Detecting DNA-protein Interactions Xinghua Lu Dept Biomedical Informatics BIOST 2055.
BIOST/STAT 578 A Statistical Methods in Infectious...
Transcript of BIOST/STAT 578 A Statistical Methods in Infectious...
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BIOST/STAT 578 A Statistical Methods in Infectious Diseases
Lecture 16February 26, 2009
Cholera: ecological determinants and vaccination
![Page 2: BIOST/STAT 578 A Statistical Methods in Infectious ...courses.washington.edu/b578a/slides/slides-id09-16.pdf · Lecture 16 February 26, 2009 Cholera: ecological determinants and vaccination.](https://reader036.fdocuments.in/reader036/viewer/2022071004/5fc0cf70e527fc14472449f2/html5/thumbnails/2.jpg)
![Page 3: BIOST/STAT 578 A Statistical Methods in Infectious ...courses.washington.edu/b578a/slides/slides-id09-16.pdf · Lecture 16 February 26, 2009 Cholera: ecological determinants and vaccination.](https://reader036.fdocuments.in/reader036/viewer/2022071004/5fc0cf70e527fc14472449f2/html5/thumbnails/3.jpg)
Latest big epidemic in Zimbabwe
![Page 4: BIOST/STAT 578 A Statistical Methods in Infectious ...courses.washington.edu/b578a/slides/slides-id09-16.pdf · Lecture 16 February 26, 2009 Cholera: ecological determinants and vaccination.](https://reader036.fdocuments.in/reader036/viewer/2022071004/5fc0cf70e527fc14472449f2/html5/thumbnails/4.jpg)
Support• International Vaccine Institute• National Institute of Allergy and Infectious
Diseases ’Epidemiology and Ecology of Vibrio cholerae in Bangladesh’ grant 5R01AI039129-08
• National Institute of General Medical Sciences MIDAS grant 5U01GM070749-02– “Containing Bioterrorist and Emerging Infectious
Diseases”
![Page 5: BIOST/STAT 578 A Statistical Methods in Infectious ...courses.washington.edu/b578a/slides/slides-id09-16.pdf · Lecture 16 February 26, 2009 Cholera: ecological determinants and vaccination.](https://reader036.fdocuments.in/reader036/viewer/2022071004/5fc0cf70e527fc14472449f2/html5/thumbnails/5.jpg)
Ecological & Epidemiological Publications
• Longini, I.M., Yunus, M., Zaman, K., Siddique, A.K., Sack, R.B. and Nizam, A.: Epidemic and endemic cholera trends over thirty-three years in Bangladesh. Journal of Infectious Diseases 186, 246-251 (2002).
• Sack, R.B., Siddique, K., Longini, I.M., et al.: A four year study of the epidemiology of Vibrio cholerae in four rural areas in Bangladesh. Journal of Infectious Diseases 187, 96-101 (2003).
• Huq, A., Sack, R.B., Nizam, A., Longini, I.M., et al.: Critical factors influencing the occurrence of Vibrio cholerae in the environment of Bangladesh. Applied and Environmental Microbiology 17, 4645-4654 (2005).
• Longini, I.M., Nizam, A., Ali, M., Yunus, M., Shenvi, N. and Clemens, J.D.: Controlling endemic cholera with oral vaccines. Public Library of Science (PloS), Medicine 4 (11) 2007: e336 doi:10.1371/journal.pmed.0040336
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Ecology of Cholera
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Cholera Vibrios
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Copepods
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Humans
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Ecology of Cholera in Rural Bangladesh
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Support• National Institute of Allergy and Infectious
Diseases grant R01AI039129– “Epidemiology and Ecology of Vibrio cholerae in
Bangladesh”
• National Institute of General Medical Sciences MIDAS grant 5U01GM070749– “Containing Bioterrorist and Emerging Infectious
Diseases”
• International Vaccine Institute, Seoul Korea
![Page 12: BIOST/STAT 578 A Statistical Methods in Infectious ...courses.washington.edu/b578a/slides/slides-id09-16.pdf · Lecture 16 February 26, 2009 Cholera: ecological determinants and vaccination.](https://reader036.fdocuments.in/reader036/viewer/2022071004/5fc0cf70e527fc14472449f2/html5/thumbnails/12.jpg)
Ecology of Cholera in Rural Bangladesh
• 1997 – 2001: Four sites
• 2004 – 2008: Two sites
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Surveillance Sites In Bangladesh
Mathbaria
SunderbansSunderbans
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Surveillance Sites In Bangladesh
Mathbaria
SunderbansSunderbans
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Rainfall /Water Volume /Water Depth
Concentration OfOrganic Matter
Sunshine
Phyto-plankton
CO2
pH V. cholerae inEnvironment
Salinity Nutrients
Cholera inHumans
Temperature/Season
Dissolved O2
-+
+
+
+
++
+
++
+
+
+
-
+
?
