journa l homepage: www.e lsev ier .com/ locate /vacc ine
odeling spatial heterogeneity of disease risk and evaluation of thempact of vaccination
ohammad Alia,∗, Michael Emchb, Mohammad Yunusc, John Clemensa
International Vaccine Institute, San 4-8, Bongcheon 7-dong, Kwanak-ku, Seoul 151-818, Republic of KoreaDepartment of Geography, University of North Carolina, Chapel Hill, USAICDDR,B, GPO Box 128, Mohakhali, Dhaka 1000, Bangladesh
r t i c l e i n f o
rticle history:eceived 6 February 2009
a b s t r a c t
We reanalyzed data from a phase III trial for the killed oral cholera vaccine to test two hypotheses: therewill be a greater impact of the vaccine in areas where there is a low force of infection, and the spatial
eceived in revised form 25 March 2009ccepted 26 March 2009vailable online 17 April 2009
eywords:patial analysis
pattern of disease transmission will change after a mass vaccination campaign. Spatial regression wasused to test these hypotheses accounting for spatial heterogeneity in disease and vaccine coverage. Theresults of the analyses confirm both hypotheses. The paper also shows how spatial analysis can be usedto understand the impact of vaccination when there are spatially heterogeneous disease distributions.
A vaccine trial is typically designed to evaluate the efficacy ofvaccine by measuring the disease incidence difference between
ntervention and control groups after the introduction of the vac-ine. The force of infection and community level vaccine coveragepercent of people in an area who are vaccinated) might modify thefficacy but they are usually not considered in trials. Since a vac-ine can only partially protect a person against the target disease,t may not work well in areas where the force of infection is highompared to areas where the force of infection is low. Due to thendirect effect of the cholera vaccine [1], areas with high vaccineoverage can show a significant reduction of the disease comparedo areas where vaccine coverage is low. Addressing spatial differ-nces in the force of infection and vaccine coverage is, therefore,mportant in the evaluation of vaccine protection. Also, it is impor-ant to understand the impact of a mass vaccination program inifferent communities and to identify high risk areas of the disease,o that an effective control program can be designed to reduce theverall burden of the disease.
Maps of spatial variation in disease occurrence are useful toolsor identifying areas with potentially elevated risk, determiningpatial trends, and formulating and validating etiological hypothe-es for diseases [2]. By mapping spatial patterns of the diseasen pre- and post-vaccination periods, the changes in the spa-
tial process of the disease can be delineated, which may helpevaluate the impact of vaccination. Using a geographic infor-mation system (GIS), spatial and contextual factors associatedwith disease risk [3] can be addressed in a vaccine evaluationprogram.
In this paper, we used three data sets: (a) killed oral cholera vac-cination database for a trial conducted in 1985, (b) a longitudinalhealth and demographic database of the study population, and (c) aspatial database of the study area to evaluate the impact of vaccina-tion in a small geographic unit. We hypothesize that (i) the vaccinehas a greater impact in areas with a lower force of infection com-pared to areas with a high force of infection; (ii) the spatial patternof disease transmission will be changed after a mass campaign forvaccination. The analysis was done by comparing data from pre-and post-vaccination periods.
2. Materials and methods
2.1. The study site and trial
The cholera vaccine trial was conducted in Matlab, a researchsite of the International Centre for Diarrheal Disease Research,Bangladesh (ICDDR,B), which is approximately 50 km south-east ofDhaka, the capital of Bangladesh [4,5]. The study area is 184 km2,
which is bisected by the Dhonagoda River in almost two equalportions. About 85% of the population is Muslim and the rest isHindu. It is a densely populated area with about 1000 people persquare kilometer. Like other rural areas of Bangladesh, educationalattainment is low.
