Developments in microbiological risk assessment for drinking water

A REVIEW

Developments in microbiological risk assessmentfor drinking water

P. GaleWRc-NSF Ltd, Medmenham, Marlow, Buckinghamshire, UK

793/02/01: received 21 February 2001, revised 6 April 2001 and accepted 27 April 2001

1. SUMMARY

This paper considers the development of microbiological

risk assessment models for pathogenic agents in drinking

water with particular reference to Cryptosporidium parvum,

rotavirus and bovine spongiform encephalopathy (BSE).

The available evidence suggests that there is potential for

considerable variation in exposures to C. parvum oocysts

through drinking water, during both outbreak and non-

outbreak conditions. This spatial/temporal heterogeneity

arises both from variation in oocyst densities in the raw

water and ¯uctuations in the removal ef®ciencies of drinking

water treatment. In terms of risk prediction, modelling the

variation in doses ingested by individual drinking water

consumers is not important if the dose±response curve is

linear and the oocysts act independently during infection.

Indeed, the total pathogen loading on the population as

represented by the arithmetic mean exposure is suf®cient for

risk prediction for C. parvum, BSE and other agents of low

infectivity, providing the infecting particles (i.e. oocysts or

BSE prions) are known to act independently. However, for

more highly infectious agents, such as rotavirus, ignoring

1. Summary, 191

2. Introduction, 192

2.1 Overview of the risk assessment approach, 192

3. Pathogen exposures through drinking water, 192

3.1 Pathogen exposures under non-outbreak condi-

tions, 193

3.1.1 Variation in micro-organism counts within

large volume samples, 193

3.1.2 Effect of treatment on the spatial distribu-

tion of micro-organisms, 193

3.1.3 Implication for Cryptosporidium exposures to

drinking water consumers under non-out-

break conditions, 195

3.2 A model for Cryptosporidium oocyst concentrations

in drinking water during an outbreak, 196

4. Dose±response curves. Estimating the risk to humans

from ingesting low pathogen doses, 197

4.1 Cryptosporidium parvum, 197

4.1.1 Experiments with salmonellas in mice suggest

that pathogens act independently and do not

co-operate during infection, 198

4.1.2 Acquired protective immunity for Cryptospo-ridium parvum, 199

4.1.3 Virulence of different strains of Cryptospori-dium parvum, 199

4.2 Human rotavirus, 199

4.3 Bovine spongiform encephalopathy, 200

4.3.1 The human oral ID50, 200

4.3.2 Evidence that bovine spongiform encephal-

opathy prions may act co-operatively, 200

4.3.3 Estimating the risks from ingestion of minute

subfractions of an ID50 through drinking

water, 200

4.4 The need for more dose±response data for water-

borne pathogens, 201

5. Integrating pathogen exposures and

dose±response curves, 201

5.1 Risk prediction for an outbreak of cryptosporidio-

sis, 201

5.2 Risk prediction for rotavirus, 202

5.3 Risk prediction for highly infectious pathogen

(ID50 � 1 micro-organism), 202

6. Risk prediction for bovine spongiform encephalopa-

thy, 202

7. Discussion, 202

8. References, 204

Correspondence: Dr P. Gale, WRc-NSF Ltd, Henley Road, Medmenham,

Marlow, Buckinghamshire SL7 2HD, UK (e-mail: [email protected]).

ã 2001 The Society for Applied Microbiology

Journal of Applied Microbiology 2001, 91, 191±205

the variation and just using the arithmetic mean exposure

may over-estimate the risk by a factor of about threefold. If

it were to be shown that pathogens co-operate with each

other during initiation of infection, such that the dose±

response relationship is non-linear, then modelling the

variation in doses ingested by individual consumers would

be very important. Possible mechanisms for co-operation of

pathogens during infection are considered. Simulations

show that acquired protective immunity for C. parvumreduces the risk of infection during outbreak conditions by

over 10-fold. Variation in virulence between strains of

C. parvum is a further source of uncertainty.

2. INTRODUCTION

Pathogens can and do gain entry into drinking water

supplies even in well-developed countries. Break-through

during treatment and ingress through cracked pipes are

well-documented causes of waterborne outbreaks of crypto-

sporidiosis (Craun et al. 1998) and Escherichia coli O157

(Swerdlow et al. 1992), respectively. Microbiological risk

assessment (MRA) is the emerging method to predict the

risks to public health from those pathogens. Models have

been developed for a range of waterborne pathogens in

drinking water, including C. parvum (Teunis et al. 1997),

Giardia (Regli et al. 1991) and enteric viruses (Haas et al.1993). Most of the models represent non-outbreak condi-

tions and model the endemic levels of infection through

drinking water. Recently, a model for C. parvum has been

developed for conditions representative of a waterborne

outbreak of cryptosporidiosis (Gale and Stan®eld 2000).

The risk assessment approach has been used as a guide for

microbial standards (Rose and Gerba 1991). It could also be

used to answer questions such as how many more people

will be infected if part of the drinking water treatment fails

and by how much will public health be jeopardized if

disinfection is eliminated. One application of MRA is to

provide a defensive position for new and emerging agents in

the absence of epidemiological evidence. For this reason, a

risk assessment for the transmission of BSE to humans

through drinking water was developed (Gale et al. 1998).

This MRA is different to those for well-documented

waterborne pathogens in the sense that there is no epide-

miological evidence to identify the routes of transmission of

BSE from humans to cattle, although consumption of

infected bovine offals, prior to their use being banned in

human food in November 1989, is the most likely route. The

link between BSE in cattle and the 88 cases (to 28 December

2000) of variant Creutzfeldt±Jakob disease (vCJD) in

humans has been con®rmed (Bruce et al. 1997), although

no data are available for the infectivity of BSE-infected

bovine tissues in humans. The primary objectives of the

MRA for BSE were to determine whether the BSE agent in

the environment could conceivably present a risk to those

drinking water consumers supplied by water sources near to

rendering plants and to determine whether drinking water

supplies were adequately protected. The objective was not to

assign precise and quantitative risks of infection.

2.1 Overview of the risk assessment approach

Environmental risk assessment models are based on the

source±pathway±receptor approach. Of key importance is

the identi®cation of the protective barriers. There are two

types of barrier; the pathway barriers control how much

infectivity the receptors are exposed to and the biomedical

barriers control how infectious the agent is to the receptors

(Gale et al. 2000). Examples of pathway barriers include

drinking water treatment processes and hydrogeological

substrata above aquifers. Examples of biomedical barriers

include the species barrier in BSE (Gale et al. 1998),

acquired protective immunity in C. parvum infection

(Chappell et al. 1999) and natural gut microbiota in

Salmonella infection (Meynell 1963). There will be some

uncertainty in the quality of the data available for the source,

pathway and receptor terms. In addition there will be

natural variation. In this paper, the natural variation in

pathogen exposures through drinking water is ®rst reviewed

and then sources of uncertainty and variation in dose±

response curves are considered. Monte Carlo simulations are

then used to demonstrate how integrating different dose±

response curves with various pathogen exposure models

in¯uences the predicted risks.

3. PATHOGEN EXPOSURES THROUGHDRINKING WATER

Densities (or counts) of micro-organisms, including veget-

ative bacteria (coliforms and plate counts), spores of spore-

forming bacteria and pathogens, typically show considerable

variation in drinking water supplies (Pipes et al. 1977;

Christian and Pipes 1983; Maul et al. 1985; Richardson

et al. 1991; Craun et al. 1998). This variation can be spatial,

re¯ecting differences not only between different water

supply zones but also within different parts of the same

zone. For example, ingress of sewage-contaminated water

through a cracked pipe will result in locally high pathogen

concentrations. Just such an event was responsible for the

waterborne outbreak of E. coli O157 in Cabool, MO, USA

(Swerdlow et al. 1992) which resulted in 243 cases of

diarrhoea, 32 hospitalizations and four deaths. The variation

in densities can also be temporal, re¯ecting seasonal changes

in pathogen loadings in the environment. Day-to-day

changes in pathogen loads in the raw waters and ¯uctuations

in the ef®ciency of removal by drinking water treatment

in¯uence the spatial/temporal variation in pathogen densi-

192 P. GALE

ã 2001 The Society for Applied Microbiology, Journal of Applied Microbiology, 91, 191±205

ties within the supply (LeChevallier and Norton 1995; Gale

et al. 1997; Teunis et al. 1997; Gale and Stan®eld 2000).