Zoo-plankton
+
+
+
+
Hypothesized Associations
+
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0
2 0 0
4 0 0
6 0 0
8 0 0
1 0 0 0
1 2 0 0
C E C E C E C E C E C E C E C E C E C E C E C E B C E B C E B C E B C E B C E B
I n a b a O g a w a B e n g a l
1 9 6 6 1 9 6 9 1 9 7 2 1 9 7 5 1 9 7 8 1 9 8 1
1 9 8 2 1 9 8 5 1 9 8 8 1 9 9 1 1 9 9 4 1 9 9 7
Cas
esC
ases
0
2 0 0
4 0 0
6 0 0
8 0 0
1 0 0 0
1 2 0 0
C E C E C E C E C E C E C E C E C E C E C E C E C E C E C E C E
I n a b a O g a w a
C l a s s i c a l V . c h o l e r a e O 1 E l T o r V . c h o l e r a e O 1
C l a s s i c a l a n d E l T o r V . c h o l e r a e O 1
E l T o r V . c h o l e r a e O 1a n d V . c h o l e r a e O 1 3 9E l T o r V . c h o l e r a e O 1
Source: Longini, I.M., et al., J Infect Dis 186, 246-251 (2002).
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Average monthly number cholera cases over the 33
year period 1966-1998, Matlab, Bangladesh.
0102030405060708090
100
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Month
Ave
rage
Num
ber
of C
ases
Source: Longini, I.M., et al., J Infect Dis 186, 246-251 (2002).
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-0.2
-0.1
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
1 5 10 15
Total
Lag (months)
Aut
ocor
rela
tion
95% Confidence Limits
Correlogram for total cholera cases over the 33 year period 1966-1998, Matlab, Bangladesh
Source: Longini, I.M., et al., J Infect Dis 186, 246-251 (2002).
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Correlogram for Inaba and Ogawa serotypes over the 33 year period 1966-1998, Matlab, Bangladesh
-0.2
-0.1
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
1 5 10 15
Inaba
Lag (months)
Aut
ocor
rela
tion
95% Confidence Limits
-0.2
-0.1
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
1 5 10 15
Ogawa
Lag (months)
Aut
ocor
rela
tion
95% Confidence Limits
Source: Longini, I.M., et al., J Infect Dis 186, 246-251 (2002).
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El Tor cholera with Classical ToxinDehydration status of V. cholerae O1 biotype El Tor infected patients in Bakerganj:
1998 - 2001 and 2004 - 06
33.3
46.9
40
30.8
53.3
67.9
78.8
0
10
20
30
40
50
60
70
80
90
1998 (n=33) 1999 (n=32) 2000 (n=15) 2001 (n=13) 2004 (n=30) 2005 (n=28) 2006 (n=52)
Years
Perc
enta
ge
NoneSomeSevere
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8th Cholera Pandemic
• El Tor vibrio with Classical toxin
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1997 – 2001
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2004 – 2008
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• Simultaneous clinical and environmental surveillance every 15 days, at four sites:
- began in March, 1997 at Matlab and Chhatak
- began in June, 1997 at Bakerganj and Chaugaucha
Study Design
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Methods: Clinical Surveillance
• Each site visited for three days by two physicians
• All patients seen with watery diarrhea admitted into study
• Stool culture for V. cholerae
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Four surface waters (ponds, lakes, rivers) sampled at each clinical site
• V. cholerae identificationCultureDNA probes to identify cholera toxin-producing organisms
• Zooplankton and phytoplankton, identification & enumeration
• Environmental parameters (physical, coliforms)
Environmental Surveillance
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Methods: Statistical Analyses
Goal: Build a regression model to
- identify environmental variables that are associated with occurrence of cholera cases in humans, quantify associated risk
- identify time lag between changes in environmental variables and associated changes in # of cholera cases
Quantifying Associations Between Environmental Variables and Cholera Outbreaks
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Methods: Statistical Analyses
• Initial screening: lagged correlations between # of cholera cases & environmental variables
• Further screening: Stepwise regression of # of cases on lagged environmental variables
• Poisson regression of # of cholera cases on selected environmental variables; risk ratios quantifying change in risk of cholera associated with changes in environment.
