laboratory, where it was processed within 6 hours.

5.3.6 Environmental Surface Sampling

Surface samples were obtained immediately after the hand rinse samples. Between

three and five fomites were sampled during each visit, for a total of 299, chosen based

on the subjective classification as a surface with a high likelihood of contact based on

children’s activities in the previous hour. For example, a toy block would be sampled

if one or more children had been observed playing with the block. A summary of the

fomites tested is reported in Table 5.1, with the most commonly sampled surfaces

including toys, table tops, faucets, and doorknobs. The surface was sampled with

a sterile cotton-tipped applicator wetted in 10 ml of 1/4 strength Ringer’s solution.

The area sampled varied between approximately 25-100 cm2, depending on the object

tested. After sampling, the swab was replaced in the 1/4-strength Ringer’s solution

and transported to the lab for bacterial assay (Kaltenthaler et al., 1995; Kyriacou

et al., 2009).

5.3.7 Microbiological Methods

Bacterial Assays

All hand and surface samples were assayed for three fecal indicator bacteria: fecal

coliform, enterococci and Escherchia coli. Membrane filtration was used to enumerate

the bacteria. Fecal coliform was grown on mFC agar (BD Diagnostics, Inc, USA)

at 44.5◦C according to the U.S. EPA standard methods for water quality testing


(Bordner et al., 1978). Enterococci was grown on mEI agar (BD Diagnostics, Inc,

USA) at 41.5◦ according to U.S. EPA Method 1600 (USEPA, 2002a). Escherchia coli

was grown on modified mTEC agar (BD Diagnostics, Inc, USA) according to U.S.

EPA Method 1603 (USEPA, 2002b) with incubation at 35◦ for two hours followed

by incubation at 44.5◦ for an additional 22 hours. For the hand rinse samples, a

volume of between 65-80 ml of the total 350 ml collected was filtered. For the surface

samples, a volume of between 2-2.5 ml of the total 10 ml collected was filtered. To

calculate bacterial concentration per two hands, the colony counts were multiplied

by the ratio of total sample volume collected (350 ml) to sample volume filtered for

each sample. For data analysis of hand rinse samples, the lower detection limit is 5.4

CFU per two hands, based on a 65 ml filtered sample volume. When no detectable

bacteria was present, 1/2 the lower limit of detection (2.7 CFU per two hands) was

used. For data analysis of surface samples, the lower detection limit is 5 CFU per

surface, based on a 2 ml filtered sample volume. Surfaces samples were classified as

either contaminated (≥5 CFU per 25-100 cm2 surface) or uncontaminated when no

bacteria were detectable.

Viral Assays

A subset of sixty-seven hand rinse samples were also tested for the presence of en-

terovirus, norovirus genogroup I, and norovirus genogroup II. The sample volume

remaining after bacterial assays, between 80-110 ml, was filtered through a 0.45um

negatively-charged nitrocellulose filter (HA filter, Millipore, Billerica, MA, USA),

placed in a 2 oz Whirl-pak bag (NASCO Corp., Fort Atkinson, WI) and stored at

-80◦. Both RNA and DNA were extracted using the Qiagen AllPrep DNA/RNA Mini

Kit (Qiagen, Valencia, CA, USA) and eluted in 60 µl of RNAse/DNAse free water.

RNAse/DNAse free water was used as an extraction blank with every set of ten sam-

ples extracted. 5 µl of template was then used in a 25 µl RT-PCR reaction, the details

of which are included in Table 5.2. RT-PCR reagents used were the Qiagen One-Step

RT-PCR kit (Qiagen, Valencia, CA, USA). Thermocycler conditions were obtained

from RT-PCR kit manufacturer’s recommendations using the annealing temperatures

listed in Table 5.2, and optimized on an Applied Biosystems Thermal Cycler 9700


(Applied Biosystems, Foster City, CA). All PCR products were visualized on 1.5%

agarose gels using a BioRad Gel Doc XR system (BioRad, Hercules, CA).

5.3.8 Statistics

Most statistics were performed using PASW Statistics 18.0.2. (SPSS: An IBM Com-

pany, Chicago, IL, USA). Statistical methods are reported with the results, with

additional details available in Appendix C. Analyses using generalized estimating

equations (GEEs) were performed using the “geeglm” function in the “geepack” pack-

age in R (version 2.11.1, R Foundation for Statistical Computing, Vienna, Austria)

(Zuur et al., 2009). Magnitude of the coefficients (β) and significance level (p) are

reported, where the significance level used throughout the study is α =0.05.

5.4 Results

5.4.1 Surveys

The population characteristics are presented in Table 5.3, including age of the par-

ent/guardian respondent, ethnicity of the child, and percent of children with a chronic

disease and taking medication. The only category in which the parents of children at-

tending Site A and Site B differed significantly (p = 0.02) was the number of residents

under 6 years old in their households. The parents of children at Site A had a mean

0.5 more residents under six years old than parents at Site B. Characteristics of hand

hygiene habits, general health of the child, and previous and predicted respiratory

and gastrointestinal illness are reported in Table 5.4.

5.4.2 Health Data

A total of 5,651 person-days of health data were collected over the duration of the

study. Attendance and symptomatic illness status were recorded for 5,619 (99.4%)

and 5,503 (97.4%) days, respectively. The days without recorded attendance or symp-

tomatic illness data were excluded from analysis.


Summaries of attendance and symptomatic illness for staff and children at each

site are presented in Table 5.5. In total, children and staff were absent on 636 (11.3%)

person-days. Symptomatic illness in children or staff was observed and reported by

child care center staff on 1063 person-days, or 19.3%) of the total 5,303 person-days

with recorded symptomatic illness status. On an additional 94 (1.7%) person-days,

children were absent due to illness with symptoms that were not specified by the

parents or guardians; these person-days were included in analyses of symptomatic

illness but treated as missing data in analyses of respiratory or gastrointestinal illness.

A time series of the absences and illness-related absences of children and staff at both

of the centers is shown in Figure 5.1. Similarly, times series of respiratory illness,

gastrointestinal illness, and new illness episodes are shown in Figure 5.2.

Of the 5,503 person-days with recorded symptomatic illness status, respiratory

symptoms were reported on 18.3% (or 1,010 person-days). The most common res-

piratory symptoms were runny nose (15.8% or 872 of the 5,503 person-days), cough

(4.8% or 265 person-days), and sore throat (0.9% or 52 person-days). Gastrointesti-

nal symptoms were reported on 38 (0.7%) person-days. The most common gastroin-

testinal symptoms were vomiting (0.3% or 18 person-days), stomach pain (0.3% or 18

person-days), and diarrhea (0.1% or 5 person-days). Fever was reported on 83 person-

days (1.5%), 46 (0.8%) person-days were in conjunction with respiratory symptoms,

12 (0.2%) person-days with gastrointestinal symptoms, and 25 (0.5%) person-days as

the only symptom.

A total of 232 new illness episodes were identified during the study. Of those, 118

(50.8%) new illness episodes were first identified and reported by child care center

staff as symptomatic illness on a day when the child or staff member was present. The

remaining 114 (49.1%) new illness episodes were first reported as an absenteeism due

to illness. The majority of the new illness episodes were respiratory (161 episodes, or

69.4%). The rest were gastrointestinal (15 episodes, or 6.4%), fever alone (14 episodes,

or 6.0%), or an illness-related absences with unspecified symptoms (43 episodes, or

18.5%). The duration of episodes ranged between 1 and 48 days, with a mean and

median of 6.7 and 3 days, respectively.

Rates of health outcomes varied by site, as compared using Pearson’s χ2. Site A


had significantly fewer absences (p = 0.02), illness-related absences (p = 0.001), and

respiratory illness (p < 0.001) than Site B. There was no significant difference by site

in gastrointestinal illness (p = 0.20) or new illness episodes (p = 0.82). Because of the

significant differences in absences, illness-related absences, and respiratory-illness, all

analyses of health outcomes using GEEs included a site-dependent variable.

5.4.3 Hand Rinse Samples

Of the 616 hand rinse samples collected, enterococci, fecal coliform, and E. coli were

detected in 208 (33%), 83 (14%), and 31 (5%), respectively. The range in concentra-

tions of bacteria on hands was the same for all three bacteria: 8–≥1000 CFU per 2

hands. A time series of the fraction of hand rinse samples with detectable enterococci

and fecal coliform is presented in Figure 5.3. Norovirus gI and gII were not detected

in any of the 67 hand rinse samples tested. Enterovirus was detected in four of the

67 hand rinse samples tested (6%).

Mean concentrations of all three fecal indicator bacteria were higher on the hands

with visible dirt, visible dirt under nails, and on volunteers with visible runny noses.

