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Transcript of Lecture 11 BSC 417. Outline More on sensitivity analysis –Spreadsheet on website –Examples and...
![Page 1: Lecture 11 BSC 417. Outline More on sensitivity analysis –Spreadsheet on website –Examples and in-class exercise Case analysis Discussion of Eisenberg.](https://reader031.fdocuments.in/reader031/viewer/2022032605/56649e725503460f94b7137a/html5/thumbnails/1.jpg)
Lecture 11
BSC 417
![Page 2: Lecture 11 BSC 417. Outline More on sensitivity analysis –Spreadsheet on website –Examples and in-class exercise Case analysis Discussion of Eisenberg.](https://reader031.fdocuments.in/reader031/viewer/2022032605/56649e725503460f94b7137a/html5/thumbnails/2.jpg)
Outline
• More on sensitivity analysis– Spreadsheet on website– Examples and in-class exercise
• Case analysis
• Discussion of Eisenberg et al. (2002) paper
• Eisenberg presentation on model
• Translating the model to Stella
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Assessing Risk from Environmental Exposure to
Waterborne Pathogens: Use of Dynamic, Population-
Based Analytical Methods and Models
26 February 2008The following is based on lecture material prepared by Prof. Joe Eisenberg, formerly of the University of California-Berkeley and now at the University of Michigan
Used with his permission
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Overview• Role of water in disease burden
– Water as a route of disease transmission
• Methods of risk estimation– Direct: intervention trials– Indirect: risk assessment
• Population-level risks– Example: the Milwaukee outbreak
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Importance of Waterborne Pathogens
Domestic: U.S. interest in water quality– 1993 Cryptosporidium outbreak
– Increasing number of disease outbreaks associated with water
– Congressional mandates for water quality
– (Safe Drinking Water Act)
– Emphasis on risk assessment and regulation
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Importance of Waterborne Pathogens
Worldwide: WHO interest in water quality
– Estimating GBD associated with water, sanitation, and hygiene
– Diarrheal diseases are a major cause of childhood death in developing countries.
– 3 million of the 12.9 million deaths in children under the age of 5 attributable to diarrheal disease
– Emphasis on intervention and control
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Pathways of Transmission• Person-person
– Mediated through fomites (e.g., phone, sink, etc.)
– Often associated with hygiene practices• Person-environment-person
– Mediated through water, food, or soil– Contamination can occur through
improper sanitation (example: sewage inflow into drinking water source or lack of latrines)
– Animals are often sources (Zoonotic pathogens)
– Exposure can occur through improper treatment of food or water
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The Disease Transmission Process
Risk estimation depends on transmission dynamics and exposure pathways
Animals
AgriculturalRunoff
DrinkingWater
2°Trans.
Recreational Waters
orWastewater reuse
Transport to other water sources
Food
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Approaches to Risk Estimation
• Direct approach: The intervention trial– Can be used to assess risk from drinking
water and recreational water exposures– Problems with sensitivity (sample size
issue)– Trials are expensive.
• Indirect approach: Mathematical models– Must account for properties of infectious
disease processes– Pathogen specific models– Uncertainty and variability may make
interpretation difficult.
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Approaches to Risk Estimation
• Combining direct and indirect approaches
–Models can define the issues and help design studies.
–Epidemiology can confirm current model structure and provide insight into how to improve the model.
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Approaches for Risk
Estimation: Direct estimates of waterborne
infectious illnesses• Surveillance: count waterborne infectious
illnesses – How can a waterborne disease outbreak be
distinguished from other outbreak causes (food, fomites, etc.)?
– What about endemic disease?
• Observational– Ecologic studies (e.g., serosurvey comparing
communities with and without filtration).– Time series (e.g., correlation between turbidity and
hospitalization data)
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Approaches for Risk
Estimation: Distinguishing waterborne GI disease from
other GI diseases• Methods for addressing the question– In a single community: a randomized,
blinded, placebo-controlled trial
– design provides an estimate of the effectiveness of a drinking water intervention.