The MRA models for drinking water are developing by

de®ning the variation in daily pathogen exposures at the

level of the individual drinking water consumer (Teunis

et al. 1997; Gale 1998a). This variation is described here

with reference to exposure to Cryptosporidium oocysts

through drinking water supplies under both non-outbreak

and outbreak conditions.

3.1 Pathogen exposures under non-outbreakconditions

Pathogen counts are typically measured in large volume

(100±1000 l) samples taken from drinking water supplies.

Current routine methods for the enumeration of Cryptospo-ridium in water are not only inef®cient but also do not

provide an indication of which species has been recovered

nor of the viability or infectivity of the oocysts. Not

surprisingly, most 100±1000 l samples record zero patho-

gens under non-outbreak conditions (Payment et al. 1985;

LeChevallier et al. 1991). Indeed, LeChevallier et al. (1991)

reported 73% of 100-l volumes from ®ltered drinking water

supplies to contain zero oocysts of Cryptosporidium. How-

ever, even during non-outbreak conditions, small numbers

of Cryptosporidium oocysts do break through drinking water

treatment (coagulation/®ltration) and pass into the supply.

This is because no drinking water treatment process is 100%

ef®cient even when operated correctly. Indeed, some

drinking water samples contain relatively high pathogen

counts. Thus, LeChevallier et al. (1991) reported a maxi-

mum of 48 oocysts in a 100-l volume of ®ltered water. In the

UK, oocyst densities of up to 286 per 100 l have been

reported in drinking water where no associated illness or

outbreak was detected in the community (Craun et al. 1998).

The oocysts in those instances may have included dead

oocysts and species not infectious to humans.

It is apparent that oocyst densities in drinking water

supplies during non-outbreak conditions show large vari-

ation. While most samples contain zero oocysts, a few

contain high counts (LeChevallier et al. 1991). This re¯ects

both spatial/temporal variations within different parts of the

same supply and systematic differences between different

supplies (e.g. due to different raw water loadings/treatment

plant designs). Roseberry and Burmaster (1992) reported

that 95% of persons in the 20 to < 65 years age group in the

USA ingest between 430 ml and 2á9 l tap water person±1 d±1.

Gale (1996) reported that 11% of the tap water imbibed

in the UK is not boiled and calculated that 95% of

persons ingest between 47 and 322 ml unboiled tap water

person±1 d±1. To determine what doses individual drinking

water consumers ingest in single exposures, information is

needed on how pathogens are distributed within those high-

count, large-volume samples at the resolution of volumes

(unboiled) ingested daily by individual consumers, i.e. �100 ml±2 l.

3.1.1 Variation in micro-organism counts within largevolume samples. Consider a 100-l volume of drinking

water with 50 Cryptosporidium oocysts. Current MRA

models for drinking water assume that each consumer

ingests 2 l d±1 (Haas et al. 1993). Three scenarios could be

envisaged with 50 people each drinking 2 l of that 100 l

volume:

i oocysts are under-dispersed relative to the Poisson

distribution and each person ingests just one oocyst;

ii oocysts are Poisson-dispersed and most people ingest

between zero and four oocysts and

iii oocysts are over-dispersed relative to the Poisson

distribution and, in the extreme, one person ingests all

50 oocysts and the other 49 people are not exposed.

Attachment of pathogens to particulates could produce

the extreme scenario (iii), although there is no direct

evidence for this to date. The available evidence suggests

that the effect may be somewhere between scenarios (ii) and

(iii). Thus, Pipes et al. (1977) analysed 100-ml samples from

the same well-stirred 10-l volume of tap water and observed

a considerable range of counts. Surprisingly, some samples

had zero coliforms while others contained over 20 coliforms.

The counts did not ®t the Poisson distribution which

predicted no zeros and no samples with more than about 12

coliforms. The data suggested the presence of `coliform-

poor' and `coliform-rich' regions within the same 10-l

volume of water.

3.1.2 Effect of treatment on the spatial distribution ofmicro-organisms. Gale et al. (1997) presented evidence

that this effect was promoted in the case of spores of

aerobic spore-forming bacteria by the process of drinking

water treatment. The treatment process studied was alum-

coagulation followed by rapid gravity sand ®ltration at an

operational drinking water treatment works. The overall

conclusion was that drinking water treatment not only

removed 94±98% of the spores but also promoted the

spatial association of the remaining spores. Thus the spore

counts were, in general, Poisson-distributed within large

volumes (100 l) of raw water. In contrast, the counts of

aerobic spores measured in ®ltered water volumes showed

much greater variation in magnitude. This is shown in

Fig. 1 for aerobic spore counts measured within a 500-l

volume of water from a well-ripened sand ®lter. While the

mean count was 93 per 100 ml, some four samples had

counts of 300 per 100 ml or more. Those counts from the

®ltered water could be modelled by the negative binomial

distribution. Indeed, Fig. 2 shows the same counts plotted

in Fig. 1 plotted as a cumulative frequency distribution. It

DRINKING WATER RISK ASSESSMENT 193


is apparent that the Poisson distribution does not ®t the

count data.

Further data from studies undertaken at pilot scale at the

WRc drinking water treatment plant (using ferric coagula-

tion/clari®cation followed by rapid sand ®ltration) suggested

a more complicated picture. Thus, spore counts were not

over-dispersed in all of the ®ltered water volumes investi-

gated. In the case of spores of Bacillus subtilis var. niger(spiked into the raw water supply to the pilot plant) counts

were Poisson-distributed within some 500-l ®ltered water

volumes (e.g. Fig. 3). However, quite extreme spatial

heterogeneity of the spiked niger spores was observed in

the ®ltered water samples in other 500-l volumes. This is

shown for a series of 40 ´ 100-ml samples collected 2 h after

back-wash of the rapid sand ®lter in Fig. 4. While most of

the 100-ml samples contained zero, one or two counts and

the arithmetic mean count was 1á1 100 ml±1, one sample

contained seven spores and another sample contained 22

spores. This extreme heterogeneity could not be accommo-

dated by the Poisson distribution. Indeed, 99á9% of 100-ml

samples taken from a Poisson distribution with a mean of 1á1spores 100 ml±1 would contain between zero and ®ve spores.

There are several possible reasons for the spatial heterogen-

eity observed in spore counts within ®ltered water volumes.

i The few samples with extreme high counts are outliers

due to contamination or experimental error. However, it

should be noted that high count samples were more

often observed in the treated water samples and only

rarely in the raw waters. Indeed, spore counts in the raw

waters were, in general, Poisson distributed (Gale et al.1997).

ii Micro-organisms are concentrated by chemical coagu-

lation/sand ®ltration and therefore `break-through'

®lters during drinking water treatment in a non-

homogeneous manner.

iii Heterogeneity could re¯ect spores released from parti-

cles during treatment of the sample with Tween-80

detergent prior to microbiological analysis (Gale et al.1997).

iv Heterogeneity of spores could re¯ect different regrowth

rates of spore-forming bacteria or indeed spore forma-

tion within the sand ®lter environment.

In addition, there is the question of whether the effect is

`species-speci®c' (i.e. some micro-organisms show spatial

heterogeneity, while others do not). In contrast to spores,

the degree of spatial heterogeneity of total coliform bacteria

and heterotrophic plate count bacteria (22°C, 3 d) was little

–1

Fig. 1 Counts of aerobic spores in 100-ml samples as a function of

volume of ®ltered water

Fig. 2 Observed counts and cumulative

negative binomial (- - -) and Poisson fre-

quency distributions (±±) for aerobic spore

counts in 100-ml samples from 500 l ®ltered

water (Fig. 1)

194 P. GALE


affected by drinking water treatment in studies at pilot scale.

Indeed, coliform counts were found to be Poisson distri-

buted within 500-l samples of ®ltered water (resembling

those in Fig. 3). It should be noted that ground water

supplies do not generally receive coagulation/¯occulation

and ®ltration. Haas and Rose (1996) reported a Poisson

distribution of Cryptosporidium oocysts in an untreated

drinking water supply.