The concentrations of E. coli (p = 0.022), enterococci (p = 0.003), and fecal coliform

(p = 0.006) were significantly higher when dirt on hands was visible, with mean effect

sizes of 0.052, 0.113, and 0.141 log10 CFU per two hands, respectively. Only the

concentration of fecal coliform (p = 0.035), but not E. coli (p = 0.206) or enterococci

(p = 0.239), was significantly higher when dirt under nails was visible with a mean

effect size of 0.077 log10 CFU per two hands. A visible runny nose was associated with

significantly higher concentrations of enterococci (p = 0.001) and fecal coliform (p =

0.021), with mean effect sizes of 0.240 and 0.095 log10 CFU per two hands, respectively,

but not with concentrations of E. coli (p = 0.089). A volunteer’s response on how

s/he was feeling at the time of the sampling was not associated with the concentration

of E. coli (p = 0.459), enterococci (p = 0.100), or fecal coliform (p = 0.511) on the

hands. The low prevalence of enterovirus on hands precluded statistical analysis

with presentation of symptoms or presence of fecal indicator bacteria. Two of the

four children with detectable enterovirus also had reported symptomatic respiratory


illness.

5.4.4 Environmental Samples

In total, 299 environmental samples were collected. A summary of the objects tested

and the number of samples with detectable enterococci and fecal coliforms are pre-

sented in Table 5.1. Enterococci and fecal coliform were detected in 19 (6%) and

9 (3%) of the samples, respectively. E. coli were not detected in any of the sam-

ples. Concentrations on fomites for both enterococci and fecal coliform ranged from

≤5–≥1000 CFU per 100 cm2.

The presence of enterococci on a surface was significantly correlated to the pres-

ence of fecal coliform (McNemar χ2 test, p = 0.041). The presence of enterococci

or fecal coliform was not significantly associated with site (Pearson χ2 test, entero-

cocci p = 0.59, fecal coliform p = 0.82) or time of class (Pearson χ2 test, enterococci

p = 0.89, fecal coliform p = 0.79). Data on the fraction of fomites sampled in a

given week with detectable enterococci or fecal coliform were used in all time series

analyses, as presented in Figure 5.4.

5.4.5 Health Associations with Hand and Surface Contami-

nation

Fifteen separate GEE were used to examine the associations between respiratory

illness and microbial contamination on hands and surfaces for daily lags of up to

-7 through +7 days (Table 5.6). Data were clustered by child, and a variable was

included to control for site. Symptomatic respiratory illness is significantly and pos-

itively associated with hand contamination on the same day (β = 0.40, p = 0.003),

one day before (β = 0.31, p = 0.041)) and one day later (β = 0.41, p = 0.005), four

days before (β = 0.99, p =< 0.001), and seven days later (β = 0.41, p = 0.015). In

addition, there is a significant and positive association with environmental contam-

ination as measured four (β = 1.37, p = 0.42) and five (β = 0.79, p = 0.014) days

before as well as two (β = 0.69, p = 0.023) and three (β = 0.091, p = 0.030) days

later.


No significant associations between hand and environmental contamination with

either illness-related absences or new episodes of illness were found (See Tables C.1

and C.2). Gastrointestinal illness prevalence was low, and therefore insufficient to

analyze associations between gastroenteritis and microbial contamination in a manner

similar to respiratory illness.

5.5 Discussion

In child care centers, hygiene plays an important role in reducing transmission of

both gastrointestinal and respiratory illness. Previous field studies in child care cen-

ters have demonstrated significant correlations between microbial contamination and

adverse health outcomes (Van et al., 1991; Laborde et al., 1993), but this study is

the first to our knowledge to infer causality based on analysis of temporal trends.

As such, the study provides insight into the timing of microbial contamination rela-

tive to symptomatic illness. Specifically, increases in detectable enterococci on hands

and fomites precedes symptomatic respiratory illness by a four- to six- day period

consistent with incubation periods for respiratory diseases (Long et al., 1997). Fur-

thermore, the study demonstrates that the occurrence of enterococci on hands and

fomites increases in the two days following symptoms. These findings suggest that

respiratory illness can contribute to, and result from, microbial contamination on

hands and fomites.

The illness rates and microbial contamination in this study used to infer causal

relationships are consistent with previously observed values. The estimated rates for

total illness (0.76 per child per month) and respiratory illness (0.63 per child per

month) are similar to the rates reported for children of similar ages attending child

care (Wald et al., 1991; Dahl et al., 1991; Krilov et al., 1996). Microbial contami-

nation is also similar to previous studies. In classrooms with children from infancy

to under five years old, fecal coliform and fecal streptococci (a group of organisms of

which enterococci are a subset) were observed on 4-10% and 16% of fomites sampled

(Weniger et al., 1983; Holaday et al., 1990; Kyriacou et al., 2009), consistent and


expectedly greater than the 3% and 6% detection rates in the present study. Simi-

larly, our detection rates for fecal coliform (14%) and enterococci (33%) on hands are

consistent with reported detection rate of 6-20% for fecal coliform (Van et al., 1991;

Holaday et al., 1990), and 52.9% for fecal streptococci (Kyriacou et al., 2009).

Respiratory symptoms increase with enterococci occurrence on hands on the same

day, one day before, and one day after. Although other studies have demonstrated

significant correlations between bacteria on hands and health (Van et al., 1991; Pick-

ering et al., 2010), this is the first study to demonstrate significant associations with

daily lags. This finding suggests enterococci acts as a superior indicator for respira-

tory illness relative to both fecal coliform or E. coli. Enterococci, while commonly

isolated in feces, have also been isolated from the mouth (Murray, 1990) and the nose

(Crossley and Ross, 1985). In the present study, runny/stuffy nose or coughing were

reported as the majority of symptoms, providing a possible source of enterococci to

the environment. In support, individuals with visible runny noses had significantly

higher concentrations of enterococci on hands. Furthermore, (Pickering et al., 2011)

demonstrated, in Africa, significant associations between enterococci density on moth-

ers’ hands and time since last handwashing. The same relationship was not significant

for E. coli (Pickering et al., 2011).

Respiratory symptoms significantly lag enterococci occurrence on fomites two to

three days later. This finding is consistent with asymptomatic excretion of microor-

ganisms persisting after the conclusion of symptoms, as has been reported for res-

piratory viruses (Long et al., 1997). However, the median duration of symptomatic

illness is three days, so significant associations within three days of symptoms may

not necessarily imply associations occurred in the absence of symptoms. Similarly,

the majority of fomites with detectable enterococci were toys that were not cleaned

regularly by the nightly janitorial staff. Significant associations, therefore, may be

a result of enterococci persistence. Pinfold (1990) suggests that fecal streptococci, a

group of microorganisms that include enterococci, are more likely to survive cross-

contamination than E. coli because enterococci survival on fingertips is 4-8 times

longer (Pinfold, 1990). Nevertheless, the study suggests that hygiene interventions


targeting recovered individuals for up to three days following the conclusion of ob-

servable symptoms may reduce illness transmission.

Enterococci occurrence on fomites preceded increases in respiratory illness by four

to five days. As the incubation period for common respiratory viruses is 2-5 days

(Long et al., 1997), this finding is among the first field evidence of a causal role

of microbial contamination on fomites in respiratory disease transmission. As ente-

rococci may be shed with nasal secretions, the findings suggest that the increased

presence on fomites may be indicative of increased presence of pathogens responsible

for outbreaks, as well.

The detection of enterovirus on hand rinse samples demonstrates the potential role

of hands in pathogen transmission. The pan-enterovirus primers used in this study to

detect enterovirus are capable of detecting some serotypes of rhinovirus, a common

respiratory virus in child care centers (Rotbart, 1995). The low detection rate (6%

of hand rinse samples tested) is consistent with a previous study conducted in Africa

(Pickering et al., 2011). A higher detection rate may have been possible with a more

efficient virus recovery method. We compared direct extraction of sewage to the hand

rinse sample method by spiking hand rinse water with sewage and found an approx-

imate ten–fold increase in the lower limit of detection (data not shown). Accounting

for the ten–fold increase, PCR template volume, hand rinse sample volume, and a

PCR reaction lower limit of detection equal to the published 2.5 PFU of poliovirus

(Jaykus et al., 1996), the detection limit for virus is at least 1500 PFU poliovirus per

two hands. Efforts to reduce the detection limit may yield higher pathogen detection

rates.

The only health outcomes demonstrating significant associations with microbial

contamination were respiratory symptoms. The other health outcomes modeled,

illness–related absences and new illness episodes, did not demonstrate significant

trends when modeled as a function of enterococci contamination on surfaces (See

Appendix C). Modeling absences, including illness-related absences, as a function of

microbial contamination was confounded by the inability to collect data for children

who are not present during sampling trips. The lack of data on microbial contam-

ination on hands of absent children likely contributed to bias in the illness-related


absences model. New illness episodes are similarly impacted as absences accounted

for almost half (49%) of all defined illnesses. In fact, symptomatic respiratory illness

is well–suited to be modeled as a function of microbial contamination as shedding,

particularly during symptomatic illness, is perceived to be a cause of transmission

and contamination (Hall et al., 1980). For a further discussion of the health outcome

model used, specifically the use of multiple comparisons, refer to the Appendix C.