Quantifying Associations Between Environmental Variables and Cholera Outbreaks
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0
1 0
2 0
3 0
4 0
5 0
M a r'9 7 J u n S e p D e c
M a r'9 8 J u n S e p D e c
M a r'9 9 J u n S e p D e c
M a r'0 0 J u n S e p D e c
O 1 3 9 ( n = 5 6 ) O 1 ( n = 7 9 ) D i a r r h e a
0
1 0
2 0
3 0
4 0
5 0O 1 3 9 ( n = 1 0 8 ) O 1 ( n = 2 9 6 ) D i a r r h e aMatlab
Bakergonj
Cholera and Diarrhea Cases Over Time#
Cas
es#
Cas
es
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# C
ases
0
1 0
2 0
3 0
4 0
5 0
M a r'9 7 J u n S e p D e c
M a r'9 8 J u n S e p D e c
M a r'9 9 J u n S e p D e c
M a r'0 0 J u n S e p D e c
O 1 3 9 (n = 8 ) O 1 (n = 2 9 ) D ia r r h e a
0
1 0
2 0
3 0
4 0
5 0O 1 3 9 ( n = 6 ) O 1 ( n = 8 5 ) D i a r r h e aChhatak
Chaugacha
# C
ases
Cholera and Diarrhea Cases Over Time
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Results: Environmental Surveillance
Variable n mean1 max1 % +
Copepod Count 1022 1.7 4.4 54
Cyanobact. Ct. 1042 4.3 8.1 72
Probe Count 1013 1.0 4.5 26
Fecal Colif. Ct. 991 1.4 4.5 96
_______________________________________1. Log scale
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Results: Environmental Surveillance
Variable n mean (std) min. max.
Conductivity(μS) 1038 243 (220) 15 1568
Water Temp (OC ) 1038 28 (4) 16 38
Water Depth (ft) 1035 8 (6) 1 60
Air Temp. (OC ) 1038 28 (5) 15 39
pH 1029 7 (1) 5 9
Diss.O2(mg/l) 658 4 (4) 0 53
Salinity(ppt) 1008 .1 (.1) 0 1
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0
5
10
15
20
25
30
Mar'97
Jun Sep Dec Mar'98
Jun Sep Dec Feb'99
May Aug Nov
Cho
l. C
ases
.
050100150200250300350400
Con
duct
ivit
y (u
S) .
O139 O1 Conductiv ity
Lag Correlation Lag Correlation
No lag 0.54 6 Weeks 0.43
2 Weeks 0.58 8 Weeks 0.15
4 Weeks 0.47
Cholera Cases and Lake Water ConductivityOver time in Bakerganj
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0
5
10
15
20
25
30
Mar'97
Jun Sep Dec Mar'98
Jun Sep Dec Mar'99
Jun Sep Dec
Cho
l. C
ases
0
2
4
6
8
10
12
Wat
er D
epth
(ft)
O139 O1 Water Depth
Lag Correlation Lag Correlation
No lag -0.28 6 Weeks -0.43
2 Weeks -0.49 8 Weeks -0.38
4 Weeks -0.43
Cholera Cases and Pond Water DepthOver time in Bakerganj
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0
5
1 0
1 5
2 0
2 5
3 0
M ar'97
Jun S ep D ec M ar'98
Jun S ep D ec M ar'99
Jun S ep D ec
Cho
l. C
ases
.
0
0 .5
1
1 .5
2
2 .5
Pro
be C
ount
.
(log
10)
O 1 3 9 O 1 C o nduc tiv ity
Lag Correlation Lag Correlation
No lag 0.02 6 Weeks 0.07
2 Weeks 0.15 8 Weeks 0.27
4 Weeks 0.10
Cholera Cases and Lake Water Probe ResultsOver time in Matlab
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Lagged Poisson Regression
,...)|ln(21 22110 kijijij kijtkijijtijijtijijijtit XXXX τττ ββββμ −−− ++++=
t ≥ max{τ1ij , τ2ij ,…, τkij}.