3.1.3 Implications for Cryptosporidium exposuresto drinking water consumers under non-outbreak

conditions. To date, no data are available on the degree of

spatial heterogeneity of C. parvum oocysts within large

volumes of treated water. If the spiked B. subtilis var. nigerspore distributions are representative of C. parvum oocyst

distributions within large volume drinking water samples

then, under some conditions, oocysts could be Poisson

distributed (Fig. 3) while, under other conditions, they

could exhibit spatial heterogeneity (Fig. 4). The total

number of B. subtilis var. niger spores in the 40 ´ 100-ml

samples in Fig. 4 was 43. On the basis of this distribution,

43 Cryptosporidium oocysts within an 80-l volume could be

Fig. 3 Observed counts (mean 4á98; variance

4á99) and cumulative Poisson frequency dis-

tribution for Bacillus subtilis var. niger spore

counts in 100-ml samples from 500 l ®ltered

water taken from a well-ripened ®lter (20 h

after backwash)

–1

Fig. 4 Variation in counts of Bacillus subtilis

var. niger spores measured in 100-ml

samples from within 500 l ®ltered water

collected 2 h after backwash



distributed in 2-l samples such that most people imbibing

2 l would ingest zero, one or two oocysts, but one individual

would ingest seven oocysts and another 22 oocysts. It is

concluded that, even under non-outbreak conditions, the

possibility exists that some consumers could ingest doses of

higher than just one or two oocysts in a single exposure. It

could be argued that oocysts are too dilute during non-

outbreak conditions for this to happen. However, oocyst

counts of 48 and 286 in 100 l have been reported (LeCh-

evallier et al. 1991; Craun et al. 1998). Furthermore, other

samples with still higher oocyst counts could have been

missed (Christian and Pipes 1983) and would only have been

detected by more intensive sampling. It should be noted that

the major proportion of the population will ingest zero

oocysts each day during non-outbreak conditions, because

the majority of 100-l volumes record zero oocysts in ®ltered

water supplies (LeChevallier et al. 1991).

3.2 A model for Cryptosporidium oocystconcentrations in drinking water duringan outbreak

Gale and Stan®eld (2000) used a Monte Carlo approach to

simulate daily exposures to Cryptosporidium oocysts to

individual consumers drinking treated water for conditions

representative of a waterborne outbreak. The approach was

to simulate a Poisson-log-normal distribution to describe the

integer doses of oocysts in the 0á43±2á9-l volume samples

ingested daily by consumers drinking from the supply

(Roseberry and Burmaster 1992). The probabilities of

individual consumers ingesting certain doses of oocysts are

presented in Table 1. According to the model, the majority

of consumers do not ingest any oocysts each day even during

an outbreak. This is consistent with the ®nding that 66% of

1000-l volumes did not contain oocysts during the Farmoor

(UK) outbreak of cryptosporidiosis (Richardson et al. 1991)

and that oocysts are often not detected in outbreaks (Joseph

et al. 1991; Craun et al. 1998). The simulation predicts that

about half of those consumers who were exposed ingested

just a single oocyst each day. However, a small proportion of

consumers were exposed to between ®ve and 10 oocysts d±1.

Furthermore, one in 10 000 consumers was exposed to very

high doses which approached the ID50 of about 150 oocysts

for C. parvum in healthy human adults (DuPont et al. 1995)

with no acquired protective immunity (Fig. 5). (The ID50 is

the dose of pathogens which, when given to each and every

member of a population, infects half of that population.)

The arithmetic mean for the simulated oocyst exposures

was 0á373 oocysts person±1 d±1. Exposures calculated using

this single point average and assuming a Poisson distribution

are also presented in Table 1. The MRA models which use

p

i

3 4 5 6

i

Fig. 5 Negative exponential dose±response curve (r � 0á00419) ®tted

by Haas et al. (1996) to Cryptosporidium parvum human infectivity data

(j) obtained for volunteers selected on the basis of having no

serological evidence of past infection with C. parvum (DuPont et al.

1995). The log-probit curve (l � 2á119; r � 0á614; log10) is shown for

comparison (. . .). On rechallenge (Okhuysen et al. 1998), 16% of

volunteers were infected with a dose of 500 oocysts (d). Negative

exponential curve (r � 0á00035) through this point (- - -)

% of population exposed to dose

Dose

(oocysts person)1 d)1)

Poisson-log-normal distribution

(l = ±1á66; r = 1á04 log10

oocysts person)1 d)1)

Arithmetic mean exposure

(0á373 oocysts person)1 d)1)

in Poisson distribution

0 88á43 68á86

1 6á76 25á68

2±5 3á57 5á45

6±10 0á61 0á0003

11±50 0á57 < 10)10

51±100 0á05 0

> 100 0á01 0

Doses ingested are compared accommodating the variation predicted using the Poisson-log-

normal distribution and simply using the arithmetic mean exposure in the Poisson distribution.

Table 1 Simulated daily exposures to

Cryptosporidium oocysts for individual

consumers under conditions consistent with

an outbreak (Gale and Stan®eld 2000)

196 P. GALE


this single point average with the Poisson distribution and

ignore the large variation in the simulated Poisson-log-

normal exposures would predict that a much higher

proportion of the population is actually exposed daily to

oocysts (31% compared with 11%) but that those who are

exposed never ingest more than about ®ve oocysts each day.

By ignoring the temporal/spatial heterogeneity in expo-

sures, MRA would never predict that a consumer would be

exposed to a dose approaching the ID50 for C. parvum even

during an apparent outbreak. It is important to note that the

total loading of oocysts on the population is the same (3730

oocysts 10 000 persons±1 d±1) for both exposure models in

Table 1. The two models differ only in how those oocysts

are distributed across the population.

4. DOSE±RESPONSE CURVES.ESTIMATING THE RISK TO HUMANS FROMINGESTING LOW PATHOGEN DOSES

Dose±response data are critical for quantitative MRA.

Dose±response data from human studies have been obtained

for several waterborne pathogens including rotavirus (Ward

et al. 1986) and C. parvum (DuPont et al. 1995). Three types

of mathematical models may be ®tted to the infectivity data

to produce a dose±response curve for use in MRA (Haas

1983). These are the negative exponential, Beta-Poisson and

log-probit curves. The curves differ in certain fundamental

assumptions and, in particular, whether pathogens act

independently or co-operatively during initiation of infec-

tion. The risks predicted by the three types of curve differ

most at the low pathogen exposures typically experienced

through drinking treated water and the choice of mathe-

matical model critically in¯uences the predicted risks.

4.1 Cryptosporidium parvum

The infectivity of a calf strain of C. parvum was determined

by DuPont et al. (1995) in human feeding trials. A total of

29 healthy immunocompetent adults were each given a

single dose of between 30 and 106 oocysts. The proportion

infected at each dose is plotted in Fig. 5. Exposure

simulations presented in Table 1 suggest that, even under

outbreak conditions, most drinking water consumers ingest

fewer than 30 oocysts daily and half of those consumers

exposed to oocysts ingest just a single oocyst. Therefore, the

doses administered by DuPont et al. (1995) are generally too

high for the application of modelling risks through water.

To estimate the risk from ingestion of just a single C.parvum oocyst, a process of low dose extrapolation of ®tted

mathematical dose±response curves is used.

Haas et al. (1996) ®tted a negative exponential dose±

response curve to the C. parvum human infectivity data

(Fig. 5). According to this model the probability, P, of being

infected by ingesting a dose of N pathogens is expressed

mathematically as:

P � 1ÿ eÿrN �1�where r is a parameter speci®c for the pathogen and the host

population and represents the fraction of the ingested

pathogens that survive to initiate infections (Regli et al.1991). This dose±response relationship assumes that the

oocysts act completely independently during infection and

do not act co-operatively, for example, in overcoming the

host protective barriers. Indeed, at low doses, Eqn 1

transforms to:

P � rN �2�indicating a direct linear relationship between the ingested

dose, N, and risk, P, of infection. The risks of infection

predicted by Eqn 1 for low oocyst doses are presented in

Table 2. The risk of infection decreases linearly with

decreasing dose (at low doses). For example, the risk from

10 oocysts is 10-fold that from ingestion of just a single

oocyst. If pathogens acted co-operatively during infection,

perhaps by assisting in overcoming or lowering some of the

host defensive barriers such that subsequent oocysts had a

greater chance of successful infection, then, at low doses, the

probability of infection would be non-linearly related to the

dose. One mathematical model which describes a non-linear

dose±response relationship is the log-probit curve (Haas

1983). This model is de®ned by two parameters (l and r)

and is also shown in Fig. 5. It is apparent that the negative

exponential and log-probit curves do not differ markedly

over the range of doses administered in the trial (30±106

oocysts). However, much greater differences are observed on

extrapolation to the low doses of oocysts to which drinking

water consumers are likely to be exposed (Table 1). The risk

of infection predicted by the log-probit model from

ingestion of a single oocyst is 15-fold smaller than that

predicted by the negative exponential model (Table 2).