The present study suggests that respiratory illness both lags and leads increased

environmental surface and hand contamination. However, data collection methods

and statistical analysis used in the study may bias the findings. Symptomatic illness

in this study, as it was reported by child care center staff, is a subjective measure.

Evidence of the influence of subjectivity of the child care center staff includes the

significantly different respiratory incidence rates between Site A and Site B, despite

similarity between the two centers (See Table 5.3) and a similar number of new

episodes of illness (See Table 5.5). At the end of the study the child care center

staff were prompted on the recording frequency, and the resulting quality of the child

health charts. Between the choices of “highly”, “somewhat”, and “not accurate”,

the child care center staff reported that the quality of the data was “somewhat ac-

curate” and that the charts were filled out daily. Identification of health outcomes,

specifically the presentation of symptoms, by trained professionals combined with

collection and analysis of clinical samples for specific etiological agents would likely

improve reliability of health measurements. Similarly, health data were only collected

on weekdays. Therefore, samples collected on days where the associated lag time cor-

responded to a weekend were not included in the analysis. However, the frequency

of illness relative to the number of observations for every lag remained consistent

(See Table 5.6) suggesting bias may be minimized. Finally, as the study covered only

four months, seasonal trends in illness may be confounded with seasonal trends in

microbial contamination.

Future studies investigating the relationship between fomites and respiratory ill-

ness could incorporate indicator bacteria with specificity to nasal secretions or saliva

by identifying organisms from, for example, recent microbiota studies (Frank et al.,

2010). The bacteria used as indicators of microbial contamination in this study are


typically used as indicators of fecal contamination, and are therefore more typically

associated with gastrointestinal illness than with respiratory illness. However, the

significant findings of our results suggest that presence of enterococci may be an

appropriate predictor of respiratory symptoms.

5.6 Acknowledgments

The authors acknowledge Lauren Sassoubre, Isaias Espinoza, and Elfego Felix for their

assistance on site and in the laboratory, as well as Todd Russell, Thienan Nguyen, and

Francisco Tamayo for their assistance in the laboratory. The Boehm Research Group

provided helpful suggestions for study design and data analysis. The authors also

acknowledge Gojo Industries, Inc, for providing Purell Alcohol Based Hand Sanitizer

to the participating child care centers. Gojo Industries was not otherwise involved in

the study. The research has been funded, in part, by the UPS Foundation Endowment

Fund at Stanford University and the United States Environmental Protection Agency

(EPA) under the Science to Achieve Results (STAR) Graduate Fellowship Program.

EPA has not officially endorsed this publication and the views expressed herein may

not reflect the views of the EPA.


5.7 Tables


Surface Tested Enterococci Fecal Coliformball 7 - -block 11 - -book 4 - -chair 10 - -computer 5 - -doorknob 17 - -faucet 21 1 (5%) 2 (10%)floor 3 1 (33%) -glue container 1 - -kitchen surfaces 3 - -marker 5 1 (20%) -mirror 1 - -playground 29 4 (14%) -sandbox 10 2 (20%) 1 (10%)shelf 3 - -soap dispenser 1 - -storage bin 4 - -table 59 1 (2%) 1 (2%)toilet 6 - -toothbrush 1 - -toy 92 9 (10%) 5 (5%)tray 1 - -water table 5 - -Total 299 19 (6%) 9 (3%)

Table 5.1: Summary of environmental fomites sampled along with the number andcorresponding percent of samples with detectable (≥5 CFU per 100cm2) enterococciand fecal coliform. No E. coli were detected on fomites


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Characteristics Site A Site B p− valueNumber of parents or guardians interviewed

28 34Response rate (interviewed / enrolled)

Age of parent or guardian respondent (years)Mean 30 29 0.91

Range 22-44 22-48Self-reported ethnicity (%)

Hispanic 86 97 0.28African American 7 0

Other 7 3Type of family residence %)

Single Family 43 44 0.77Duplex 21 15

Apt. Complex 36 41Mean no. residents in child’s household

Total 5.2 4.9 0.45Under 6 yo 2.2 1.6 0.02

Children from families with pets (%)21 38 0.24

Children with chronic disease (%)Asthma 18 15 0.97

Other 7 9Total 25 24 0.84

Children on any medications (%) 32 15 0.2

Table 5.3: Child Care Center Population Demographics


Characteristic Site A Site B

BaselineNo. gastrointestinal illness in past 6 mo. (%)

0 89 561 4 26

2 or more 7 9No. respiratory illness in past 6 mo. (%)

0 32 31 32 21

2 or more 36 74

Healthy child index (mean)8.24 8.04

Handwashing rate per day (mean)Parent 7.80 7.40Child 6.80 6.60

Likelihood of child illness in next month (%)High 4 6Some 71 56None 21 24

Don’t Know 18 3

Follow-upHealthy child index (mean)

7.86 7.03Handwashing rate per day (mean)

Parent 9.40 11.10Child 11.20 7.30

Likelihood of child illness in next month (%)High 4 0Some 48 26None 32 59

Don’t Know 16 15Child was ill during study (%)

Gastrointestinal 16 0Respiratory 44 44

Table 5.4: Child Care Center Population Health and Hygiene Knowledge


Site A Site B

Category Children Staff Children Staff Total

n = 37 n = 3 n = 33 n = 3-5Total No. Person-Days 2560 229 2618 212 5619

AbsencesTotal 281 7 339 9 636Illness-Related 135 2 196 2 335

IllnessTotal Illness∗ 368 57 714 31 1170Symptomatic Illness 306 57 669 31 1063

RespiratoryTotal 288 57 635 30 1010

GastrointestinalTotal 14 1 22 1 38

New EpisodesTotal 106 7 113 6 232Respiratory 67 7 82 5 161Gastrointestinal 5 0 9 0 14Fever Only 5 0 9 0 14Unspecified 31 0 13 0 44

Table 5.5: Number of person-days with recorded attendance and symptomatic illnesssubset by child care facility and site. ∗Total Illness is the combination of recordedsymptomatic illness and illness-related absenteeism with unspecified symptoms.


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-0.0

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72

274

53-2.60

0.39

<0.001

0.34

0.2

20.1

12

0.69

0.30

0.023

1.22

0.40

0.002

0.0

40.0

51

443

89-2.52

0.33

<0.001

0.41

0.15

0.005

0.3

40.1

90.0

69

1.16

0.38

0.002

0.1

80.0

70

534

101

-2.10

0.30

<0.001

0.40

0.14

0.003

0.2

70.1

70.1

22

0.5

30.3

30.1

02

0.1

60.0

5-1

446

72-2.11

0.33

<0.001

0.31

0.15

0.041

0.2

80.1

70.1

04

0.2

10.3

80.5

87

0.1

90.0

7-2

338

53-2.27

0.34

<0.001

0.15

0.1

80.3

93

0.2

30.2

20.3

00

0.73

0.39

0.0

58

0.1

10.0

5-3

223

33-2.29

0.37

<0.001

0.38

0.2

40.1

07

0.1

50.3

30.6

49

0.3

90.4

70.4

14

0.0

80.0

4-4

184

38-2.63

0.38

<0.001

0.99

0.22

<0.001

1.37

0.42

0.001

0.5

50.4

60.2

35

0.1

00.1

0-5

223

59-2.22

0.42

<0.001

0.32

0.2

00.1

16

0.79

0.32

0.014

1.31

0.43

0.002

0.1

20.0

7-6

359

77-2.01

0.33

<0.001

0.19

0.1

60.2

50

0.2

50.2

20.2

57

0.81

0.37

0.029

0.1

60.0

6-7

420

82-1.99

0.30

<0.001

0.00

0.1

60.9

87

0.0

10.2

30.9

56

1.0

00.3

40.0

03

0.1

10.0

4

Tab

le5.

6:P

aram

eter

sof

gener

aliz

edes

tim

atin

geq

uat

ion

for

resp

irat

ory

illn

ess

asfu

nct

ion

ofen

tero

cocc

ion

han

ds

and

fom

ites

.“L

ag”

isth

enum

ber

ofday

sb

etw

een

collec

tion

ofhea

lth

dat

aan

dth

eco

llec

tion

ofdat

afo

rco

nta

min

atio

non

surf

aces

wher

ea

pos

itiv

ela

gim

plies

that

hea

lth

outc

omes

pre

ceded

mic

robia

lco

nta

min

atio

n.

“Obs.