Let Yit be the number of reported cholera cases at time t, in area i. We assume that Yit follows a Poisson distribution with mean μit.
Xijt is the jth predictor at time t, in area i.
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Regression results
• RRij(☺) = exp( )– goes with a lagged Xij
– change in Xij
• Predictions and credibility intervals constructed using MCMC methods for Poisson regression
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Results: Poisson Regression Bakergonj River Predictors
Risk Ratio forVariable (lag1) Δ change of Δ (95% CI)
Conduct. (8) +150μS 1.3 (1.2, 1.3)
Copepods (0) +10 1.4 (1.2, 1.7)
______________________________________1. Lag, in weeks, between a change of of Δ units in the
environmental variable and a subsequent change in the number of cholera cases.
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Poisson Regression Results: Bakergonj Lake 2 Predictors
Risk Ratio forVariable (lag) Δ change of Δ (95% CI)
Conduct. (4) +150μS 4.1 (2.6, 6.6)
PH (8) +1 1.7 (1.3, 2.2)
Cyanobact. (2) +10 1.9 (1.6, 2.3)
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Poisson Regression Results: Bakergonj Pond Predictors
Risk Ratio forVariable (lag) Δ change of Δ (95% CI)
Water Depth (2) -2 ft. 2.5 (1.9, 3.3)
Copepods (2) +10 2.2 (1.7, 3.0)
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Bakergonj Pond Predictors Water Depth (2) and Copepods (2)
0
5
10
15
20
25
30
Mar'97
May Jul Sep Nov Jan'98
Mar May Jul Sep Nov Jan'99
Mar May Jul Sep Nov
Obs
. Cas
es.
0
5
10
15
20
25
30
Pred
. Cas
es.
O139 O1 Predicted 95% Upper CI
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0
5
10
15
20
25
Jun '97
Aug Oct Dec Feb '98
Apr Jun Aug Oct Dec Feb '99
Apr Jun Aug Oct Dec Feb'00
Apr Jun Aug Oct Dec
# C
hole
ra C
ases
Observed Predicted 95% Upper Pred. Limit
One month prediction in Bakerganj lake using water temperature, ctx gene probe count, conductivity, and rainfall
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Summary: I
• Both V. cholerae O1 and O139 are widespread in Bangladesh
• Seasonal patterns of cholera are observed, but are not always identical in different locations
• Cholera outbreaks in different geographic areas may be synchronous
• Not all diarrhea outbreaks are cholera
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Summary: II
• The main environmental predictors of cholera outbreaks were:
Conductivity
Water depth
Concentrations of copepods
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Controlling Endemic Cholera With Killed Oral Vaccines
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RATIONALE
• Advances in dehydration therapy make case fatality rate low
• Still, estimated 150,000 deaths per year in most impoverished countries
• Licensed, oral killed whole-cell cholera vaccines (OCV) have been available for over a decade– 70% efficacy against disease– 2 years protection
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“The role of OCVs as an additional public health tool to improve cholera control activities seems to be a promising strategy that needs to be further defined, especially for endemic settings.”4
4. Weekly Epidemiological Record, 5 August, 2005. World Health Organization.
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Introduction• Studies have shown that
orally administered killed cholera vaccines are safe and protective
• Vaccines have not been adopted for use in most endemic regions due to cost and efficacy concerns
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Recent Analysis
• Mid 1980’s randomized vaccine trial with OCV in Matlab, Bangladesh– 183,826 subjects– Current GIS mapping– Ali, M et al. Herd immunity conferred by killed oral cholera
vaccines in Bangladesh: a reanalysis. Lancet 366, 44 - 49 (2005).
– Durham, L.K., Longini, I.M., Halloran, et al.: Estimation of vaccine efficacy in the presence of waning: Application to cholera vaccines. American Journal of Epidemiology 147, 948-959 (1998).
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Source: Durham, L.K., Longini, I.M., Halloran, M.E., Clemens, J.D., Nizam, A. and Rao, M.: Am J Epidem 147, 948-959 (1998).
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Source: Durham, L.K., Longini, I.M., Halloran, M.E., Clemens, J.D., Nizam, A. and Rao, M.: Am J Epidem 147, 948-959 (1998).