According to the log-probit model, ingestion of single

Table 2 Risks of Cryptosporidium parvum infection predicted by low

dose extrapolation of negative exponential and log-probit dose±

response curves (Fig. 5) for human volunteers with no serological

evidence of past infection

Dose±response curve

Dose

(oocysts)

Negative

exponential

(independent action)

Log-probit

(co-operative

action)

Ratio (negative

exponential :

log-probit)

1 0á0041 0á00028 14á92 0á0083 0á0015 5á43 0á0125 0á0038 3á310 0á041 0á034 1á2



oocysts presents a very small risk to humans and oocysts

only present a considerable risk of infection when ingested

in higher doses (i.e. > 10 oocysts). This re¯ects the fact that

if oocysts acted co-operatively then low doses (e.g. one

oocyst) would present greatly diminished risks. According to

the log-probit model, the risk of infection from a dose of one

oocyst is 120-fold lower than from ingestion of 10 oocysts.

At doses of above about 10 oocysts, however, the risks

predicted by the negative exponential and log-probit dose±

response curves are similar (Table 2).

The extreme scenario for a non-linear dose±response

relationship is the threshold or minimum infective dose.

The concept of a minimum infective dose or threshold is

misleading in MRA because it suggests zero risk of infection

at very low pathogen doses. This does not appear to be the

case for C. parvum. Teunis (1997) has demonstrated that

®tted models assuming threshold doses of two, three, four or

®ve oocysts do not ®t the infectivity data in Fig. 5 and lead

to increasingly steep dose±response curves. There is no

experimental evidence for a minimum infective dose and no

reason why a single oocyst or other waterborne pathogen

should not be able to initiate infection, albeit with very low

probability in some cases. Indeed, Meynell (1957) concluded

that any bacterium of Salmonella typhimurium `which enters

the tissues from the gut can initiate a fatal infection and that

the probability of effecting such an entrance almost entirely

determines the probability of an inoculated bacterium

causing a fatal infection'. Blewett et al. (1993) concluded

that the minimum infective dose for gnotobiotic lambs is

just one oocyst of C. parvum. According to Blewett et al.(1993), C. parvum is very infectious to gnotobiotic lambs and

the ID50 is less than 10 oocysts. The ID50 for C. parvum in

healthy human adults with no previous exposure is about

150 oocysts (Fig. 5) and single oocysts appear to present

much lower risks to healthy human adults than to gnoto-

biotic lambs. The reason for this may lie in the nature of the

host protective barriers. In particular, gnotobiotic animals

have no indigenous gut microbiota which are known to

inhibit infection by enteric pathogens such as salmonellas,

shigellas and Vibrio cholerae (Savage 1972). It has been

shown, for example, that E. coli suppressed the growth of

Shigella ¯exneri in gnotobiotic animals.

4.1.1 Experiments with salmonellas in mice suggestthat pathogens act independently and do notco-operate during infection. Meynell and Meynell

(1958) studied the relationship between dose and latent

period (the time interval between inoculation and response)

for Salm. typhimurium given to mice by intraperitoneal

injection. Their results demonstrated that the mean death

time increased with decreasing doses greater than the ID50

but tended to become constant for doses less than the ID50.

Those observations are consistent with the inoculated

organisms acting completely independently such that, at

doses below the ID50, a mouse fatally infected will die

following the multiplication of only one effective organism.

This conclusion of Meynell and Meynell (1958) supports the

use of a linear dose±response curve in MRA. However, the

pathogens were administered by intraperitoneal injection

and not by oral challenge in those experiments. This is an

important consideration in MRA models for drinking water

where consumers are infected through oral challenge.

Indeed, the oral route presents many more barriers to

infection by salmonella than intraperitoneal injection. These

barriers include pH and the combined inhibitory effects of a

low oxidation±reduction potential and short-chain fatty

acids produced by the indigenous gut microbiota (Meynell

1963). Meynell (1955) reported that the numbers of a

virulent strain of Salm. typhimurium needed to kill half of

a group of mice is 10 or less by intraperitoneal injection but

a million or more when the organisms are given by mouth.

Meynell (1957) demonstrated that fatal infections from

Salm. typhimurium given to mice by mouth arose from a very

small number of organisms or possibly just a single

organism. He concluded that inoculated bacteria act inde-

pendently not co-operatively. However, it could be argued

that, if infection through the oral route were a two-stage

mechanism, then his conclusions are only applicable to the

second stage and that the initial stage could involve events

more consistent with a co-operative model. One mechanism

of protection against infection by mouth is the effect of the

normal gut microbiota on the ingested organisms. Indeed,

Meynell (1955) reported that, when the gut microbiota were

killed by streptomycin, the oral LD50 for a streptomycin-

resistant strain of Salm. typhimurium was reduced from

greater than one million organisms to less than ®ve

organisms.

If infecting salmonellas were able to co-operate with each

other by locally inhibiting the normal gut microbiota or by

saturating the host defences then a linear dose±response

relationship may not be appropriate. Bacteria contain a

variety of mechanisms for overcoming host barriers, inclu-

ding endotoxins to damage the mucosa, neutralization of

antibacterial agents, IgA proteases and acid suppression. In

addition, micro-organisms produce many substances which

give them competitive advantage, or a means of defence, in

establishing themselves in natural environments. For exam-

ple, the bacteriocins are polypeptide antibiotics produced by

one species of bacteria which cause damage to the cellular

membranes of other bacteria. It could be speculated that in

an oral challenge comprising thousands of salmonellas, some

of those salmonellas could use these mechanisms to lower

the host barriers so increasing the chances of a subsequent

single bacterium successfully establishing infection. Essen-

tially this would involve a two-stage process for microbial

infection. Meynell and Maw (1968) presented results with

198 P. GALE


salmonellas in mice suggesting that infection passed through

an initial stage lasting a few hours, in which a varying

proportion of the inoculum is killed, followed by a second

stage. The initial stage is `decisive'. Could it be that bacteria

act co-operatively during that initial stage so helping

subsequent bacteria to survive and progress to the second

stage in which a single bacterium multiplies and causes

infection? If so, then exposure to low pathogen doses would

present lower risks than those predicted by the negative

exponential or Beta-Poisson dose±response curves and a

non-linear model would be more appropriate.

There are currently no experimental data on whether

C. parvum oocysts act independently or co-operatively

during infection. It is generally assumed that oocysts act

independently and that the negative exponential dose±

response curve is appropriate (Haas et al. 1996). Williams

and Meynell (1967) presented a hypothetical model for

infection based on morbidity and mortality thresholds.

4.1.2 Acquired protective immunity for Cryptosporidiumparvum. The 29 adults used in the study of DuPont et al.(1995) were selected on the basis of having no serological

evidence of past infection with C. parvum. Frost et al. (1997)

suggested that endemic Cryptosporidium infection, for

example through surface water supplies, protects residents

from illness through acquired protective immunity. Infor-

mation on the extent of seroprevalence in various drinking

water communities and the quantitative protective effect is

necessary for development and validation of MRA models

for C. parvum in drinking water. In a recent study,

Okhuysen et al. (1998) investigated whether infection of

humans with C. parvum is protective 1 year after exposure.

The subjects were 19 of the 29 adults used in the study of

DuPont et al. (1995). Rechallenge with a dose of 500 oocysts

resulted in infection of three of the 19 adults (16%).

Constructing a negative exponential dose±response curve

through this one result (dashed curve in Fig. 5) suggests

that primary exposure increased the ID50 from 165 to 1987

oocysts and that the risk of infection from a single oocyst (rin Eqn 1) is decreased from 0á00419 to 0á000349. Chappell

et al. (1999) challenged 17 healthy adults with pre-existing

anti-C. parvum serum IgG with doses of 500±50 000

oocysts. The ID50 was 1880 oocysts. It should be noted

that the protective effect of acquired immunity on infection

becomes less important at higher doses. Indeed, Chappell

et al. (1999) reported that infection and diarrhoea were

associated with the higher challenge doses.

4.1.3 Virulence of different strains of Cryptosporidiumparvum. Okhuysen et al. (1999) reported the infectivity of

three distinct C. parvum isolates. The ID50 for presumed

infection differed among isolates: TAMU, nine oocysts;

IOWA, 87 oocysts and UCP, 1042 oocysts. Risks of

presumed infection from ingestion of single oocysts (r in

Eqn 1) according to the negative exponential dose±response

relationship (Eqn 1) are calculated as: TAMU, r � 0á078;

IOWA, r � 0á008 and UCP, r � 0á00067.