”is

the

num

ber

ofob

serv

atio

ns

wit

hb

oth

hea

lth

mea

sure

men

tsan

dm

icro

bia

lco

nta

min

atio

ndat

a.“I

ll.”

isth

enum

ber

ofob

serv

atio

ns

when

resp

irat

ory

illn

ess

was

obse

rved

,“H

ands”

isth

enum

ber

ofen

tero

cocc

idet

ecte

don

han

ds,

“Fom

ites

”is

the

frac

tion

offo

mit

essa

mple

dw

ith

det

ecta

ble

ente

roco

cci,

“Sit

e”is

the

faci

lity

,w

ith

the

coeffi

cien

tsre

pre

senti

ng

the

diff

eren

ceif

orSit

eB

rela

tive

toSit

eA

,“C

oef

.”an

d“S

E”

are

the

coeffi

cien

tan

dst

andar

der

ror

ofth

eco

rrel

atio

nco

effici

ent.

“Pr(>|W|)”

isth

esi

gnifi

cance

,w

ith

valu

esle

ssth

an0.

05co

nsi

der

edsi

gnifi

cant

and

hig

hligh

ted

inb

old.


5.8 Figures


0.0

0.2

0.4

0.6

0.8

1.0

Children - Site B (n = 33)Children - Site A (n = 37)

Absences(a)

2-Feb

25-Feb

21-Mar

14-Apr

8-May

1-Jun

Prop

ortio

n A

bsen

t Per

Day

0.0

0.2

0.4

0.6

0.8

Illness-Related Absences(b)2-Feb

25-Feb

21-Mar

14-Apr

8-May

1-Jun

Prop

ortio

n A

bsen

t due

to Il

lnes

s Pe

r Day

1.0

Sta� - Site B (n = 3-5)Sta� - Site A (n = 3)



Figure 5.1: Time series of the proportion of children and staff who (a) are absent, and(b) are absent due to illness. The shaded portion of the figures represents five days(April 13–April 17) when no child care classes were held and no data were collected


0.0

0.2

0.4

0.6

0.8

1.0

Respiratory Symptoms(a)

2-Feb

25-Feb

21-Mar

14-Apr

8-May

1-Jun

Prop

ortio

n w

ith R

espi

rato

ry S

ympt

oms

Per D

ay

0.0

0.2

0.4

0.6

0.8

Gastrointestinal Symptoms(b)

2-Feb

25-Feb

21-Mar

14-Apr

8-May

1-Jun

Prop

ortio

n w

ith G

astr

oint

estin

al S

ympt

oms

Per D

ay

0

2

4

6

8

10

12New Illness Episodes(c)

2-Feb

25-Feb

21-Mar

14-Apr

8-May

1-Jun

No.

New

Illn

ess

Epis

odes

Per

Day







Figure 5.2: Time series of the proportion of children and staff who (a) have respiratorysymptoms, and (b) have gastrointestinal symptoms. Also presented is a time series ofthe first day of (c) new illness episodes. The shaded portion of the figures representsfive days (April 13–April 17) when no child care classes were held and no data werecollected


0.0

0.2

0.4

0.6

0.8

1.0

enterococci - AM

fecal coliform - AMenterococci - PM

Hand Contamination at Site A(a)

2-Feb

25-Feb

21-Mar

14-Apr

8-May

1-Jun

Prop

ortio

n of

Han

ds w

ith D

etec

tabl

e Ba

cter

ia

fecal coliform - PM

Under Limit of Detection

enterovirus neg / pos

0.0

0.2

0.4

0.6

0.8

1.0Hand Contamination at Site B(b)

2-Feb

25-Feb

21-Mar

14-Apr

8-May

1-Jun


enterococci - AM


fecal coliform - PMenterovirus neg / pos

Prop

ortio

n of

Han

ds w

ith D

etec

tabl

e Ba

cter

ia

Figure 5.3: Time series of the proportion of hand samples with detectable bacteriaat (a) Site A and (b) Site B. The shaded portion of the graph represents five days(April 13–April 17) when no child care classes were held and no data were collected.Sampling visits when no samples had bacterial densities above the lower limit ofdetection on hands (≥ 5 CFU per two hands) are marked by columns with heightsequal to the line marked “Limit of Detection”. Samples tested for enterovirus areunder the abscissa corresponding to the sample’s date. Each ◦ represents a negativesample and • represents a positive sample


0.0

0.2

0.4

0.6

0.8

1.0

enterococci - AM


Environmental Contamination at Site A(a)

2-Feb

25-Feb

21-Mar

14-Apr

8-May

1-Jun

Prop

ortio

n of

Sur

face

s w

ith D

etec

tabl

e Ba

cter

ia

fecal coliform - PM


0.0

0.2

0.4

0.6

0.8

1.0Environmental Contamination at Site B(b)

2-Feb

25-Feb

21-Mar

14-Apr

8-May

1-Jun


enterococci - AM


fecal coliform - PM

Prop

ortio

n of

Sur

face

s w

ith D

etec

tabl

e Ba

cter

ia

Figure 5.4: Time series of the proportion of fomites sampled with detectable bacteriaon hands at (a) Site A and (b) Site B, and on fomites at (a) Site A and (b) SiteB. The shaded portion of the graph represents five days (April 13–April 17) whenno child care classes were held and no data were collected. Sampling visits whenno samples had bacterial densities above the lower limit of detection on fomites (≥2.5 CFU per 25 cm2) are marked by columns with heights equal to the line marked“Limit of Detection”.

Chapter 6

Conclusions and Future Directions

6.1 Conclusions

Conclusion 1: Virus transfers readily between surfaces

Chapter 2 investigates the fraction of virus that transfers between a fingerpad

and a glass surface. From the study, we demonstrate that the mean, median, and

standard deviation of the fraction of virus transferred between a fingerpad and glass

surface is 0.23, 0.18, and 0.22, respectively. These findings are of similar order of

magnitude as findings in previous literature on virus transfer between nonporous

surfaces and fingerpads (Ansari et al., 1991; Mbithi et al., 1992; Rusin et al., 2002).

The findings demonstrate that the amount of virus transferred, on a single contact,

to the fingerpad from a contaminated fomite is at a similar order of magnitude as

the original level of contamination. Chapter 2 also demonstrated that specific factors

investigated (e.g., hand washing, direction of transfer, and virus species) significantly

influenced the fraction transferred. However, the small effect size (5-10% of total

fraction transferred) of the factors on viral transfer suggests the factors likely have

little impact on infection risk from fomites.

Conclusion 2: Density of microorganisms on surfaces is both increased

by, and leads to, adverse health outcomes

126

CHAPTER 6. CONCLUSIONS 127

A major finding of this dissertation is evidence of a causal link between density of

microorganisms on surfaces and risk of infection. Chapter 3 demonstrates that in a

model of child-fomite interaction, the concentration of virus on the child’s previously

uncontaminated hands will equal the concentration on the fomite within minutes due

to frequent, repetitive hand-surface contacts (Figure 3.3). At that time, hand-mouth

contacts will contribute to ingested dose, and therefore risk of illness. This will occur

even if the fomite is removed. In the model, the initial concentration on the hands

is directly linked to the likelihood of infection. As Table 3.2 demonstrates, a tenfold

increase in the concentration of virus on the model fomite increased the child’s dose

one hundred fold, even after only 10 minutes of child-fomite interaction. Therefore,

the model suggests that high concentrations of virus on surfaces are indicative of

increased risk of illness.

If microbial contamination on surfaces are indicative of increased risk of illness,

it is likely that a field scale study investigating temporal trends in contamination

and health outcomes would demonstrate associations. Multiple previous studies have

suggested that a significant correlation exists between microbial contamination and

health (Van et al., 1991; Butz et al., 1993; Laborde et al., 1994). However, these

studies have shown correlations based on sampling fomites only once, or based on

multiple samplings that all occur after outbreaks. Without temporal sampling during

both outbreak and non-outbreak periods, causation between microbial contamination

and illness can not be inferred. Rather, increases in microbial contamination need

to precede increases in disease burden to demonstrate causation, as suggested in

Chapter 3. As there is a lag between dose and response for gastrointestinal and

respiratory viral disease of between 12 and 120 hours (See Table 1.1), increases in

illness due to microbial contamination need to be tracked with daily resolution. This

was the motivation for Chapter 5, the field-based study investigating temporal trends

in contamination and health.

Chapter 5, demonstrates that increases in microbial contamination lead to in-

creases in adverse health outcomes. Using enterococci as an indicator of bacterial

contamination, respiratory disease is significantly and positively associated with con-

tamination four and five days prior to illness. The lag of four or five days is consistent


with incubation periods (typically between 1-4 days) for respiratory illness. Not only

does the work in Chapter 5 suggest that microbial contamination contributes to res-

piratory disease burden, but it also demonstrates that microbial contamination is

caused by respiratory disease burden. Specifically, enterococci on the hands is sig-

nificantly associated with symptomatic illness on the same day as symptoms, as well

as on both the day before and the day after. This finding holds not only for the

symptoms reported by the child care center staff, but also for the symptom of visible

runny nose as reported by the research team during hand sample collection.