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Endemic Cholera
• Cholera always present• Triggering events cause outbreaks
– Sack RB et al. . A 4-Year Study of the Epidemiology of Vibrio cholerae in Four Rural Areas of Bangladesh. J Infect Dis, (2003).
– Huq et al. Critical factors influencing the occurrence of Vibrio cholerae in the environment of Bangladesh. Applied and Environmental Biology (2005).
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Goals of Simulation Model• Calibrate to historical attack rate and vaccine
effectiveness data
• Simulate use of cholera vaccine at various coverage levels, study effectiveness measures
• Longini, I.M., Nizam, A., Ali, M., Yunus, M., Shenvi, N., Clemens, J.D.: Controlling endemic cholera with oral vaccines. (In preparation)
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Simulator Overview
Input Population
Code Outputs
•Population of Matlab in 1985
•ANSI c code models cholera natural history and community level transmission
•Developed on unix. Portable
•1000 runs per simulation
• Illness attack rates
• Effectiveness measures
•Spatial distribution of cholera cases
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Simulator Elements• Disease natural history model and parameters
• Community-level transmission of cholera infection
• Matlab population demographics (age, gender, location, travel within Matlab)
• Historical illness attack rate data for model calibration
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Cholera Natural History
Susceptible LatentIll
AsymptomaticRecovered/Removed
In each subpopulation, on any given day of the epidemic, there is a probability of infection, determined by an infection function (next slide)
90%
10%
1 day: 40%
2 days: 40%
3-5 days: 20%
Uniform distribution 7-14 days
In each subpopulation, on any given day of the epidemic, there is a probability of infection, determined by an infection function (next slide)
Additional assumptions:
•Ill shed at 10 times the rate of asymptomatics
•Working males:
• circulate >= 1 day
•Pr(withdrawal after ill)= 0.75
Uniform distribution 7-14 days
1 day: 40%
2 days: 40%
3-5 days: 20%
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Infection FunctionThe probability that a susceptible person will be infected in a particular location on day t is:
Wherep = transmission probabilityӨ = 1 – vaccine efficacy against susceptibility (VES)x = 1 if susceptible is vaccinated, 0 if unvaccinatedb = seasonal boost factor for first monthnuv(t) = # unvacc. infectious peoplenv(t) = # vacc. infectious peopleФ = 1 – vaccine efficacy against infectiousness (VEI)
( ) ( )1 (1 ) (1 )uv vn t n tx xf bp bpθ θ φ= − − −⎡ ⎤⎣ ⎦
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Model CalibrationModel input parametersp: 0.000009b: 10VES: 0.7VEI: 0.5
Number of initial infectives: 5
Probability of withdrawal given ill: 0.75
Probability asymptomatic: 0.9
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Population Characteristics
• 183,826 subjects from Matlab• 50.5% Female 49.5% Males• Geographic map
– Bari code– X,Y coordinates– Age on 1/1/1985
• Vaccinated where children 2 – 15 years old and women > 15 years old.
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Population CharacteristicsMatlab “Grid”• Matlab area mapped to 64 ‘sub-regions’• Each subject mapped to one of the sub-
regions based on the GIS location
Matlab
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Population Characteristics
Distribution of Population Across the Grid
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Population CharacteristicsConnectivity Between Sub-regions• Males over 16 years old, and 50% of males
between 14 -16 years old were randomly assigned a work sub-region according to the following distance function:
– 55% work and reside in same sub-region– 35% work 4-10km away from residence
sub-region– 10% work >10km away4
4. Distance function derived from time traveled to school reported in Matlab Health and Socioeconomic Survey dataset, 1996. http://www.icpsr.umich.edu/