4.2 Human rotavirus

For waterborne pathogens such as salmonellas and rotavi-

ruses, the Beta-Poisson dose±response curve is more appro-

priate than the negative exponential curve (Rose and Gerba

1991). The negative exponential dose±response curve (Eqn 1)

is based on a single parameter, r, which is constant for a given

host population±pathogen interaction. The Beta-Poisson

dose±response curve assumes that r is actually not a constant

but is itself described by a Beta probability distribution.

According to the Beta-Poisson model, the probability of

infection, P, from ingesting a dose N is written as:

P � 1ÿ 1� N

N50

�21=a ÿ 1�� ÿa

�3�

where a and N50 characterize the dose±response (Regli et al.1991). As a increases so the Beta-Poisson model becomes

closer to the negative exponential model (Eqn 1). N50

represents the ID50.

Ward et al. (1986) obtained infectivity data for human

rotavirus in 62 healthy adult volunteers. Subjects ingested

doses ranging from 0á009 to 90 000 focus-forming units

after consumption of 50 ml 4% NaHCO3. The proportions

of adults infected at each dose are presented in Fig. 6. The

Beta-Poisson dose±response curve ®tted by Haas et al.(1993) is plotted in Fig. 6 together with the negative

exponential curve with an ID50 of 5á6 virus particles. The

Beta-Poisson curve is less steep than the linear negative

exponential model (Regli et al. 1991) and predicts relatively

Fig. 6 Beta-Poisson dose±response curve (a � 0á265; N50 � 5á6)

(±±±) ®tted by Haas et al. (1993) to rotavirus infectivity data (j) of

Ward et al. (1986). Negative exponential model (r � 0á124) with same

ID50 is shown for comparison (- - -)



higher risks for the lower doses. Thus an average exposure of

0á373 rotaviruses person±1 d±1 is translated into a risk of

infection of 0á149 person±1 d±1 by the Beta-Poisson model but

only 0á045 person±1 d±1 by the negative exponential model

(see arrow in Fig. 6). It is apparent from Fig. 6 that

rotaviruses are very infectious to humans and the ID50 is

just 5á6 virus particles. However, it should be noted that the

infectivity of the rotavirus in the study may have been

augmented over that in nature because of the prior consump-

tion of bicarbonate. Ward et al. (1986) argued that food may

have a similar effect. Water would not. Matrix effects on

pathogen infectivity are an important factor when directly

comparing quantitative risks of rotavirus infection predicted

through water and food routes.

4.3 Bovine spongiform encephalopathy

4.3.1 The human oral ID50. Risk assessment models for

BSE in drinking water have been developed by estimating

the human oral ID50 on the basis of infectivity data from

mice feeding studies. Of key importance in this respect is the

magnitude of the cow-to-man species barrier relative to the

cow-to-mouse species barrier. For the purpose of MRA it

was assumed that the oral ID50 for humans is just 1 g BSE-

infected bovine brain (Gale et al. 1998). Molecular calcula-

tions suggest that this is comprised of some 1013 BSE prion

protein (PrP) molecules which will be dispersed to a large

degree in the aquatic environment. Thus, assuming BSE

infectivity were to break-through into a drinking water

supply, drinking water consumers would only ever be

exposed to doses comprising minute subfractions of an ID50,

even cumulatively over the period of a human lifetime.

4.3.2 Evidence that bovine spongiform encephalopa-thy prions may act co-operatively. Two possible mech-

anisms, which are not mutually exclusive, have been

discussed for a `co-operative' effect or a threshold dose by

Gale (1998b). The ®rst mechanism is based on there being

a requirement for a crystalline aggregate of prions such

that individual molecules cannot initiate infection. The

second mechanism speculates that conversion of PrP into

the disease-related form proceeds through a `co-operative'

interactive effect between individual PrP molecules within

the two-dimensional plane of the lipid bilayer membrane.

While these theories re¯ect speculation based on the

biophysical mechanisms of PrP interactions, there is some

indirect evidence for a threshold from cattle studies. Thus,

data presented in Anderson et al. (1996) from cattle

feeding studies show that the incubation time to infect

50% of animals increases with decreasing dose. For

example, the incubation time to infect ®ve of the 10 cattle

was 40 months for a dose of three ´ 100 g bovine brain,

43 months for 100 g and greater than 52 months for 1 g.

This raises the question as to how long the incubation

period is for smaller doses, e.g. 0á0001 g. If the trend of

increasing incubation time with lower dose continues, then

for very small doses the incubation time may exceed the

natural lifetime of a bovine (and even a human) such that

small doses essentially present zero risk if ingested orally.

However, it should be noted that this observation is not

proof of a threshold. Thus, Meynell and Meynell (1958)

showed that the mean death time for mice challenged by

intraperitoneal injection with Salm. typhimurium increased

with decreasing doses greater than the ID50 but tended to

become constant for doses less than the ID50. Indeed, they

concluded that inoculated organisms acted completely

independently such that, at doses below the ID50, a mouse

fatally infected will die following the multiplication of only

one effective organism. Kimberlin and Wilesmith (1994)

suggested that BSE infection is transmitted through

infectious `packets'. Evidence for a threshold effect is also

supported to some extent by epidemiological data which

show no evidence of horizontal transmission of BSE in

cattle (Donnelly et al. 1999). Horizontal transmission

would include infection through contaminated pastures.

4.3.3 Estimating the risks from ingestion of minutesubfractions of an ID50 through drinking water. Gale

(1998b) ®tted negative exponential and log-probit dose±

response curves to titration data for BSE-infected bovine

brain in mice (intracerebral challenge). The doses admin-

istered ranged between 0á02 and 20 ID50s (Fig. 7). As for the

C. parvum dose±response data (Fig. 5), the two curves were

similar over the range of doses for which experimental

infectivity data were available. However, the two curves

Fig. 7 Bovine spongiform encephalopathy (BSE) dose±response

curves from Gale (1998b); negative exponential (- - -) and log-probit

(±±±) dose±response curves ®tted to BSE infectivity data in mice (j).

BSE data from Taylor et al. (1995)

200 P. GALE


differed markedly in their predictions for exposures of less

than 0á01 of an ID50. This is apparent when the axis for the

probability of infection is plotted logarithmically (Fig. 7).

The negative exponential curve shows a direct linear effect

between risk and dose as expected if the prions were to act

completely independently (Eqn 2). In contrast, the risk

predicted by the log-probit curve diminished rapidly at the

lower doses which would be consistent with the prions

acting in a co-operative manner.

4.4 The need for more dose±response datafor waterborne pathogens

Dose±response data currently available for waterborne path-

ogens were, in general, obtained from volunteer studies with

healthy immunocompetent adults. Little or no data are

available for the more susceptible individuals in the popu-

lation such as children, the elderly and the immunocompro-

mised. More information is needed on how acquired

protective immunity affects the dose±response curve not just

for C. parvum but also for other waterborne pathogens such as

E. coli O157 and rotavirus. It is generally accepted for MRA

that waterborne pathogens such as salmonellas, protozoa and

viruses act independently during infection and do not co-

operate or interact. However, evidence on whether there is a

threshold dose for BSE prions is of critical importance for

assessing the risks of BSE transmission to humans and cattle

through environmental routes of exposure.

5. INTEGRATING PATHOGEN EXPOSURESAND DOSE±RESPONSE CURVES

Table 1 compares Cryptosporidium oocyst exposures simu-

lated according to the Poisson-log-normal and Poisson

models. It is important to note that the arithmetic mean

exposure (0á373 oocysts person±1 d±1) is the same for both

models. Therefore, the total oocyst loading on the popula-

tion as a whole each day is the same for both models. The

two models differ only in how those oocyst doses are

distributed to individual consumers within the population.

The effect of these differences in variation in pathogen

exposures on risk prediction is illustrated below using the

dose±response curves for C. parvum (Fig. 5) and rotavirus

(Fig. 6).