Initiated in 1966, the health and demographic surveillance sys-em (HDSS) keeps reliable records of vital demographic events ofhe study area population [6]. Each individual is identified by a reg-stration number given in the surveillance system. The demographicata of the study period were obtained from the dynamic popula-ion database, and religion and educational levels were obtainedrom a 1984 socio-economic survey. Cholera data were obtainedrom ICDDR,B diarrhea hospital records. The ICDDR,B hospital is thenly diarrhea treatment center in the surveillance area. There wasne hospital and two community-operated treatment centers forhe management of diarrhea patients during the study period. Allatient services are free and there is a free boat service for patientshat need to be transported to the hospital on an emergency basis.tool samples are collected from all patients who are admitted tohe hospital who live in the ICDDR,B study area and the samples arecreened for enteric pathogens including Vibrio cholerae in the lab-ratory. Cholera hospitalization in Matlab varies by year. Between983 and 2003, we observed a minimum of 0.23/1000 choleraospitalizations in 1990 and a maximum of 5.49/1000 cholera hos-italizations in 1993 [7].
The Matlab population was 188,152 when the trial began. Allhildren aged 2–15 years and females over 15 years were indi-idually randomized to receive a three-dose regimen of eithersubunit-killed whole cell (BS-WC) oral cholera vaccine; killedhole cell-only oral cholera vaccine (WC) (which contains the same
ellular constituents as BS-WC, but lacks BS); or Escherichia coli K12lacebo. The vaccination was carried out from January to May, 1985.f the 124,035 persons in the age and gender groups targeted for
he trial, 50,499 received two or three doses of BS-WC or WC, and5,252 received two or three doses of placebo after giving verbal
nformed consent. At 1-year follow-up, vaccine protective efficacyPE) was 62% for BS-WC and 53% for WC [4]. Neither vaccine wasssociated with significant side-effects [8,9].
.2. Definitions
To define the primary outcome for the analysis, we considered aatient who presented for treatment of diarrhea (defined as at leasthree loose or liquid motions during the 24 h before presentation forare, or 1–2 or an indeterminate number of loose or liquid motionsn conjunction with at least two signs of dehydration) and V. cholerae1 isolated from a fecal (stool/rectal swab) specimen.
.3. Unit of analysis and study variables
Villages were the unit of analysis. There were 149 villages inhe Matlab study area during the time of vaccination. River ero-ion destroyed seven villages in the interval between vaccinationn 1985 and mapping the study area in 1994. The mass campaignas not conducted in six villages leaving 136 villages for analysis.e grouped the BS-WC and WC vaccines in the analysis because
hey were identical in composition except for the inclusion of thesubunit and because the two vaccines conferred similar levels
f protective efficacy [4,5]. Moreover, because previous analyses ofhe trial showed that protection by two doses of each vaccine wasimilar to protection by three doses [4,5], we considered an indi-idual to be vaccinated if she/he had completely ingested an initialose and had completely or almost completely ingested at least onedditional dose.
This study compares disease patterns in pre- and post-accination periods. The pre-vaccination period used in this
nalysis is January 1, 1984 to December 31, 1984 and the post-accination period is June 1, 1985 to May 31, 1986. Because ouroal was to evaluate incidence of cholera hospitalization in stable,eographically defined population, we considered 1 year for bothre- and post-vaccination periods to limit the effect of population
2009) 3724–3729 3725
movement. We believe that 1 year is brief enough that the popu-lations can be assumed to have been stable. The primary outcomefor this study is the proportion of patients who visited one of thethree health centers and had V. cholerae 01 in their fecal specimen.We evaluated risk for cholera hospitalization among all individualsresiding in villages before and after the vaccination.
We computed village level vaccine coverage as the number ofpeople who received at least two doses of BS-WC or WC divided bythe number of individuals living in the village on January 1, 1985. Wealso computed the percentage of Hindus, educational attainment,and population density by village because they were previouslyfound to be related to cholera incidence [10,11]. There were insignif-icant differences in the population size during the two time periods,thus we computed percent Hindus and percent educated people liv-ing in villages using the 1985 data. We defined the threshold of aneducated person as 4 years of formal schooling. Village level edu-cational attainment considered all people 6 years and older in thestudy area. Population density (in 100 km2) was computed for both1984 and 1985 data. Distances from village center to the nearestcholera treatment center and to the nearest river were computedas linear distances using the GIS.