• Basic study design: two groups– “Exposed” group = normal tap water.
– “Treated” group = use a water treatment device to provide water as pathogen-free as technically possible
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Approaches for Risk Estimation:
A Tap Water Intervention Trial• Enroll 1000 subjects
• 500 receive an active home water treatment device (and carry drinking water to work, etc. when practical)
• 500 receive a “placebo” home water drinking device (does nothing to change the water)
• Follow the subjects for one year with daily logs of GI illness
• Alternative design: Each household changes device type after 6 months.
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Approaches for Risk Estimation:
A Tap Water Intervention Trial• Placebo group (tap water):
– 90 illnesses over course of the study– “Rate” = 90 / 500
Rate in placebo group = 0.18 per person per year
• Treated group (active device):– 60 illnesses in the treated group (active
device)– “Rate” = 60 / 500
Rate in treated group = 0.12 per person per
year
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Approaches for Risk Estimation:
Epidemiologic Measures
•Relative Risk (RR) Incidence in exposed groupIncidence in unexposed group
Interpretation: the risk of disease in the tap water group is 1.5 times higher than that of the treated group
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Approaches for Risk Estimation:
Epidemiologic Measures
Attributable Risk (AR) Incidence in exposed – Incidence in unexposed
Interpretation: There are 6 excess cases of disease per 100 subjects receiving tap water
06.012.018.0 activetapwater IncidenceIncidence
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Approaches for Risk Estimation:
Epidemiologic MeasuresAttributable Risk Percent (AR%)
Excess cases in exposedIncidence in exposed
Interpretation: 33% of the cases of disease in the tap water group are due to water
33.018.0
06.0 tapwater
tapwater
Incidence
CasesExcess
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Approaches for Risk Estimation: Epidemiologic
Measures• To generalize beyond the cohort,
need an estimate of the community incidence.
• PAR: population attributable risk
• PAR%: population attributable risk %
• AR compares completely protected group with completely unprotected group.
• PAR incorporates intermediate exposure
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Approaches for Risk Estimation: Epidemiologic
Measures• Population attributable risk
• Incidence in the community–incidence in the unexposed
Interpretation: In the community, 2 excess cases of disease per every 100 subjects in the community
02.012.014.0 activeComm IncidenceIncidence
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Approaches for Risk Estimation: Epidemiologic
Measures• Population attributable risk percentage
Excess cases in the communityIncidence in the exposed
Interpretation: 14% of the cases of disease in the community are due to tap water
14.014.0
02.0 tapwater
Comm
Incidence
CasesExcess
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Approaches for Risk Estimation:
Tap Water Intervention Trials Trials in immunocompetent populations Canada (Payment)--challenged surface water
– AR = 0.35 (Study 1), 0.14-0.4 (Study 2) Australia (Fairley)--pristine surface water
– No effect Walnut Creek (UCB) – pilot trial
– AR = 0.24 (non-significant effect) Iowa (UCB)--challenged surface water
– No effect
Trials in sensitive populations HIV+ in San Francisco (UCB)--mixed sources Elderly in Sonoma (UCB)--intermediate quality surface
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Approaches for Risk Estimation:
Tap Water Intervention Trials• Davenport, Iowa study
– Comparing sham vs. active groups
– AR = - 365 cases/10,000/year (CI: -2555, 1825)
– Interpretation: No evidence of a significantly elevated drinking water risk
– Is the drinking water safe?