5.1 Risk prediction for an outbreakof cryptosporidiosis

Table 3 compares the risks of infection predicted by the

three C. parvum dose±response curves (Fig. 5) for the

oocyst exposures simulated in Table 1. The risk predicted

using the negative exponential dose±response curve is little

affected by whether the log-normal variation in exposures

is accommodated or just the single point arithmetic mean

exposure is used in the Poisson distribution. For both the

Poisson-log-normal and Poisson distributions of exposure,

a risk of about 15 infections per 10 000 persons d±1 is

predicted. Indeed, the same risk is predicted simply by

using the arithmetic mean of 0á373 oocysts person±1 d±1

directly in Eqn 1 (Table 3). This is because the dose±

response relationship is linear at low doses (Eqn 2). It has

been shown mathematically that, if oocysts act independ-

ently during infection, then the arithmetic mean exposure

is suf®cient for prediction of the risk of infection (Haas

1996). In effect, the risk of infection is directly related to

the total number of oocysts in the drinking water supply

and is not in¯uenced by their spatial distribution. There-

fore, in the three scenarios with 50 oocysts in 100 l,

whether one consumer ingests all 50 oocysts in his/her 2 l

(and the other 49 consumers ingest no oocysts) or whether

Table 3 Predicted number of

Cryptosporidium parvum infections (per 10 000

persons per day) using simulated oocyst

exposures in Table 1 and dose±response

curves ®tted in Fig. 5

Exposure

Dose±response curve

Poisson-log-normal

(l = ±1á66; r = 1á04

log10 oocysts

person)1 d)1)

Poisson distribution

(mean = 0á373 oocysts

person)1 d)1)

Arithmetic mean

(0á373 oocysts

person)1 d)1)

Negative exponential

(no immunity)

14á5* 15á5* 15á6

Log-probit (no immunity) 9á3* 1á8* 0á17

Negative exponential

(acquired immunity)

1á3* 1á3* 1á3

*For each integer dose (n = 0, 1, ¼ 300) the probability of exposure to that dose was multiplied

by the risk of infection from that dose predicted using the dose±response curve (Fig. 5). The

products were then summed. The risk calculation is described fully in Gale and Stan®eld (2000).

Risks predicted using the arithmetic mean oocyst exposure directly in the dose±response curve are

also presented.



all 50 consumers each ingest one oocyst in their 2 l, the risk

predicted by Eqn 1 is similar; 0á19 infections per 50 persons

for the former and 0á21 infections per 50 persons for the

latter.

The variation in exposures is, however, important for the

log-probit dose±response curve. Indeed, ignoring the log-

normal variation in oocyst exposures and just using the

Poisson distribution of exposures with the log-probit dose±

response curve underestimates the risk of infection by a

factor of ®vefold (Table 3). This is because the Poisson

distribution of pathogen exposures does not predict expo-

sures to high pathogen doses approaching the ID50

(Table 1). It only predicts exposures to low doses, which

have a greatly diminished risk according to the log-probit

curve (Table 2). Using the arithmetic mean exposure of

0á373 oocysts person±1 d±1 directly in the log-probit dose±

response curve predicts a 55-fold lower risk compared with

using the Poisson-log-normal exposure model with the log-

probit dose±response curve (Table 3).

The impact of acquired protective immunity in the

Cryptosporidium risk assessment model is important. For

persons with acquired protective immunity the model

predicted 1á3 infections per 10 000 persons d±1 for both

the Poisson and Poisson-log-normal exposures (Table 3).

This is a 12-fold lower infection rate than that predicted for

persons with no serological evidence of past infection with

C. parvum.

5.2 Risk prediction for rotavirus

Haas et al. (1993) modelled the risks from enteric viruses in

drinking water and calculated that the risk of death from a

70- year exposure to waterborne virus may be as high as

one in 20, which is considerable. The risk was determined

using a single point estimate of exposure based on the

arithmetic mean virus exposure calculated from the mon-

itoring data of Payment et al. (1985). The effect on risk

prediction of ignoring the log-normal variation in virus

densities in drinking water and just using the arithmetic

mean exposure is investigated by assuming the Cryptospo-ridium oocyst exposures simulated in Table 1 represent

virus exposures. Using the arithmetic mean exposure of

0á373 virus person±1 d±1 directly in the Beta-Poisson dose±

response curve (arrow in Fig. 6) predicts 1488 infections

per 10 000 persons d±1. In contrast, accommodating the

Poisson-log-normal variation in exposures in Table 1

predicts 410 viral infections per 10 000 persons d±1. Thus,

ignoring the log-normal variation in virus exposures

through drinking water could, in the case of the virus risk

assessment, overpredict the risk by a factor of 3á6-fold.

This is partly due to the fact that ignoring the log-normal

variation predicts that more consumers are exposed to virus

each day (Table 1).

5.3 Risk prediction for highly infectious pathogen(ID50 = 1 micro-organism)

Setting r � 0á7 in Eqn 1 generates a dose±response curve for

which the ID50 is just one pathogen. This would represent a

highly infectious agent. Accommodating the log-normal

variation in exposures in Table 1 predicted 785 infections

per 10 000 persons d±1 compared with 2298 infections

predicted by using the arithmetic mean exposure of 0á373

oocysts person±1 d±1 directly in Eqn 1. Thus, by ignoring the

log-normal variation in exposures, the risk assessment would

overpredict the risk by almost threefold.

6. RISK PREDICTION FOR BOVINESPONGIFORM ENCEPHALOPATHY

The arithmetic mean exposure estimated through the

drinking of 2 l d±1 of water from an aquifer potentially

contaminated by rendering plant ef¯uent is 6 ´ 10±11 ID50

person±1 d±1 (Gale et al. 1998). Thus, the arithmetic mean

dose ingested by each consumer is 2á19 ´ 10±8 ID50 over a

period of 1 year. This dose translates into a risk of 1á5 ´ 10±8

person±1 year±1 using the negative exponential dose±

response curve. If the BSE prions were to act co-operatively,

according to the log-probit dose±response curve, then

molecular dispersion and dilution of the BSE prions in

drinking water would virtually eliminate drinking water as a

route of transmission. Thus, according to the log-probit

model, the predicted risk of contracting vCJD from

ingesting an annual dose of 2á19 ´ 10±8 ID50 in a single

exposure is 2á7 ´ 10±28 person±1 year±1 (Gale 1998b). This is

almost a factor of 1020 less than that predicted by the linear

dose±response curve.

7. DISCUSSION

The available evidence suggests that there is the potential for

considerable variation in pathogen exposures through

drinking water. For Cryptosporidium, this arises from

variation in oocyst loadings in the raw waters and ¯uctu-

ations in the removal ef®ciencies of oocysts by drinking

water treatment processes. A Monte Carlo simulation for

oocyst exposures during a waterborne outbreak of crypto-

sporidiosis predicts that most consumers on the supply are

not exposed to any oocysts each day, but a few ingest high

doses which approach the ID50 for C. parvum (�150 oocysts)

in a small proportion (Table 1). Even during non-outbreak

conditions, mechanisms may exist by which a small

proportion of consumers could be exposed to high doses

in a single exposure.

Modelling the variation in pathogen densities in drinking

water is important for MRA for three reasons. First,

temporal/spatial heterogeneity in pathogen densities will

202 P. GALE


cause monitoring programmes based on `spot' samples to

underestimate the net pathogen loading in the drinking

water supply and hence the risk to public health (Gale and

Stan®eld 2000). Second, the increase in variation of

pathogen densities in treated water relative to the raw

water will tend to cause monitoring programmes to

underestimate the arithmetic mean pathogen density to a

greater degree in the treated water than in the raw waters.

This will result in an over-estimation of the net pathogen

removal by treatment and hence an under-estimation of the

risk (Gale and Stan®eld 2000). Third, the degree of

variation directly affects the magnitudes of the pathogen

doses ingested daily by individual consumers (Table 1).

This paper assesses the importance of modelling the

variation in pathogen exposures through drinking water

in the case of risk prediction for C. parvum and rotavirus.

More extreme differences in individual exposure are

illustrated by comparing the doses of BSE infectivity

ingested through consumption of beef-on-the-bone and

through drinking water from an aquifer potentially con-

taminated by rendering plant ef¯uent (Gale 1998b).

The negative exponential dose±response relationship

assumes that the infecting agents (oocysts, virions or BSE

prions) act independently during initiation of infection and

that there is no threshold dose. In the case of the less

infectious agents such as C. parvum (and BSE prions),

modelling the log-normal variation in exposures to individ-

ual consumers is not necessary if this dose±response

relationship is used. This is apparent from the Monte Carlo

simulations in Table 3 which show that the risks predicted

using the negative exponential dose±response curves are

little affected whether the variation in exposures is accom-

modated using the Poisson-log-normal distribution or

whether the arithmetic mean exposure is used directly.