Conclusion 3: Virus sampling methods on fomites should be standardized

Evidence that microbial contamination contributes to respiratory illness supports

the need for standardized fomite sampling (Chapter 4). In the study in child care

centers (Chapter 5), enterococci was used as an indicator of microbial contamination.

The next research step is to sample hands and fomites for etiological agents in addition

to indicators. Significance of an association between etiological agents on surfaces and

increased illness (and accounting for the three to five day incubation period) would

provide stronger evidence of the role of fomites in respiratory disease transmission

in child care centers. Indicators such as enterococci are more easier to detect in

the environment than pathogens. Therefore, etiological agents may be detectable on

fewer than the 6% of samples with detectable enterococci identified in Chapter 5.

To improve detection of virus on surfaces, Chapter 4 suggests an effective sampling

method. The use of polyester-tipped swabs in conjunction with 1/4 strength Ringer’s

(hereafter referred to as “Ringer’s”) or saline solution resulted in significantly in-

creased detection of infective virus relative to other methods tested. Using MS2

bacteriophage as a model virus, polyester-tipped swab with Ringer’s recovered 60%

more than the mean total virus recovered using other methods tested in the study.

The literature review included in the chapter also demonstrated that polyester-tipped

swabs were significantly associated with higher fraction of samples with detectable

virus.

Use of a standardized method will also allow cross comparisons of fomite-sampling

studies. In the 45 studies identified in Chapter 4 that sampled surfaces for pathogenic


virus, the authors used 12 different implements and 4 different eluents. As implement

choice significantly influences recovery of virus from surfaces, comparison of outcomes

across studies is difficult unless the same sampling method is used. Standardizing the

sampling method, specifically through use of the polyester-tipped swabs in Ringer’s

or saline solution, would reduce this bias. Alternatively, quantifying the lower limit

of detection for the assay, or quantifying the virus detected on surfaces, would allow

cross comparison of studies.

6.2 Future Directions

My dissertation explored the role of fomites in disease transmission. I anticipate

my future research will build upon this knowledge, expand to incorporate additional

transmission routes, and continue investigating additional environmental reservoirs of

infectious disease. The goal of my future research will be to contribute to a holistic

understanding of human-environment interactions in infectious disease transmission.

In this section I describe several areas of future research following on my dissertation

work that will lead to new discoveries concerning the role of fomites in communicable

infectious disease.

Linking physicochemical properties of etiological agents to survival and

transmission

The movement and fate of virus through the environment via fomites may be influ-

enced by virus physicochemical properties. In support, Chapter 2 demonstrates that

virus species significantly influences virus transfer to and from fomites. Similarly, the

work by Abad et al. (1994) demonstrates that virus species also influence virus per-

sistence on fomites. The physicochemical properties of virus, therefore, are influential

in both virus transfer between surfaces and virus persistence. This is consistent with

work demonstrating the importance of physicochemical properties of virus movement

and fate in the subsurface Dowd et al. (1998).


In Chapter 2, the characteristics that differed between the three bacteriophage

tested are the isoelectric point and hydrophobicity. Both characteristics have been

demonstrated to influence transport through the subsurface (Shields and Farrah,

2002). Unfortunately, as transfer of only three viruses was investigated, no trends

between fraction transferred and either isoelectric point or hydrophobicity were elu-

cidated. In the estimate of the inactivation rate for MS2 bacteriophage, discussed in

Chapter 3, only one virus was studied and therefore no conclusions relating physic-

ochemical properties to persistence can be drawn. Therefore, questions remain con-

cerning the cause of the difference in transfer between the viruses and whether or

not the difference would be applicable to transfer of animal virus. Similar questions

concerning whether or not physicochemical properties of virus influence viral persis-

tence on surfaces, and whether or not the differences in either persistence or transfer

contribute to increased efficacy in fomite-mediated transmission.

Incorporating secondary transmission into quantitative microbial risk

assessments of fomite-mediated transmission

Chapter 3 models the risk of infection for a single individual interacting with a

contaminated fomite. The model is among the first to incorporate complex human-

fomite interactions, including sporadic, sequential contact events, into a quantitative

microbial risk assessment. However, the model only focuses on half of the fomite-

mediated transmission route (steps 2-4 of Figure 1.2). The model ignores the steps

leading to contamination of the fomite. Therefore, questions remain concerning the

influence of shedding of virus to fomites on resulting infectious disease transmission,

and the role of fomites in secondary infections (person-to-person spread).

Agent-based modeling provides a framework for modeling secondary transmission

rates due to indirect contact. Recently, infectious disease modeling has incorporated

environmental reservoirs into compartmental modeling (Li et al., 2009; Stilianakis

and Drossinos, 2010). In compartmental modeling, rate parameters drive movement

of individuals between compartments. In this manner, compartmental modeling as-

sumes homogeneity and perfect mixing within compartments, as well rates of transfer


between compartments (Rahmandad and Sterman, 2008). Fomite-mediated transmis-

sion, however, is an inherently spatial phenomenon: infection from a fomite can not

occur unless both an infected individual and a susceptible individual contact the same

fomite in that order. Therefore, an alternative method, such as agent-based modeling,

might prove more useful in understanding fomite-mediated transmission. Agent based

modeling allows for heterogeneity across individuals, as well as among the network

of their interactions (Rahmandad and Sterman, 2008). Defining both individuals as

agents capable of moving within predefined ranges, and fomites as stationary agents,

a framework for agent-based modeling of infectious disease transmission is suggested.

The work of Chapter 3 lays the groundwork for agent-based modeling of fomite-

mediated transmission. The child modeled in Chapter 3 is provided a defined set of

parameters (e.g., frequency and sequence of fomite contacts, likelihood of infection

given a dose). Similarly, the fomite parameters are predefined (e.g., inactivation rate

of virus, fraction virus transferred on contact). Replicating those agents, and defining

additional parameters (e.g., frequency of contacts between agents, shedding of agents

to others), would be among the first steps toward development of an agent-based

model. Using a representative closed system, such as a nursing home, office, or child

care center, could then provide an opportunity to validate the results by comparing

predicted outbreak patterns to documented patterns, as seen in (Bartlett III et al.,

1988; Iizuka, 2006). Once fomite-mediated transmission is modeled, characteristics of

fomite-mediated transmission could be explored. Additionally, interventions could be

implemented in the model to characterize likelihood of success in laboratory or field

settings.

Relative contribution of transmission routes to total respiratory and

gastrointestinal disease burden

Chapter 5 identified a significant association between respiratory illness and mi-

crobial contamination on surfaces. The daily resolution of health data provided an

opportunity to investigate causal links between illness and fomites. However, no

data were collected on direct contact, common vehicle, or airborne transmission.


Although there is a significant association between health outcomes and microbial

contamination, the proportion of total respiratory illness attributable to indirect con-

tact transmission is unknown. Therefore, the question of the relative contribution of

transmission routes, and fomite-mediated transmission in particular, remains. Efforts

to develop a systematic, evidence-based approach, to understanding relative contribu-

tions of transmission routes will contribute to development of interventions to reduce

overall disease burden.

Based on the sampling method of the work in Chapter 5, a multi-route expo-

sure study could be performed. Airborne and common vehicle transmission could be

monitored by incorporating personal air monitoring and replicate food/water diets.

Simultaneously, data on subjects’ behaviors (e.g., contacts with other subjects, move-

ment within the facility, contact with surfaces) could be collected via third-person or

videographic observations (Ferguson et al., 2006). An individual’s likelihood of ill-

ness could then be modeled as a function of both their behaviors and the presence

of etiological agents in the environment. The modeling could provide insight into the

relative contributions of transmission routes to disease burden.

Estimating the contribution of heterogeneous fomite use to variability in

infection risk

Current sampling protocol for estimating virus contamination on fomites relies on

subjective sampling choice. In Chapter 4, we identified over 40 unique publications

investigating virus contamination on fomites. In the publications, as in the fomites

sampled in Chapter 5, the fomite choice for sampling was subjectively chosen by the

research staff. No publication included a sampling protocol that identified the fomites

that should be sampled prior to the study.

However a fomite’s contribution to disease transmission is likely a function of its

use as well as the presence of microbial contamination. For example, in Chapter 5

we modeled the interaction of a child with a contaminated toy ball. If the same

child was in a room of multiple toys, and only one or a couple were contaminated, the

child’s choice would increase the variability in the likelihood of infection. By choosing


to interact with an uncontaminated toy, the child would reduce his risk of infection

to zero. Additional evidence is provided by Jiang et al. (1998), who suggested that

surfaces likely to be contacted by children were more likely contaminated with a DNA

marker seeded into a child care center.