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Approaches for Risk Estimation:
Risk Assessment vs. Intervention TrialComparing estimates from a risk assessment
to randomized trial results (Eienberg et al. AJE, submitted)
Data collected during the intervention trial– Self-report illnesses from participants:
Weekly diaries– Source water quality: Cryptosporidium,
Giardia, enteric viruses– Drinking water patterns: RDD survey– Water treatment: B. subtilis, somatic
coliphage
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Approaches for Risk Estimation: Risk Assessment Model
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Approaches for Risk Estimation: Risk Assessment Model Model
Cryptosporidium Giardia Viruses
1. Source water
Concentration (organisms per liter) (Normal Mean (SD)*)
1.06 (2.24)
2.68 (24.20)
0.93 (3.00)
Recovery rate 0.40 0.40 0.48
2. Treatment efficiency (logs removal)
Sedimentation and filtration (Mean (SD)*)
3.84 (0.59)
3.84 (0.59)
1.99 (0.52)
Chlorination (Mean (SD) 0 3.5 (2.93) 4 (2.93)
3. Water Consumption
in liters (mean (SD) ‡)
0.094 (0.42)
0.094 (0.42)
0.094 (0.42)
4. Dose Response § : 0.004078 : 0.01982 ,: 0.26, 0.42
5. Morbidity Ratio# 0.39 0.40 0.57
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Approaches for Risk Estimation: Risk Assessment Results
Table 2. Summary of risk estimates (cases/10,000,yr)
Cases of Illness
Mean
Percentile (2.5, 97.5)
Cryptosporidium 2.1 (0.8, 3.5)
Giardia 3.4 (0.6, 15.5) Enteric viruses (disinfection = 4 log removal)
8.4 (0.2, 18.7)
Enteric viruses (disinfection = 4 log removal)
0 (0, 0.2)
Overall risk estimate: 14 cases/10,000/yr
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Approaches for Risk Estimation: Comparison/Conclusions
Risk Assessment Intervention Trials
Sensitivity Not relevant Low
Causal evidence Indirect Direct
Pathogen inclusion Few Many
Model Specification Adds uncertainty Not relevant
Transmission processes
Can be included* Only in a limited way
Distribution System effects
Can be included* Was included
Examining alternative control strategies
Yes No
Expense Low High
Time Fast Slow *Was not included in this study
Table 3. Comparison of risk assessment and intervention trials
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Microbial Risk Assessment • Two classes of risk assessment models
– Individual-based – Population-based
• Individual-based estimates– Risk estimates assume independence
among individuals within the population– Chemical risk paradigm– Focus is on direct risks– Probability of disease for a given individual– This probability can be either daily, yearly,
our lifetime.
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Microbial Risk Assessment
• Chemical risk paradigm–Hazard identification, exposure assessment, dose response, risk characterization
• Model structure
where P = probability that a single individual, exposed to N organisms, will become infected or diseased.
• Exposure calculation:
))(1(1
NP
dktko
dtecVN 101
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Microbial Risk AssessmentAlternative framework: risk estimates at the population level allow for the inclusion of indirect risks due to secondary transmission
Animals
AgriculturalRunoff
DrinkingWater
2°Trans.