Indeed, Haas (1996) demonstrated mathematically that the

arithmetic mean oocyst exposure is the appropriate descrip-

tor of exposure for C. parvum. However, in the case of more

highly infectious agents, such as rotavirus (ID50 � 5á6virions) or an agent with ID50 � one organism, ignoring

the log-normal variation in pathogen exposures and just

using the arithmetic mean directly over-estimates the risk by

a factor of about threefold.

The variation in pathogen densities in drinking water is an

important consideration for the design of sampling pro-

grammes. According to the Poisson-log-normal simulation

for an outbreak of cryptosporidiosis (Gale and Stan®eld

2000), nine out of every 10 100-l volume `spot' samples will

under-estimate the arithmetic mean oocyst density and

hence the risk of infection. Furthermore, in some samples

this under-estimation was considerable. Thus, more than

half of the 100-l volume `spot' samples under-estimated the

arithmetic mean oocyst density in the drinking water by a

factor of over 10-fold.

Two factors in the dose±response relationship which

contribute uncertainty in microbiological risk assessment are

whether the pathogens act independently during infection

and the degree of acquired protective immunity in the

drinking water population. It is apparent from Table 3 that

acquired protective immunity has a major impact on the

predicted risk of Cryptosporidium infection in an outbreak

simulation. On the basis of the available dose±response data

for adult volunteers (Fig. 5), acquired protective immunity

affects the risk prediction by about 12-fold. Information on

whether C. parvum oocysts act independently during

infection, or alternatively co-operate in some way, appears

to be less important for the cryptosporidiosis outbreak

model (Table 3). Indeed, using the Poisson-log-normal

distribution of exposures presented in Table 1, the predic-

tion of 14á5 infections 10 000 persons±1 d±1 with the

negative exponential dose±response model is only a factor

of 1á6-fold greater than the prediction of 9á3 infec-

tions 10 000 persons±1 d±1 with the non-linear log-probit

dose±response curve (Table 3). However, the differences

become more signi®cant at the lower doses, i.e. one, two or

three oocysts (Table 2). It is these low doses that consumers

would be exposed to even during outbreak conditions if the

oocysts were Poisson-distributed (Table 1). Using the

Poisson model for oocyst exposures in combination with

the log-probit dose±response curve under-predicts the risk

of infection by a factor of 8á6-fold in the outbreak model

(Table 3). The risk predicted from ingestion of doses of

single oocysts is almost 15-fold higher by the negative

exponential dose±response model than by the log-probit

model (Table 2). Therefore, models which predict expo-

sures to very low oocyst doses (e.g. that of Teunis et al.1997) would be expected to over-estimate the risk by more

than 10-fold if there were a co-operative interaction between

oocysts in overcoming the host defensive barriers during

initiation of infection. Such mechanisms can be speculated

for bacterial agents. However, it is generally accepted that

oocysts act independently during infection. The negative

exponential dose±response model is therefore used in the

current risk assessment models for C. parvum (Haas et al.1996; Teunis et al. 1997). It is concluded that modelling the

log-normal variation in pathogen exposures would be of

considerable importance for risk assessment if pathogens

were to act co-operatively during infection. Furthermore, if

there were morbidity and mortality thresholds, as suggested

by Williams and Meynell (1967), then microbiological

risk assessment models need to accommodate the log-normal

variation in exposures to cover the possibility of

some consumers ingesting high doses which exceed those

thresholds.

Information on the risks from exposure to low doses of

BSE prions is critical for assessing the risks of BSE

transmission to both cattle and humans through the



aquatic environment. In the absence of direct evidence for

a co-operative effect between prions, risk assessment

models must assume a linear dose±response relationship.

The risk predicted for drinking water consumers supplied

from an aquifer potentially contaminated with rendering

plant ef¯uent was 1á5 ´ 10±8 person±1 year±1. Although

small, this prediction was based on the assumption that the

cow-to-man species barrier is a factor of 1000 (i.e. humans

are 1000-fold more resistant to BSE than cattle). Genetic

susceptibility between individual humans and, in partic-

ular, polymorphisms at codon 129 of the human prion

gene, appears to be a factor in de®ning the magnitude of

the cow-to-man species barrier (Raymond et al. 1997).

At the time when this risk assessment was developed

(1996) the only species barrier data available were for

humans with the amino acid valine at codon 129 of the

PrP gene. There were no data available for humans with

the amino acid methionine at codon 129 of the PrP gene.

Epidemiological data demonstrated that humans of this

second genotype are more susceptible to BSE than humans

with the amino acid valine at codon 129 (Will et al. 1996).

It was suggested, therefore, that the risk assessment should

assume that the cow-to-man species barrier was 1 and that

there was no protective species barrier effect for humans.

This would increase the predicted risk of BSE transmis-

sion to humans through drinking water by a factor of

1000-fold to 1á5 ´ 10±5 person±1 year±1, which would be

unacceptably high for a fatal brain disease. Information on

whether a threshold dose existed for BSE prions would

have helped to over-ride concerns about the exact mag-

nitude of the species barrier, providing it could have been

shown that BSE prions are indeed dispersed in drinking

water. The difference of 1020 in the predicted annual risk

of BSE transmission through drinking water according to

whether the negative exponential or log-probit dose±

response curve is used is extreme (Gale 1998b). Indeed,

the log-probit curve may not be appropriate because, in

terms of molecular biology, it is dif®cult to imagine

mechanistically how, for example, the 1010 PrP molecules

comprising a dose of �0á001 ID50 could co-operate

together during initiation of infection. The threshold

effect may manifest itself through incubation times. The

question to be addressed in this respect is how small is the

dose at which the incubation time exceeds the human

lifetime. In the absence of such data, risk assessment

models for BSE in drinking water should concentrate on

the pathway barriers which have less uncertainty than the

biomedical barriers.

8. REFERENCES

Anderson, R.M., Donnelly, C.A., Ferguson, N.M., Woolhouse,

M.E.J., Watt, C.J., Udy, H.J., MaWhinney, S., Dunstan, S.P.,

Southwood, T.R.E., Wilesmith, J.W., Ryan, J.B.M., Hoinville, L.J.,

Hillerton, J.E., Austin, A.R. and Wells, G.A.H. (1996) Transmission

dynamics and epidemiology of BSE in British cattle. Nature 382,

779±788.

Blewett, D.A., Wright, S.E., Casemore, D.P., Booth, N.E. and Jones,

C.E. (1993) Infective dose size studies on Cryptosporidium parvum

using gnotobiotic lambs. Water Science and Technology 27(3), 61±64.

Bruce, M.E., Will, R.G., Ironside, J.W., McConnell, I., Drummond,

D., Suttie, A., McCardle, L., Chree, A., Hope, J., Birkett, C.,

Cousens, S., Fraser, H. and Bostock, C.J. (1997) Transmission to

mice indicate that `new variant' CJD is caused by the BSE agent.

Nature 389, 498±501.

Chappell, C.L., Okhuysen, P.C., Sterling, C.R., Wang, C., Jakubow-

ski, W. and DuPont, H.L. (1999) Infectivity of Cryptosporidium

parvum in healthy adults with pre-existing anti-C. parvum serum

immonoglobulin G. American Journal of Tropical Medicine and

Hygiene 60(1), 157±164.

Christian, R.R. and Pipes, W.O. (1983) Frequency distribution of

coliforms in water distribution systems. Applied and Environmental

Microbiology 45, 603±609.

Craun, G.F., Hubbs, S.A., Frost, F., Calderon, R.L. and Via, S.H.

(1998) Waterborne outbreaks of cryptosporidiosis. Journal of

American Water Works Association 90(9), 81±91.

Donnelly, C.A., MaWhinney, S. and Anderson, R.M. (1999) A review

of the BSE epidemic in British cattle. Ecosystem Health 5(3), 164±173.

DuPont, H.L., Chappell, C.L., Sterling, C.R., Okhuysen, P.C., Rose,

J.B. and Jakubowski, W. (1995) Infectivity of Cryptosporidium parvum

in healthy volunteers. New England Journal of Medicine 332, 855±859.

Frost, F., Craun, G.F., Calderon, R. and Hubbs, S.A. (1997) So many

oocysts, so few outbreaks. Journal of American Water Works

Association 89(12), 8±10.

Gale, P. (1996) Developments in microbiological risk assessment

models for drinking water Ð a short review. Journal of Applied

Bacteriology 81, 403±410.

Gale, P. (1998a) Simulating Cryptosporidium exposures in drinking

water during an outbreak. Water Science Technology 38(12), 7±13.

Gale, P. (1998b) Quantitative BSE risk assessment: relating exposures

to risk. Letters in Applied Microbiology 27, 239±242.

