New Frontiers, Old ObstaclesPathogens, Genetics, Models, and Public Health

J. Glenn Morris, Jr., MD, MPH&TMEmerging Pathogens InstituteUniversity of Florida

UF Emerging Pathogens Institute

Created with $60 million appropriation from Florida state legislature, focusing on human, animal, and plant pathogens

Over 200 faculty members, from 9 UF colleges (including medicine, public health, veterinary medicine, and agriculture)

Strong global emphasis, reflecting Florida’s sub-tropical location

How do you guide optimal allocation of limited public

health resources for prevention of diarrheal disease?

Why do kids in the developing world get symptomatic

diarrhea? Driven by specific pathogens, but with

occurrence of illness dependent on a complex set of interactions that include: Prior exposure to the pathogen

/vaccination (immunity) Inoculum size Nutritional status Intestinal microenvironment, driven by

local exposures

Lindsey et al, EID 2011;17:608-611

Sample of 2,748 patients with diarrheal disease in Kolkata; samples screened for 26 pathogens using standard microbiological techniques

Approximately 1/3 had multiple pathogens

Likelihood of infection with another specific pathogen among

patients culture-positive for V. cholerae

Global Enterics Multi-Center Study (GEMS)(Levine et al, funded by Gates Foundation) 3609 samples from children with and without

diarrhea, collected at 7 sites in Africa and Southern Asia

Samples screened for pathogens using standard microbiological techniques

Sub-Study: Comprehensive genetic analysis/ identification of all microorganisms in samples Data to date: analysis of 16S sequence data from

samples from 1007 cases (514 children with diarrhea, 493 control children)

Average of 3,900 sequences per sample

Bacterial Identification and Classification by 16S rRNA Permits

screening of “total community DNA,” and identification of all “OTU” in sample Stool samples Environmenta

l samples

Percentage of Case and Control Samples with Specific Pathogens, as Identified by Genetic vs. Microbiologic Techniques

Aerom

onas

Campy

lobact

er

C. diffi

cle

Salm

onell

a

Shige

lla0.00%

5.00%

10.00%

15.00%

20.00%

25.00%

30.00%

35.00%

Case GeneticControl GeneticCase MicroControl Micro

Underlying intestinal flora did not differ by case/control status (although further analysis does suggest that risk can be influenced by flora composition)

Patterns of intestinal flora (and pathogen distribution) did differ by country

Blue – Bangladesh

Green - Kenya

Black - Gambia

Red Control

Black Case

So does pathogen really matter? Yes: may be striking differences in

public health impact, depending on pathogen

Report released: April 28,

2011

Funded by: Robert Wood

Johnson Foundation

Available: epi.ufl.edu

U.S. Foodborne Pathogen Incidence*

PathogenIllness

es

Rank

(ill) HospsRank

(hosp)Death

s

Rank (death

)

Norovirus5,461,7

31 1 14,663 2 149 4

Salmonella1,027,5

61 2 19,336 1 378 1C. perfringens 965,958 3 438 10 26 8Campylobacter 845,024 4 8,463 3 76 5Shigella spp. 131,254 6 1,456 6 10 10E. coli STEC non-O157 112,752 7 271 12 0 20Yersinia enterocolitica 97,656 8 533 9 29 7Toxoplasma gondii 86,686 9 4,428 4 327 2E. coli 0157:H7 63,153 12 2,138 5 20 9Cryptosporidium spp. 57,616 13 210 14 4 15V. parahaemolyticus 34,664 14 100 16 4 16Vibrio spp., other 17,564 16 83 21 8 12Cyclo. cayetanensis 11,407 21 11 27 0 20Listeria mono. 1,591 24 1,455 7 255 3Vibrio vulnificus 96 28 93 18 36 6

*Scallan et al, EID 2011;17:7-15

So does pathogen really matter? Yes: may be striking differences in

public health impact, depending on pathogen

Yes: transmission pathways (and optimal prevention strategies) may differ dramatically, depending on pathogen

Cholera Transmission Pathways

“Slow” transmission: through fecal contamination of environment/water sources

“Fast” transmission: driven by genetically-induced hyperinfectious state, occurring within a time window of a few hours after passage of stool. Transmission generally occurs within household or immediate environment of patient

Cholera infections in

humans

V. cholerae in environment

including plankton

Environmental Parameters

Zimbabwe Spatial Models SIR model

Calculation of R0 Average number of secondary

infections that occur when one infective is introduced into a completely susceptible host population

Estimation of relative contributions of: human/human transmission (short

cycle, increased infectivity) vs. human/environment/human (long

cycle, decreased infectivity) Use of these estimates to assess

utility of intervention strategies such as vaccination

  ℛ0 95 % CI

Harare 1.52 (1.14-1.96)

Bulawayo 1.36 (1.12-1.61)

Mashonaland Central 1.38 (1.21-1.54)

Mashonaland East 1.11 (0.90-1.32)

Mashonaland West 1.87 (1.34-2.38)

Midlands 1.39 (1.23-1.56)

Manicaland 2.06 (1.78-2.34)

Matebeleland South 2.72 (1.19-4.24)

Matebeleland North 1.72 (1.44-1.99)

Masvingo 1.61 (1.20-2.03)

Zimbabwe 1.15 (1.08-1.23)

R0 by ProvinceZimbabwe Cholera Epidemic, 2008-9

Mukandavire et al, PNAS 2011;108:8767-72

Mapping ℛ0 values: Haiti Cholera Epidemic, 2010

Relative Contribution of “Slow” (Environmental) vs. “Fast” (Human) Sources to Cholera Transmission Zimbabwe

RE (slow cycle) = 0.20 (17%)

RH (fast cycle) = 0.95 (83% R0 = 1.15 Vaccine coverage to stop

epidemic: 17% Haiti

RE (slow cycle) = 0.84 (54%)

RH (fast cycle) = 0.70 (46%)

R0 = 1.54 Vaccination coverage to

stop epidemic: 45%

How do you guide optimal allocation of limited public health resources

for prevention of diarrheal disease? Systems are complex

Mix of pathogens Mix of factors driving occurrence of

symptomatic infection (disease) in individual patients

Varying outcomes dependent on pathogen Mix of transmission routes, varying by

pathogen, country, and region Variety of potential interventions,

including water systems, sanitation, improved protection of water and food in households, vaccination…..

How do you guide optimal allocation of limited public health resources

for prevention of diarrheal disease?

Need for geographically-targeted, data-driven risk analysis, to define optimal approaches to disease prevention