Recreational Watersor
Wastewater reuse
Transport to other water sources
Food
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Microbial Risk AssessmentEisenberg et al. AJE 2005
• Transmission pathways – Example: a Cryptosporidium outbreak in Milwaukee
Wisconsin, 1993
• Competing hypotheses on the cause– Oocyst contamination of drinking water influent
coupled with treatment failure– Chemical risk paradigm may be sufficient (still
need to consider secondary transmission)– Amplification of oocyst concentrations in the
drinking water influent due to a person-environment-person transmission process
– Chemical risk paradigm cannot address this potential cause of the outbreak
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A model of disease transmission: The SIR model
• Mathematical modeling of a population where individuals fall into three main categories:– Susceptible (S)– Infectious (I)– Recovered (R)
• Different individuals within this population can be in one of a few key states at any given time– Susceptible to disease (S)– infectious/asymptomatic (I)– infectious/symptomatic (I)– non-infectious/asymptomatic; recovered (R)
• A dynamic model: individuals are moving from state to state over time
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The SIR model: key detailsThere are two sets of variables:• Variables describing the states
people are in– S=susceptible
– I=infectious
– R=non-infectious/asymptomatic
• Variables describing how many people are moving between these states (parameters)– Example: γ=Fraction of people in state R who
move to state S
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• S: Susceptible• I: Infectious (symptomatic+asymptomatic)• R: Non-infectious• W: Concentration of pathogens in the environment• β: Infection rate due to exposure to pathogen• δ: Fraction of people who move from state I to state R• γ: Fraction of people who move from state R to state S• Solid lines: Individuals moving from state to state• Dashed lines: Pathogen flows between individuals in different states
The SIR Model
ENVIRONMENTW
S R
I
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The SIR Model: slightly different version
The variables• X: susceptible• Y: infectious/asymptomatic• Z: non-infectious/asymptomatic• D: infectious/symptomatic• W: concentration of pathogens at the source• a: number of new susceptible individuals
migrating in
δ+μ
W
X Z0+ (W) (ρ)
Dρ σ
Y
λ
a μ
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The SIR Model: slightly different version (cont)
The parameters• ρ: fraction in state Y who move to state D• α: Fraction in state Y who move to state Z• σ: Fraction in state D who move to state Z• γ: Fraction in state Z who move to state X• δ: Fraction in state D who die• μ: Fraction who die of natural causes• λ: Numbers of pathogen shed per
infectious/asymptomatic individual• β0 : Baseline transmission rate• β : Infection rate due to pathogen
δ+μW
X Z0+ (W)
(ρ)
Dρ σ
Y
λ
a μ
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Dynamic Modeling of Disease Transmission: an
exampleXWXXZadt
dX )(0
• Remember: a derivative is a rate of change• X= the population of individuals susceptible to a
disease• dX/dt = rate of change in the susceptible
population• The equation describes individuals moving in
and out of the susceptible population• Each variable represents some number of
individuals moving – into the susceptible population (+) from some other
group, – out of the susceptible population (-) to some other
group
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Dynamic Modeling of Disease Transmission: an
exampleXWXXZadt
dX )(0
• a= number of susceptible individuals migrating into the population
• γZ =number of non-infectious/asymptomatic individuals migrating back into the susceptible population
• μX =Fraction of susceptible individuals who drop out of the susceptible population because they die of natural causes
• β0X =number of susceptible individuals who become infected and drop out of the susceptible population
• β(W)X =number of susceptible people becoming ill due to pathogen exposure and drop out of the susceptible population
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Analysis of Disease Transmission Models
• Traditional approaches to evaluating dynamics models are qualitative – Stability analysis, threshold estimates (Ro),
qualitative fits– Statistics rarely used to analyze output
• Methodological goal to obtain public health relevant estimates of the outbreak– Need to modify traditional statistical
techniques to address models with large number of parameters, sparse data, and collinearity
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Analysis of Disease Transmission ModelsLikelihood
Traditional likelihood methods
– Difficult to find maximum likelihood point in highly parameterized models.
– Confidence intervals are often not possible in complex likelihood spaces
Profile likelihood is an alternative option
– Fix a subset of the parameters across a grid of values.
– At each point in the grid the remaining parameters are maximized.
Bayesian techniques Practical for combining outbreak data with existing information about
parameters. Modifications required to deal with collinearities
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Model 1
Goals:To examine the role of person-person (secondary)
transmission
To estimate the fraction of outbreak cases associated with person-person (secondary) transmission
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Cryptosporidium Outbreak - Model Diagram
S(t)Susceptible E1 E2 Ek
IA(t) Infectious
(asymptomatic)
R(t) Removed
W(t)Environmental Transmission
...