2.4. Local empirical Bayes map
Since spatial patterns in the resulting maps may fail to delineatetrue trends in underlying risk caused by the instability of observedrates in villages having small numbers of people at risk, we usedlocal empirical Bayes estimation for mapping spatial patterns ofcholera hospitalization during pre- and post-vaccination period.The approach provides stable estimates retaining geographic anddemographic resolution [12,13]. When using this method, the priorassumes geographically structured heterogeneity (i.e. clustering),and the model incorporates the geographical structure of thedisease. Each relative risk is conditionally independent of all theothers except a small set of villages, defined by adjacency (sharinga common boundary). Therefore, the relative risk of a village isstrongly influenced by the estimates in neighboring villages (in ourcase, only the 1st order neighbor is considered), and only indirectlyinfluenced by the estimates in the remaining villages of the map.The estimates are thus adjusted towards a local rather than a globalmean value.
2.5. Spatial analysis
An ordinary least square (OLS) regression proceeded by a spatialautoregressive [14] lag model [15–17] implemented in GeoDA ver-sion 0.9.5.i (Luc Anselin and the Regents of the University of Illinois)was used to measure the relationships between village level cholerahospitalization rate and vaccine coverage after adjusting for severalfactors known to be associated with the risk of the disease. The spa-tial lag model is a maximum likelihood estimate that uses a spatiallylagged dependent variable. Formally, this model is y = �Wy + Xˇ + ε,where y is a vector of observations of the dependent variable, Wyis a spatially lagged dependent variable for weight matrix W, X is amatrix of observations of the explanatory variables, ε is the vectorof the independently and identically distributed (IID) error terms,and � and ˇ are parameters [18].
In the model, the dependent variable was the village levelcholera hospitalization rate during pre- and post-vaccination peri-ods, and the independent variable was vaccine coverage along withother socio-ecological covariates such as percentage of Hindus in
the village, percent educated individuals in the village, distancefrom the village center to the nearest treatment center, distancefrom the village center to the nearest river side, and popula-tion density in the village. The spatial weights were constructedbased on 1st order of Queen Contiguity (determines neighboring
Cholera hospitalization rate/1000 in 1984 .00 20.78 2.42 3.26Cholera hospitalization rate/1000 in 1985–86 .00 22.08 3.83 4.62Vaccine coverage (%)a 9.97 40.96 28.24 7.19% Hindus .00 100.00 17.55 29.34% educated individualsb 6.80 57.36 23.86 7.54Distance from village center to the nearest treatment center (km) .145 8.847 3.814 1.87Distance from village center to the nearest river side (km) .021 5.669 1.32 1.25Population density/100 m2 in 1984 .602 56.302 12.91 8.68Population density/100 m2 in 1985 .594 57.118 12.96 8.75
a All individuals were counted in computing vaccine coverage.b Individuals with at least 4 years of secular education were considered as educated individuals. Individuals with <5 years of age were excluded in the counting.
Table 2Results of the spatial autoregressive lag model, outcome: cholera hospitalization rate, Matlab, 1985–86 (post-vaccination period).
Weighted cholera hospitalization rate 0.501 0.083 6.032 0.000Constant 4.743 1.734 2.735 0.006Vaccine coverage (%) −0.162 0.043 −3.708 0.000% Hindus −0.017 0.010 −1.646 0.099% educated individuals 0.022 0.038 0.595 0.551Distance from village center to the nearest treatment center (km) 0.158 0.165 0.957 0.338Distance from village center to the nearest river side (km) −0.207 0.266 −0.779 0.435Population density/100 m2 in 1985 0.064 0.032 1.967 0.049Cholera hospitalization rate/1000 in 1984 0.138 0.090 1.533 0.125
Table 3Results of the spatial autoregressive lag model, outcome: cholera hospitalization rate, Matlab, 1984 (pre-vaccination period).
or below were classified as areas with a low force of infection(n = 68), and the villages that had above pre-vaccination mediancholera hospitalization rates were classified as high force of infec-tion areas (n = 68). Fig. 1 shows that with an increase of vaccine
educated individuals 0istance from village center to the nearest treatment center (km) 0istance from village center to the nearest river side (km) −opulation density/100 m2 in 1984 0
nit as those that have any point in common neighbors) of theillage.