Gale, P. and Stan®eld, G. (2000) Cryptosporidium during a simulated

outbreak. Journal of American Water Works Association 92(9),

105±116.

Gale, P., van Dijk, P.A.H. and Stan®eld, G. (1997) Drinking water

treatment increases micro-organism clustering; the implications for

microbiological risk assessment. Journal of Water Supply Research

and Technology ± Aqua 46, 117±126.

Gale, P., Young, C. and Lewin, K. (2000) Comment on `Outbreak of

enteroviruses and groundwater contamination in Taiwan: concept of

biomedical hydrogeology' (Jean1 1999) Ð A question of barriers and

epidemiology. Hydrogeology Journal 8, 348±349.

Gale, P., Young, C., Stan®eld, G. and Oakes, D. (1998) Development

of a risk assessment for BSE in the aquatic environment. Journal of

Applied Microbiology 84, 467±477.

Haas, C.N. (1983) Estimation of risk due to low doses of microorgan-

isms: a comparison of alternative methodologies. American Journal of

Epidemiology 118, 573±582.

Haas, C.N. (1996) How to average microbial densities to characterize

risk. Water Research 30(4), 1036±1038.

204 P. GALE


Haas, C.N., Crockett, C.S., Rose, J.B., Gerba, C.P. and Fazil, A.M.

(1996) Assessing the risk posed by oocysts in drinking water. Journal

of American Water Works Association 88(9), 131±136.

Haas, C.N. and Rose, J.B. (1996) Distribution of Cryptosporidium

oocysts in a water supply. Water Research 30, 2251±2254.

Haas, C.N., Rose, J.B., Gerba, C. and Regli, S. (1993) Risk assessment

of virus in drinking water. Risk Analysis 13, 545±552.

Joseph, C., Hamilton, G., O'Connor, M., Nicholas, S., Marshall, R.,

Stanwell-Smith, R., Sims, R., Ndawula, E., Casemore, D., Galla-

gher, P. and Harnett, P. (1991) Cryptosporidiosis in the Isle of

Thanet; an outbreak associated with local drinking water. Epidemi-

ology and Infection 107, 509±519.

Kimberlin, R.H. and Wilesmith, J.W. (1994) Bovine spongiform

encephalopathy (BSE): epidemiology, low dose exposure and risk.

Annals of N Y Academy of Science 724, 210±220.

LeChevallier, M.W. and Norton, W.D. (1995) Plant optimisation using

particle counting for treatment of Giardia and Cryptosporidium.

In: Protozoa Parasites and Water ed. Betts, W.B., Casemore, D.,

Fricker, C., Smith, H. and Watkins, J. pp. 180±187. Cambridge:The

Royal Society of Chemistry.

LeChevallier, M.W., Norton, W.D. and Lee, R.G. (1991) Giardia and

Cryptosporidium spp. in ®ltered drinking water supplies. Applied and

Environmental Microbiology 57, 2617±2621.

Maul, A., El-Shaarawi, A.H. and Block, J.C. (1985) Heterotrophic

bacteria in water distributions systems. I. Spatial and temporal

variation. Science of the Total Environment 44, 201±214.

Meynell, G.G. (1955) Some factors affecting the resistance of mice to

oral infection by Salm. typhi-murium. Proceedings of the Royal Society

of Medicine 48,2 916±918.

Meynell, G.G. (1957) The applicability of the hypothesis of inde-

pendent action to fatal infections in mice given Salmonella typhimu-

rium by mouth. Journal of General Microbiology 16, 396±404.

Meynell, G.G. (1963) Antibacterial mechanisms of the mouse gut II:

The role of EH and volatile fatty acids in the normal gut. British

Journal of Experimental Pathology 44, 209±219.

Meynell, G.G. and Maw, J. (1968) Evidence for a two-stage model of

microbial infection. Journal of Hygiene 66, 273±280.

Meynell, G.G. and Meynell, E.W. (1958) The growth of micro-

organisms in vivo with particular reference to the relation between

dose and latent period. Journal of Hygiene 56, 323±346.

Okhuysen, P., Chappell, C., Crabb, J., Sterling, C. and DuPont, H.

(1999) Virulence of three distinct Cryptosporidium parvum isolates for

healthy adults. Journal of Infectious Diseases 180, 1275±1281.

Okhuysen, P.C., Chappell, C.L., Sterling, C.R., Jakubowski, W. and

DuPont, H.L. (1998) Susceptibility and serologic response of

healthy adults to reinfection with Cryptosporidium parvum. Infection

and Immunity 66(2), 441±443.

Payment, P., Trudel, M. and Plante, R. (1985) Elimination of viruses

and indicator bacteria at each step of treatment during preparation of

drinking water at seven water treatment plants. Applied and

Environmental Microbiology 49, 1418±1428.

Pipes, W.O., Ward, P. and Ahn, S.H. (1977) Frequency distributions

for coliform bacteria in water. Journal of American Waterworks

Association 69, 664±668.

Raymond, G.J., Hope, J., Kocisko, D.A., Priola, S.A., Raymond, L.D.,

Bossers, A., Ironside, J., Will, R.G., Chen, S.G., Peterson, R.B.,

Gambetti, P., Rubenstien, R., Smits, M.A., Lansbury, P.T. and

Caughey, B. (1997) Molecular assessment of the potential transmis-

sibilities of BSE and scrapie to humans. Nature 388, 285±288.

Regli, S., Rose, J.B., Haas, C.N. and Gerba, C.P. (1991) Modelling the

risk from Giardia and viruses in drinking water. Journal of American

Waterworks Association 83(11), 76±84.

Richardson, A.J., Frankenberg, R.A., Buck, A.C., Selkon, J.B.,

Colbourne, J.S., Parsons, J.W. and Mahon-White, R.T. (1991) An

outbreak of waterborne cryptosporidiosis in Swindon and Oxford-

shire. Epidemiology and Infection 107, 485±495.

Rose, J.B. and Gerba, C.P. (1991) Use of risk assessment for development

of microbial standards. Water Science and Technology 24, 29±34.

Roseberry, A. and Burmaster, D.E. (1992) Log-normal distributions

for water intake by children and adults. Risk Analysis 12, 99±104.

Savage, D.C. (1972) Survival on mucosal epithelia, epithelial penet-

ration and growth in tissues of pathogenic bacteria. In: Microbial

Pathogenicity in Man and Animals ed. Smith, H. and Pearce, J.H.

pp. 25±57. The Society for General Microbiology. Cambridge:Cam-

bridge University Press.

Swerdlow, D.L., Woodruff, B.A., Brady, R.C., Grif®n, P.M., Tippen, S.,

Donnell, D., Geldreich, E., Payne, B.J., Meyer, A., Wells, J.,

Greene, K.D., Bright, M., Bean, N.H. and Blake, P.A. (1992) A

waterborne outbreak in Missouri of Escherichia coli O157:H7

associated with bloody diarrhea and death. Annals of Internal

Medicine 117, 812±819.

Taylor, D.M., Woodgate, S.L. and Atkinson, M.J. (1995) Inactivation

of the bovine spongiform encephalopathy agent by rendering

procedures. Veterinary Record 137, 605±610.

Teunis, P.F.M. (1997) Infectious Gastro-Enteritis Ð Opportunities for

Dose Response Modelling. Report 284 550 003. Bilthoven, the

Netherlands: National Institute of Public Health and the Environ-

ment (RIVM).

Teunis, P.F.M., Medema, G.J., Kruidenier, L. and Havelaar, A.H.

(1997) Assessment of the risk of infection of Cryptosporidium or

Giardia in drinking water from a surface water source. Water

Research 31(6), 1333±1346.

Ward, R.L., Bernstein, D.I., Young, E.C., Sherwood, J.R., Knowlton,

D.R. and Schiff, G.M. (1986) Human rotavirus studies in volun-

teers: Determination of infectious dose and serological response to

infection. Journal of Infectious Diseases 154, 871±880.

Will, R.G., Ironside, J.W., Zeidler, M., Cousens, S.N., Estibeiro, K.,

Alperovitch, A., Poser, S., Pocchiari, M., Hofman, A. and Smith,

P.G.3 (1996) A new variant of Creutzfeldt-Jakob disease in the UK.

Lancet 347, 921±925.

Williams, T. and Meynell, G.G. (1967) Time-dependence and count-

dependence in microbial infection. Nature 214, 473±475.



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Developments in microbiological risk assessment for drinking water