Latently Infected IS(t)Infectious
(symptomatic)
+ S
S: SusceptibleW: Concentration of Pathogens in the EnvironmentIS: Symptomatic and Infectious
IA: Asymptomatic and Infectious
R: Immune/ Partially ProtectedSolid: Individual Flows from State to StateDashed: Pathogen Flows
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Analysis - Model 1Monte Carlo Markov Chain (MCMC) was used to generate
a posterior distribution.Two step procedure was used to address
collinearities of the parameter estimates– MCMC at profiled points– Second MCMC on draws from first MCMC
Cumulative incidence, I1, was produced by a random draw of the posterior
Cumulative incidence, I0, was produced by first setting bs=0 then obtaining a random draw of the posterior.
The attributable risk associated with secondary transmission was I1- I0
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Risk Attributable to Secondary Transmission
10% , 95% CI [6, 21]
0 0.1 0.2 0.3 0.4 0.50
100
200
300
400
500
600
700
Percent attributable risk
Fre
qu
enc
y
0 0.1 0.2 0.3 0.4 0.50
100
200
300
400
500
600
700
Percent attributable risk
Fre
qu
enc
y
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Model 2Goal:
To examine the role of person-environment-person transmission
To estimate the preventable fraction due to an increase in distance between wastewater outlet and drinking water inlet
Examine preventable fraction as a function of transport time parameter, d
– Where d is a surrogate for the potential intervention of moving the drinking water inlet farther from the wastewater outlet
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Cryptosporidium Outbreak- Model Diagram
S(t)Susceptible E1 E2 Ek
IA(t) Infectious
(asymptomatic)
R(t) Removed
W(t)Environmental Transmission
...
Latently Infected IS(t)Infectious
(symptomatic)
+ S
S: SusceptibleW: Concentration of Pathogens in the EnvironmentIS: Symptomatic and Infectious
IA: Asymptomatic and Infectious
R: Immune/ Partially ProtectedSolid: Individual Flows from State to StateDashed: Pathogen Flows
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Analysis - Model 2
• Estimate the likelihood for different values of d, ranging from 1 - 40 days.
• Estimate the attack rate (AR) for the MLE parameters
• Estimate the AR for different values of d, keeping all other parameters constant at their MLE values.
• Plot PFd = 1 - ARMLE / ARd
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Profile Likelihood of the Delay Parameter
0 5 10 15 20 25 30 35 40-2480
-2475
-2470
-2465
-2460
-2455
-2450
Days
Lo
g L
ike
lih
oo
d
0 5 10 15 20 25 30 35 40-2480
-2475
-2470
-2465
-2460
-2455
-2450
Days
Lo
g L
ike
lih
oo
d
MLE for the time between contamination of sewage and exposure from drinking water tap
was 11 days (95% CI [8.3, 19])
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Preventable Fraction As a Function of Delay Time
Predicting the public health benefits of moving the drinking water inlet
0 5 10 15 20 25 30 35 40 450
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
Days
Pre
ve
nta
ble
Fra
cti
on
0 5 10 15 20 25 30 35 40 450
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
Days
Pre
ve
nta
ble
Fra
cti
on
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Conclusions• Secondary transmission was small.
– Best guess is 10%, most likely less than 21%
– Consistent with empirical findings of McKenzie et al.
– Kinetics of the outbreak in Milwaukee were too quick to be driven solely by secondary transmission
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Conclusions• Person-water-person transmission as
the main infection pathway has not been well studied
– Few data exist that examines person-water-person transmission
– Studies have demonstrated a correlation between cases of specific viral serotypes in humans and in sewage
– Provides information on a potentially important environmental intervention
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Conclusions: MethodsAnalyzing disease transmission models using
statistical techniquesAllows inferences about parameters that are
interesting and relevant
– Can get at posterior distribution that allows for calculation of relevant public health measures
Requires the modification of existing techniques
– Profile likelihood to deal with large numbers of parameters
– Bayesian estimation techniques to address the co-linearity.
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ConclusionsRisk assessments should use models that
can integrate relevant informationHealth data
–Epidemiology–Basic biology
Environmental data–Water quality–Fate and transport
Need a population perspective–Model-based approach