. Results
In the 136 study villages there were 450 cases of cholera hospi-alizations in the pre-vaccination period (January–December 1984)nd 803 in the post-vaccination period (June 1985–May 1986)ielding incidence rates 2.46/1000 and 4.38/1000 for the pre-nd post-vaccination period respectively. To evaluate the effect ofaccine coverage on the incidence of cholera hospitalization, wenitially fit the data in an ordinary least squares (OLS) regression
odel. Table 1 shows summary statistics for all study variables athe village level. The post-vaccination period data yielded Moran’s(error): 5.57 (p < .001), Robust Lagrange Multiplier (Lag): 21.89
p < .001), Robust Lagrange Multiplier (error): 2.33 (p = .12); andhe pre-vaccination period data yielded Moran’s I (error): 3.95p < .001); Robust Lagrange Multiplier (lag): 4.25 (p = .039); Robustagrange Multiplier (error): 1.79 (p = .18). Measures in the OLS out-ut including Moran’s I z-value and Lagrange Multiplier tests for
ag and error indicate spatial dependence is present and a spatialag model is a better fit for the data [18]. The spatial lag modelor the post-vaccination period shows there was an inverse rela-
ionship between vaccine coverage and cholera hospitalization rateTable 2), and the relationship is statistically significant (p < .001).s a bias indicator, the data show that the level of vaccine coverageid not influence (p = .37) the risk of cholera hospitalization in there-vaccination period (Table 3).
To test our first hypothesis, we classified the villages by cholerahospitalization rate in the pre-vaccination period. The villages witha median pre-vaccination cholera hospitalization rate (1.30/1000)
Fig. 1. Average cholera hospitalization rate in the post-vaccination period by killedoral cholera vaccine coverage (classified in quintiles of the coverage rate), Matlab1985–86.
M. Ali et al. / Vaccine 27 (2009) 3724–3729 3727
Table 4Results of the spatial autoregressive lag model in low and high forces of infection areas, outcome: cholera hospitalization rate, Matlab, 1985–1986 (post-vaccination period).
Variable Areas of low forces of infection Areas of high forces of infection
Coefficient Std. error 95% CIa Coefficient Std. error 95% CIa
with an increase of vaccine coverage confirms that there is a herd
istance from village center to the nearest treatment center (km) 0.187istance from village center to the nearest river side (km) −0.183opulation density/100 m2 0.021
a Confidence interval.
overage (classified in quintiles of vaccine coverage) the post-accination cholera hospitalization rate decreases, and the rate ofecrease is higher in the areas with a low force of infection than inhe areas with a high force of infection. The results of the subgroupnalysis show that a 1% increase in vaccine coverage lowers theholera hospitalization rate by 0.21/1000 (95% CI = −0.32, −0.10) inreas with a low force of infection (median pre-vaccination choleraospitalization rate or below). In areas with a high force of infectionabove median pre-vaccination cholera hospitalization rate) a 1%ncrease in coverage lowers incidence by 0.16/1000 (95% CI = −0.31,0.01) (Table 4).
Force of infection was modeled as a binary variable along withts interaction with vaccine coverage in a separate spatial lag regres-ion model. The point estimate of the coefficient for the high force ofnfection was positive, indicating an increased risk in the areas withigh force of infection. The interaction term computed with the
orce of infection and vaccine coverage yielded a negative relation-hip with the outcome. However, the relationships of both termsith cholera hospitalization rate were not statistically significant
results not shown).The maps derived from local Bayes estimation and the Moran’s I
tatistic show that cholera hospitalization is highly clustered in thetudy area (Fig. 2). The northern part of the study area witnessed
Fig. 2. Spatial patterns (quintile classification of the villages) of cholera hospit
.264 −0.330, 0.704 0.210 0.280 −0.338, 0.758
.326 −0.821, 0.455 −0.197 0.569 −1.312, 0.918
.063 −0.102, 0.144 0.063 0.045 −0.025, 0.151
a lower rate of cholera hospitalization in both the time periods. Inthe pre-vaccination period, disease clusters were observed in thecentral part of the study area, and this shifted to the southern partof the study area in the post-vaccination period. This shift can beattributed to the village level vaccine coverage (Fig. 3).