Understanding the heterogeneity of fomite use would improve understanding of

fomite-mediated tranmsission. Fomite use could be identified through, for example,

sensors or videographic techniques. The data gleaned could then be incorporated in

sample choice for future fomite contamination studies, as well as in the interpretation

of results. Additionally, identification of highly used fomites could be used to tailor

environmental hygiene interventions.

Extension of models to nosocomial bacterial infections

Fomite-mediated transmission is an important route of nosocomial bacterial in-

fections, and future research should extend the presented work to investigate trans-

mission of bacterial infections in hospitals. The focus of the dissertation is on in-

door transmission of viral respiratory and gastrointestinal disease. Motivation for

the focus is provided in Chapter 1, and includes the notion that viral infections,

unlike bacterial infections, can not readily be treated with antibiotics. Antibacterial-

resistant bacteria, however, are more frequently responsible for nosocomial infections.

The most common examples, methicillin-resistant Staphylococcus aureus (MRSA) and

vancomycin-resistant enterococci (VRE), are believed to be readily transmitted via

fomites. Evidence of detection and persistence of MRSA and VRE on surfaces sup-

ports the likelihood of fomite-mediated transmission .

There are both clinical and economic incentives for curbing nosocomial bacterial

infections. Approximately 1.7 million hospital acquired infections occurred in the U.S.

hospitals alone in 2002 (Klevens et al., 2007). This comes at an estimated cost of $700-

$2000 per case (Graves et al., 2008). Reductions in total healthcare expenditures,

therefore, may be achieved through increased infection control targeted to effectively

interrupting transmission routes (Graves et al., 2008). Future work extending the

dissertation to understanding transmission of hospital-acquired infections may aid in

the design and implementation of interventions for infection control.

Appendix A

Supplemental Material for Chapter

3: Equations Used in

Discrete-Time Model

Equations used to represent fomes-mouth contacts:

Change in concentration on both hands:

CH(tc) = CH(tc−1)e(−kh∆t) (A.1)

Change in concentration on fomes:

CF (tc) = (1− TEFMSF )CF (tc−1)e(−kf∆t) (A.2)

Increase in dose:

DOSE = TEFMSFAFCF (tc−1)e(−kf∆t) (A.3)

134

APPENDIX A. SUPPLEMENTAL MATERIAL FOR CHAPTER 3 135

Equations used to represent hand-fomes contacts:

Change in concentration on hand in contact with fomes:

CH(tc) = CH(tc−1)e(−kh∆t) − TEFHSH(CH(tc−1)e

(−kh∆t) − CF (tc−1)e(−kf∆t)) (A.4)

Change in concentration on hand not in contact with fomes:



CF (tc) = CF (tc−1)e(−kf∆t) − TEFHSH

AHAF

(CF (tc−1)e(−kf∆t) − CH(tc−1)e

(−kh∆t)) (A.6)

Increase in dose:

Dose = 0 (A.7)

Equations used to represent hand-mouth contacts:

Change in concentration on hand in contact with mouth:

CH(tc) = (1− TEHMSM)CH(tc−1)e(−kh∆t) (A.8)

Change in concentration on hand not in contact with mouth:



CF (tc) = CF (tc−1)e(−kf∆t) (A.10)

Increase in dose:

DOSE = TEHMSMAHCH(tc−1)e(−kh∆t) (A.11)

APPENDIX A. SUPPLEMENTAL MATERIAL FOR CHAPTER 3 136

Variables

tc = time of the current contacttc−1 = time of the previous contact

∆t = tc - tc−1 = time between successive contactsCH = concentration of virus on surface of hand, virus/cm2

CF = concentration of virus on surface of fomes, virus/cm2

kh = inactivation rate of virus on hand, s−1, base ekf = inactivation rate of virus on fomes, s−1, base e

TEFM = fraction of virus transferred from fomes to mouthTEFH = fraction of virus transferred between fomes and handTEHM = fraction of virus transferred from hand to mouth

SF = fraction of surface area of fomes in contact with mouthSH = fraction of surface area of hand in contact with fomesSM = fraction of surface area of hand in contact with mouthAF = surface area of fomes, cm2

AH = surface area of hand, cm2

DOSE = number of viral particles ingested

Appendix B


4: Virus Recovery from Fomites

B.1 Tables

137

APPENDIX B. SUPPLEMENTAL MATERIAL FOR CHAPTER 4 138

Fir

stA

uth

or

Yea

rV

iru

sIl

lA

ssay

Imp

lem

Elu

ent

Su

rface

sN

o.

Pos.

Tota

lF

rac

Pos

LO

DL

oca

le

Akhte

r1995

Rh

ino-

Yes

EIA

Sw

ab

TP

B+

Anti

toilet

han

dle

s,

tele

vis

ion

s,

toys,

vit

al

sign

chart

12

146

0.0

8N

oH

osp

ital

Infl

uen

zaY

escu

ltu

reS

wab

TP

B+

Anti

N/

A0

146

<0.0

1N

oH

osp

ital

Ad

eno-

Yes

cult

ure

Sw

ab

TP

B+

Anti

N/

A0

146

<0.0

1N

oH

osp

ital

Ente

ro-

Yes

cult

ure

Sw

ab

TP

B+

Anti

N/

A0

146

<0.0

1N

oH

osp

ital

Ad

eno-

Yes

EIA

Sw

ab

TP

B+

Anti

N/

A0

146

<0.0

1N

oH

osp

ital

Asa

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ab

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via

tion

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Tab

leB

.2.


Amies Amies mediumAnti AntibioticsBE Beef extractBHIB Brain heart infusion brothBSA Bovine serum albuminCulture Cell cultureDBH Dot blot hybridizationDCC Day care centerEIA Enzyme immunoassayEluent Eluent type usedHAV Hepatitis A virusHBV Hepatitis B virusHCV Hepatitis C virusHIV Human immunodeficiency virushnPCR Hemi-nested PCRHPV Human papillomavirusIll Clinically infected individual was presentImplem Implement type usedLB Letheen brothLOD Lower limit of detection is reported in articleLTC Long Term CareMCV Molluscum contagiosum virusMEM Minimal essential mediumnPCR / nRTPCR Nested PCR / RTPCRN/R Not reported by authorPBS Phosphate buffered salinePCR Polymerase chain reactionqPCR / qRTPCR quantitative PCR / RTPCRRinger’s 1/4 strength Ringer’s solutionRSV Respiratory syncytial virusRT-PCR Reverse transcription PCRSARS-CoV Severe acute respiratory syndrome - corona virusSurfaces Type of surfaces with detectable targetSwab Unreported type of swabTotal Total number of surfaces sampledTPB Tryptose phosphate brothVTM Viral transport mediumVZV Varicella zoster virus

Table B.2: The abbreviations, and corresponding definitions, used in Table B.1 andTable B.3.


Virus No. Studies Samples Collected No. Pos. Frac. Pos.

Adeno- 6 1006 203 .202Astro- 5 453 22 .049Ebola 2 56 1 .018Entero- 2 594 3 .005HAV 1 30 2 .067HBV 3 284 15 .053HCV 5 269 14 .052HIV 1 30 3 .100HPV 2 202 73 .361Influenza 7 546 151 .277MC 1 9 8 .889Nipah 1 468 11 .024Noro- 14 1019 204 .200Orthopox- 1 25 8 .320Picorna- 1 52 10 .192Rhino- 3 297 135 .455Rota- 11 957 161 .168RSV 1 17 0 .000SARS-CoV 4 365 34 .093VZV 3 125 47 .376

Total 74 6804 1105 .162

Table B.3: A summary of the articles included in the analysis using the abbreviationsprovided in Table B.2

Appendix C


5: Fomites and Health in Child

Care Centers

C.1 Methods

C.1.1 Statistics

Most statistics were performed using PASW Statistics 18.0.2. (SPSS: An IBM Com-

pany, Chicago, IL, USA). A significance level of α <0.05 was used throughout the

study.

Survey.

The results of the surveys were compared across two sites using a Z-test for two pro-

portions for percentage data (pet ownership, medication, and chronic illness), Pear-

son’s χ2 for categorical data (ethnicity, type of household), and Mann-Whitney U test

or Kruskal-Wallis one-way analysis of variance for two or more independent samples

of ordinal data( Changes in parent and child handwashing rates and healthy child in-

dex between the baseline and follow-up surveys were examined using a matched-pair

t-test.

150

APPENDIX C. SUPPLEMENTAL MATERIAL FOR CHAPTER 5 151

Microbial Contamination.

Significance of self-reported health on microbial contamination was determined using

Kruskal-Wallis one-way analysis of variance. Mann-Whitney U tests were used to

assess significance of associations of hand contamination with visible signs (dirt on

hands, dirt under fingernails, and runny nose), time of class (morning or afternoon),

site (A or B), location of sampling (indoor or outdoor). Mann-Whitney U tests were

also used to assess significance of associations of environmental contamination with

time of class (morning or afternoon), site (A or B), and location of sampling (indoor

or outdoor).