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Vacf
AR1v
Nonvac1-f
AR1u
Nonvac
AR2u
Overall
Direct Indirect
Total
Intervention Population: 1
Control Population: 2
Vaccine Effectiveness
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Vacf
AR1v
Nonvac1-f
AR1u
Overall
Direct Indirect
Total VEVE totaltotal = 1= 1-- (AR1v / AR2u)(AR1v / AR2u)
Intervention Population: 1
Control Population: 2
Vaccine Effectiveness
VEVEoveralloverall = 1= 1-- (AR(AR1ave1ave/ AR/ AR2u2u))
VEVEdirectdirect = 1= 1-- (AR(AR1v1v / AR/ AR1u1u)) VEVEindirectindirect = 1= 1-- (AR(AR1u1u / AR/ AR2u2u))
Nonvac
AR2u
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Vaccine Effectiveness
VEdirect = 1- (AR1v / AR1u)VEindirect = 1- (AR1u / AR2u)VEtotal = 1- (AR1v / AR2u)
VEoverall = 1- (AR1ave/ AR2u)
where AR1ave = f AR1v + ( 1 – f) AR1u
Halloran, et al., Am J Epidemiol 146, 789-803 (1997)
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Vacf1
AR1v
Nonvac1-f1
AR1u
Overall
Direct Indirect
Total
Population: 1 Population: 2
Vaccine Effectiveness Gradient
Vacf2
AR2v
Nonvac1-f2
AR2u
Direct
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Model Calibration• Annual autumn/winter outbreaks in Matlab
0102030405060708090
100
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Month
Ave
rage
Num
ber
of C
ases
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Vaccination Coverages, Average Incidence Rates and Direct Effectiveness (Calibration Runs)
Mean Cases/1000 (95% CI)
Vaccination Coverage (%)
Placebo
Vaccinated
Mean Direct Effectiveness (%)
(95% CI) Target
Population Overall
Population
Observed
Simulated
Observed
Simulated
Observed
Simulated 14 9 7.0
(6.5, 7.5) 7.8
(1.9, 14.8) 2.7
(1.9, 3.5) 2.8
(0.5, 6.1) 62 65
(52, 77)
31 20 5.9 (5.4, 6.4)
4.7 (0.9, 10.2)
2.5 (2.0, 3.0)
1.7 (0.3, 3.8)
58 65 (55, 76)
38 25 4.7
(4.2, 5.2) 3.8
(0.8, 8.6) 1.6
(1.2, 2.0) 1.3
(0.2, 3.4) 67 65
(54, 77)
46 30 4.7 (4.2, 5.2)
2.8 (0.5, 6.8)
2.3 (1.9, 2.7)
1.0 (0.1, 2.5)
52 66 (54, 79)
58 38 1.5
(1.2, 1.8) 1.8
(0.3, 4.8) 1.3
(1.0, 1.6) 0.6
(0.1, 1.8) 14 66
(51, 80)
χ² goodness-of-fit test for frequency data p = 0.84
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0
50
100
150
0 30 60 90 120 150 1800
50
100
150
0 30 60 90 120 150 180
0
50
100
150
0 30 60 90 120 150 180
No Vaccination11.2 cases/1000
0
50
100
150
0 30 60 90 120 150 180
14% VaccinationUnvacc. 7.6 cases/1000Vacc. 2.7 cases/1000
58% VaccinationUnvacc. 1.8 cases/1000Vacc. 0.6 cases/1000
38% VaccinationUnvacc. 3.7 cases/1000Vacc. 1.3 cases/1000
Day Day
Cas
es/1
000
Cas
es/1
000
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Average Indirect, Total and Overall Effectiveness of Vaccination, and Cases Prevented 10,000 Per Doses
Mean Effectiveness (%)
(95%CI)
Vaccination Coverage (%)
Indirect
Total
Overall
Mean # Cases Prevented per 10,000 Doses
10 30 (-39, 83)
76 (47, 95)
34 (-30, 84)
50
30 70 (31, 93)
90 (76, 98)
76 (44, 95)
40
50 89 (72, 98)
97 (91, 99)
93 (82, 99)
30
70 97 (91, 99)
99 (97, 100)
98 (95, 100)
20
90 99 (98, 100)
100 (99, 100)
100 (99, 100)
20
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0102030405060708090
100
Vaccination Coverage (%)
Effe
ctiv
enes
s (%
)
10 30 50 70 90
Total
Overall
Indirect
0
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Recommendations• For endemic cholera
– Should have at least 50% coverage– Vaccinate people every two years– If vaccine is limited, conduct environmental
surveillance to target vaccination programs– Randomized community vaccine trial
• For epidemic cholera– Mobile stockpile of cholera vaccine– More work is needed to determine best vaccination
strategy • Simulations
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Randomized Community Trial
• Paired control and vaccinated communities (at least 10 pairs).
• Or at least a gradient in coverage• Could expand the WHO/IVI trial in
Mozambique to do this• Need study of environmental predictors of
cholera in Africa
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The End