4. Discussion
The results of our analyses show that although cholera hospital-ization in Matlab increased during the post-vaccination period dueto the temporal fluctuations of cholera in Bangladesh [14,19–21],villages with higher cholera vaccine coverage had significantlylower cholera hospitalization rates. Such a relationship was notobserved during the pre-vaccination period, confirming that vac-cine effectiveness can also be measured by comparing pre- andpost-vaccination periods even when there are temporal fluctua-tions of the disease.
A decreasing trend in post-vaccination cholera hospitalization
alization during pre- and post-vaccination periods, Matlab, Bangladesh.
effect of the killed oral cholera vaccine [1]. Furthermore, the choleravaccines were given to the target group of the population (exclud-ing children under 2 and males over 15 years). Therefore, overallimpact of the vaccination should be considered accordingly. The
3728 M. Ali et al. / Vaccine 27 (
Fo
gdfdoaltTfo
istohcbhad
icatohwoc
ig. 3. Spatial patterns of killed oral cholera vaccine coverage (classified in quintilesf the coverage rate) in the Bangladesh trial, Matlab 1985.
raph and the results of our subgroup analyses show a steeperescending trend for cholera hospitalization in areas with a loworce of infection compared to areas with a high force of infectionuring the post-vaccination period. This indicates that the impactf vaccine was affected by the magnitude of force of infection in therea. Since a vaccine can protect a person only partially, the peopleiving in areas with a high force of infection will be more suscep-ible than the people living in areas with a low force of infection.herefore, overall effectiveness of the vaccine in areas with a highorce of infection will not occur as often as it will with a low forcef infection, supporting our first hypothesis.
The maps show that cholera hospitalization was clustered dur-ng both time periods and the Moran’s I spatial autocorrelationtatistic also supports the notion. The shift of cholera clusters tohe southern part of the study area may be attributed to the levelf vaccine coverage of the area since villages with higher choleraospitalization in the post-vaccination period had very low vac-ine coverage. The changes of high areas of cholera hospitalizationetween pre- and post-vaccination periods support our secondypothesis that spatial heterogeneity of the vaccine coverage inmass vaccination program changes the spatial structure of the
isease.Geographic evaluation of vaccine effectiveness is uncommon
n vaccine trials. Since the incidence of cholera hospitalization islustered in space, using a spatial autoregressive lag model thatccounts for spatial correlation is important in order to overcomehe problem of spatial heterogeneity in the data. One limitationf this study is the arbitrary definition of village boundary, which
as no relationship to the cholera transmission process. However,e accounted for the 1st order neighboring villages in the data to
vercome the arbitrary boundary definition and random variationaused by the instability of observed rates in villages with small
[
2009) 3724–3729
numbers of people at risk. Additionally, although our results indi-cate that the performance of the vaccine is better in areas with alower force of infection, we need to study this further using largersample sizes to overcome problems with low statistical power.
A vaccine trial is important for evaluating vaccine efficacy aswell as its effectiveness [22]. Health authorities may need to knowhow to contain a disease or to reduce the burden of disease througha vaccination program. The recent work of Longini et al. [23] andour results suggest that a higher level of vaccine coverage in an areacan lead to significant reduction of the burden of the disease. Spa-tial analysis, as shown in this study, offers a better understandingof the effectiveness of a vaccine under different ecological con-ditions. Therefore, the results of trials that take into account thespatial structure and ecological circumstances of a disease canbetter inform public health officials who make decisions about vac-cination.