Bivariate Correlations

Bivariate Spearman rank correlations were used to investigate correlations between

environmental contamination (weekly fraction of samples with /geq5 CFU entero-

cocci or fecal coliform), hand contamination (daily and weekly fraction of samples

with /geq5.4 CFU enterococci or fecal coliform per two hands), and health (daily and

weekly respiratory illness, gastrointestinal illness, absences, illness-related absences,

and number of unique illness episodes). Results from the bivariate correlations were

then used to identify fecal indicator bacteria (e.g., enterococci, fecal coliform, or E.

coli) and health outcome (e.g. respiratory illness, gastrointestinal illness, absences,

illness-related absences or unique illness episodes) to include in model of health out-

come as a function of microbial contamination.

Health Outcome as a Function of Contamination

To investigate associations between surface contamination and health, health out-

comes were modeled as functions of the density of enterococci on hands, the presence

/ absence of enterococci on at least one sampled environmental surface, and site.

Preliminary bivariate correlations suggest enterococci is a more appropriate indicator

of hand and surface contamination then fecal coliform and E. coli, so enterococci was

used as the dependent variable. Inter - individual correlation in the longitudinal data

was accounted for using generalized estimating equations (GEE) with a logit link


function clustered on individual St Sauver et al. (1998). We assumed data correla-

tion was independent in time, and use a compound correlation structure. The GEE

analysis was performed using the “geeglm” function in the “geepack” package in R

(version 2.11.1, R Foundation for Statistical Computing, Vienna, Austria). To in-

fer casual links between surface (hand and environmental) contamination and health

(where respiratory illness has an incubation time of 2-5 days Long et al. (1997)), we

used separate GEEs to model health outcomes as a function of microbial contamina-

tion at daily lags of up to plus and minus seven days. Only the subset of data with

measured health outcomes and corresponding microbial contamination data at the

specified lag was included (i.e., missing values were removed from analysis). Health

outcomes explored included illness-related absences, respiratory illness, and onset of

unique illness episodes. The low prevalence of gastrointestinal illness during the study

(see Results) precluded analysis of gastroenteritis.

C.2 Results

C.2.1 Bivariate Correlations

C.2.2 Hand Contamination and Health Data.

Correlations between hand contamination and health data were performed by ag-

gregating each individual’s data over the duration of the study and investigating

associations between the fraction of total days individuals experienced an adverse

health outcome to the mean density of bacteria on their hands over the duration of

the study. The sample size was, therefore, seventy-seven, equal to the number of

individuals who were both enrolled in the study and assented to at least one hand

sample.

Enterococci on hands was significantly correlated to respiratory illness (ρs = 0.276,

p = 0.015), but not gastrointestinal illness(ρs = 0.034, p = 0.771), absences (ρs =

−0.047, p = 0.688), illness-related absences (ρs = 0.046, p = 0.693), or number of

unique illnesses (ρs = −0.093, p = 0.422). Neither the density of fecal coliform nor E.

coli on hands were significantly correlated to any health outcomes. Specifically, fecal


coliform was not significantly correlated to respiratory illness (ρs = 0.128, p = 0.267),

gastrointestinal illness(ρs = 0.148, p = 0.199), absences (ρs = −0.041, p = 0.726),

illness-related absences (ρs = −0.022, p = 0.850), or number of unique illnesses

(ρs = −0.031, p = 0.786). Similarly, E. coli was not significantly correlated to

respiratory illness (ρs = 0.122, p = 0.290), gastrointestinal illness(ρs = −0.056,

p = 0.626), absences (ρs = 0.034, p = 0.769), illness-related absences (ρs = 0.127,

p = 0.270), or number of unique illnesses (ρs = −0.140, p = 0.224).

C.2.3 Hand Contamination and Environmental Contamina-

tion.

The fraction of environmental fomites with detectable enterococci on a given day was

not correlated with corresponding fraction of hand samples with detectable entero-

cocci (ρs = 0.206, p = 0.108). Similarly, there was no significant correlation for fecal

coliform (ρs = 0.069, p = 0.591).

C.2.4 Environmental Contamination and Health Data.

The fraction of samples with detectable enterococci at each facility is significantly

correlated with the fraction of individuals with respiratory illness symptoms during

the same week (ρs = 0.297, p = 0.018), but is not significantly correlated with

gastrointestinal illness (ρs = 0.157, p = 0.218), absences (ρs = −0.151, p = 0.237),

illness-related absences (ρs = 0.039, p = 0.761), or number of unique illness episodes

(ρs = 0.126, p = 0.326). There are no significant correlations for fecal coliform and

respiratory illness, (ρs = −0.023, p = 0.859), gastrointestinal illness, (ρs = 0.045,

p = 0.728), absences (ρs = 0.066, p = 0.606), illness-related absences (ρs = 0.088,

p = 0.490), or number of unique illness episodes (ρs = 0.170, p = 0.184).


C.2.5 Health Associations with Hand and Surface Contami-

nation

Using generalized estimating equations to model illness-related absences as a function

of hand and environmental contamination, while controlling for site and incorporating

daily lags did not elucidate clear trends. Illness-related absences is significantly asso-

ciated with hand and environmental contamination only sporadically (See Table C.2)

and is likely a result of false positive detection. Specifically, illness-related absences

is negatively associated with hand contamination as measured five days previously

(β = −0.958, p = 0.039) and positively associated with environmental surface con-

tamination as measured three (β = 2.34, p < 0.001) and seven days later (β = 0.546,

p = 0.021).

New episodes of illness as a health outcome was also significantly associated with

hand and environmental contamination and hand hygiene only sporadically (See Table

C.1). False positive detection likely explains significant associations. Specifically, new

episodes of illness were positively associated with hand contamination as measured on

the same day (β = 0.613, p = 0.019) and with environmental surface contamination

as measured two days later (β = 1.49, p = 0.0136).

C.3 Discussion

C.3.1 Use of Multiple Comparisons

The use of fifteen GEEs to model health outcome at daily lags requires use of multiple

comparisons. The expected number of false positives is two, as estimated for fifteen

models with two variables (excluding intercept and adjustment for site) and a signifi-

cance threshold of 0.05. In the model of respiratory illness, 9 of the 30 total variables

are significant. The likelihood of a false positivity rate of 8 or more variables, relying

on the assumption that all tests are independent, is less than 0.001% Storey (2003).

Therefore, most of the 9 significant variables are likely true positives. The clustering

of significant associations around specific lags (e.g. hands are significantly associated

with respiratory illness on -1,0, and +1 days, fomites on -4, and -5 days as well as +2


and +3 days) provides evidence that the findings are likely true. Random significant

associations, such as hands at a lag of +7 days are more likely false. Conversely, the

likelihood of 3 or more significant associations out of 30 significant tests, as observed

in the two models for new episodes of illness and illness-related absences is approx-

imately 19%, suggesting that the majority of significant findings for the two models

are false positives.


C.4 Tables.


Inte

rcep

tH

an

ds

Fom

ites

Sit

eC

orr

elati

on

Lag

Ob

s.Il

l.C

oef

.S

EP

r(>|W|)

Coef

.S

EP

r(>|W|)

Coef

.S

EP

r(>|W|)

Coef

.S

EP

r(>|W|)

Est

imate

SE

743

918

-3.01

0.32

<0.001

-0.3

00.3

30.3

61

0.6

60.4

20.1

20

-0.5

80.5

60.3

07

0.0

00.0

46

395

10-2.89

0.40

<0.001

0.10

0.3

20.7

47

-1.6

11.1

20.1

51

-1.9

71.1

00.0

75

0.0

00.0

25

328

8-4.41

0.68

<0.001

0.28

0.4

10.4

84

0.2

30.7

00.7

36

0.7

40.6

90.2

84

-0.0

20.0

54

214

7-2.79

0.50

<0.001

-0.3

20.3

80.4

02

0.6

20.8

60.4

68

-42.98

0.42

<0.001

-0.0

80.1

33

204

10-4.24

0.66

<0.001

0.53

0.4

70.2

55

1.11

0.57

0.050

1.0

00.6

90.1

47

0.0

30.0

62

284

9-4.75

1.02

<0.001

-0.6

00.6

70.3

72

0.5

80.5

20.2

67

1.9

71.1

00.0

73

0.0

00.0

31

455

19-3.34

0.45

<0.001

-0.3

30.4

00.4

09

0.0

20.4

50.9

66

0.6

60.5

50.2

26

0.0

30.0

40

572

14-4.03

0.50

<0.001

0.62

0.25

0.014

0.5

40.4

80.2

58

-0.9

90.6

30.1

17

0.0

00.0

1-1

472

13-3.03

0.45

<0.001

0.11

0.4

00.7

84

-1.7

10.9

40.0

71

-0.7

00.5

40.2

00

-0.0

20.0

4-2

365

11-2.84

0.49

<0.001

-0.8

00.6

10.1

87

-0.1

10.5

50.8

45

-0.5

50.6

30.3

81

0.0

00.0

2-3

233

8-3.39

0.51

<0.001

-0.0

80.4

20.8

50

-0.1

20.6

00.8

37

0.3

20.8

10.6

93

0.0

30.0

6-4

193

15-3.14

0.47

<0.001

0.48

0.3

10.1

18

-0.1

60.7

60.8

35

0.7

20.5

60.2

04

-0.0

60.0

7-5

231

13-2.72

0.42

<0.001

-0.7

30.3

90.0

60

0.1

90.7

60.8

00

0.3

70.5

90.5

37

-0.0

30.0

7-6

370

18-2.71

0.39

<0.001

-0.7

10.4

40.1

04

0.4

70.4

30.2

80

-0.1

50.4

80.7

56

-0.0

20.0

3-7

440

15-3.71

0.55

<0.001

0.01

0.4

10.9

90

-0.2

00.6

60.7

66

0.7

00.6

10.2

48

0.0

30.0

6

Tab

leC

.1:

New

epis

odes

ofilln

ess

model

par

amet

ers

for

gener

aliz

edes

tim

atin

geq

uat

ion

asfu

nct

ion

ofen

tero

cocc

ion

han

ds

and

ente

roco

cci

onsu

rfac

esw

hile

contr

olling

for

site

.L

ags

ofup

toplu

san

dm

inus

seve

nday

sb

etw

een

illn

ess

and

conta

min

atio

nar

em

odel

led

separ

atel

y.“L

ag”

isth

enum

ber

ofday

sb

etw

een

collec

tion

ofhea

lth

dat

aan

dth

eco

llec

tion

ofdat

afo

rco

nta

min

atio

non

han

ds,

and

fom

ites

,so

ap

osit

ive

lag

implies

that

hea

lth

outc

omes

pre

ceed

edm

icro

bia

lco

nta

min

atio

nuse

and

neg

ativ

ela

gsim

ply

that

hea

lth

dat

asu

ccee

ded

mic

robia

lco

nta

min

atio

n.

“Obs.

”re

fers

toth

enum

ber

ofob

serv

atio

ns

wit

hb

oth

hea

lth

mea

sure

men

tsan

dm

icro

bia

lco

nta

min

atio

ndat

aat

the

spec

ified

lag.

”Ill.”

refe

rsto

the

num

ber

ofob

serv

atio

ns

inw

hic

hnew

epis

odes

wer

eob

serv

ed,“

Han

ds”

isth

enum

ber

ofen

tero

cocc

idet

ecte

don

anin

div

idual

’shan

ds,

“Fom

ites

”is

the

frac

tion

ofsu

rfac

essa

mple

dw

ith

det

ecta

ble

ente

roco

cci,

“Sit

e”is

the

faci

lity

,w

ith

the

coeffi

cien

tsre

pre

senta

tive

ofth

ediff

eren

cein

the

model

for

Sit

eB

rela

tive

toSit

eA

,“C

oef

.”is

the

coeffi

cien

ton

the

vari

able

,“S

E”

isth

est

andar

der

ror

ofth

eco

effice

nt,

and

“Pr(>|W|)”

isth

esi

gnifi

cance

ofth

eco

effici

ent,

wit

hva

lues

less

than

0.05

consi

der

edsi

gnifi

cant

and

hig

hligh

ted

inb

old.


Inte

rcep

tH

an

ds

Surf

ace

sS

ite

Corr

elati

on

Lag

Ob

s.Il

l.C

oef

.S

EP

r(>|W|)

Coef

.S

EP

r(>|W|)

Coef

.S

EP

r(>|W|)

Coef

.S

EP

r(>|W|)

Est

imate

SE

743

431

-2.89

0.37

<0.001

-0.1

30.2

90.6

64

0.53

0.24

0.023

0.3

70.4

40.4

01

0.0

50.0

46

391

19-2.64

0.39

<0.001

0.0

80.3

50.8

11

-0.5

30.4

60.2

49

-0.5

80.5

10.2

54

-0.0

20.0

35

326

20-3.05

0.40

<0.001

-0.0

50.3

20.8

76

0.3

30.4

10.4

11

0.4

50.4

00.2

55

-0.0

70.0

44

212

7-2.99

0.62

<0.001

-0.6

40.5

40.2

37

0.4

70.7

40.5

23

-0.8

10.7

80.2

96

0.0

60.2

43

203

13-3.15

0.57

<0.001

-0.6

20.5

20.2

39

2.17

0.56

<0.001

-0.1

50.5

80.7

92

-0.0

30.1

02

283

17-4.05

0.67

<0.001

0.2

90.3

40.3

98

0.4

30.3

10.1

71

1.44

0.63

0.022

-0.0

10.0

61

454

12-4.03

0.58

<0.001

-0.0

30.3

60.9

29

0.2

80.4

70.5

58

0.5

80.6

20.3

56

-0.0

10.0

30

--

--

--

--

--

--

--

--

-146

920

-3.06

0.42

<0.001

0.3

90.2

60.1

33

-0.4

10.3

50.2

41

-0.5

40.5

90.3

53

0.0

90.1

3-2

361

18-2.64

0.43

<0.001

-0.9

30.5

10.0

68

-0.8

60.5

90.1

42

0.5

90.5

00.2

35

-0.0

10.0

5-3

228

9-3.73

0.84

<0.001

0.4

70.3

90.2

28

-0.2

60.5

40.6

32

0.6

50.7

80.4

06

0.1

00.3

4-4

193

12-2.39

0.40

<0.001

-0.3

60.4

80.4

52

0.8

30.6

90.2

30

-0.7

90.5

80.1

73

-0.0

40.0

6-5

231

17-3.27

0.56

<0.001

-0.59

0.29

0.043

0.0

90.5

50.8

71

1.49

0.66

0.024

0.0

10.0

3-6

368

27-2.86

0.33

<0.001

0.3

10.2

20.1

52

0.4

50.3

50.1

98

-0.1

30.3

40.7

04

-0.0

70.0

5-7

438

25-3.23

0.42

<0.001

-0.1

20.3

90.7

58

0.3

40.4

10.4

13

0.6

00.4

00.1

36

-0.0

20.0

2

Tab

leC

.2:

Illn

ess-

rela

ted

abse

nce

sm

odel

par

amet

ers

for

gener

aliz

edes

tim

atin

geq

uat

ion

asfu

nct

ion

ofen

tero

cocc

ion

han

ds

and

ente

roco

cci

onsu

rfac

es,

while

contr

olling

for

site

.L

ags

ofup

toplu

san

dm

inus

seve

nday

sb

etw

een

illn

ess

and

conta

min

atio

nar

em

odel

led

separ

atel

y.“L

ag”

isth

enum

ber

ofday

sb

etw

een

collec

tion

ofhea

lth

dat

aan

dth

eco

llec

tion

ofdat

afo

rco

nta

min

atio

non

han

ds

and

fom

ites

,so

ap

osit

ive

lag

implies

that

hea

lth

outc

omes

pre

ceed

edm

icro

bia

lco

nta

min

atio

nan

dneg

ativ

ela

gsim

ply

that

hea

lth

dat

asu

ccee

ded

mic

robia

lco

nta

min

atio

n.

“Obs.

”re

fers

toth

enum

ber

ofob

serv

atio

ns

wit

hb

oth

hea

lth

mea

sure

men

tsan

dm

icro

bia

lco

nta

min

atio

ndat

aat

the

spec

ified

lag.

”Ill.”

refe

rsto

the

num

ber

ofob

serv

atio

ns

inw

hic

hilln

ess-

abse

nce

sw

asob

serv

ed,“

Han

ds”

isth

enum

ber

ofen

tero

cocc

idet

ecte

don

anin

div

idual

’shan

ds,

“Fom

ites

”is

the

frac

tion

ofsu

rfac

essa

mple

dw

ith

det

ecta

ble

ente

roco

cci,

“Sit

e”is

the

faci

lity

,w

ith

the

coeffi

cien

tsre

pre

senta

tive

ofth

ediff

eren

cein

the

model

for

Sit

eB

rela

tive

toSit

eA

,“C

oef

.”is

the

coeffi

cien

ton

the

vari

able

,“S

E”

isth

est

andar

der

ror

ofth

eco

effice

nt,

and

“Pr(>|W|)”

isth

esi

gnifi

cance

ofth

eco

effici

ent,

wit

hva

lues

less

than

0.05

consi

der

edsi

gnifi

cant

and

hig

hligh

ted

inb

old.

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FOMITES IN INFECTIOUS DISEASE TRANSMISSION: A MODELING ...cf347cn1097/... · the dissertation investigates virus transfer between surfaces and virus recovery from surfaces, models