Acknowledgements
The data sets used in this paper were collected with the supportof ICDDR,B and its donors which provide unrestricted support to theCentre for its operations and research. Current donors providingunrestricted support include: Australian Agency for InternationalDevelopment (AusAID), Government of the People’s Republic ofBangladesh, Canadian International Development Agency (CIDA),Embassy of the Kingdom of Netherlands (EKN), Swedish Interna-tional Development Cooperation Agency (Sida), Swiss Agency forDevelopment and Cooperation (SDC), and Department of Interna-tional Development, UK (DFID). We gratefully acknowledge thesedonors for their support and commitment to the Centre’s researchefforts.
We thank the staff of the ICDDR,B, whose diligence and dedica-tion were critical to the success of the trial.
References
[1] Ali M, Emch M, von Seidlein L, Yunus M, Sack DA, Rao M, et al. Herd immunityconferred by killed oral cholera vaccines in Bangladesh: a reanalysis. Lancet2005;366(July (9479)):44–9.
[2] Xia H, Carlin B, Waller L. Hierarchical models for mapping Ohio lung cancerrates. Environmetrics 1997;8:107–20.
[3] Emch M, Ali M, Acosta C, Yunus M, Sack DA, Clemens JD. Efficacy calculationin randomized trials: global or local measures? Health & Place 2007;13(March(1)):238–48.
[4] Clemens JD, Harris JR, Sack DA, Chakraborty J, Ahmed F, Stanton BF, et al. Fieldtrial of oral cholera vaccines in Bangladesh: results of one year of follow-up.The Journal of Infectious Diseases 1988;158(July (1)):60–9.
[5] Clemens JD, Sack DA, Harris JR, Van Loon F, Chakraborty J, Ahmed F, et al. Fieldtrial of oral cholera vaccines in Bangladesh: results from three-year follow-up.Lancet 1990;335(February (8684)):270–3.
[6] Koenig MA, Fauveau V, Chowdhury AI, Chakraborty J, Khan MA. Mater-nal mortality in Matlab, Bangladesh: 1976–85. Studies in Family Planning1988;19(2):69–80.
[7] Emch M, Feldacker C, Yunus M, Streatfield PK, DinhThiem V, Canh do G, etal. Local environmental predictors of cholera in Bangladesh and Vietnam. TheAmerican Journal of Tropical Medicine and Hygiene 2008;78(May (5)):823–32.
[8] Clemens JD, Sack DA, Harris JR, Chakraborty J, Khan MR, Stanton BF, et al. Fieldtrial of oral cholera vaccines in Bangladesh. Lancet 1986;2(July (8499)):124–7.
[9] Clemens JD, Stanton BF, Chakraborty J, Sack DA, Khan MR, Huda S, et al. Bsubunit-whole cell and whole cell-only oral vaccines against cholera: stud-ies on reactogenicity and immunogenicity. The Journal of Infectious Diseases1987;155(January (1)):79–85.
[10] Ali M, Emch M, Donnay JP, Yunus M, Sack RB. Identifying environmen-tal risk factors for endemic cholera: a raster GIS approach. Health & Place2002;8(September (3)):201–10.
[11] Glass RI, Becker S, Huq MI, Stoll BJ, Khan MU, Merson MH, et al. Endemiccholera in rural Bangladesh, 1966–1980. American Journal of Epidemiology1982;116(December (6)):959–70.
12] Anselin L. Interactive techniques and exploratory spatial data analysis. In: Lon-gley P, Goodchild M, Maguire D, Rhind D, editors. Geographical informationsystems: principles, techniques, management and applications. New York: JohnWiley; 1999. p. 251–64.
[13] Marshall R. Mapping disease and mortality rates using empirical Bayes estima-tors. Applied Statistics 1991;40(2):283–94.
e 27 (
[
[
[
[
[
[
[
[
M. Ali et al. / Vaccin
14] Islam MS, Drasar BS, Sack RB. Probable role of blue-green algae in maintainingendemicity and seasonality of cholera in Bangladesh: a hypothesis. Journal ofDiarrhoeal Diseases Research 1994;12(December (4)):245–56.
15] Anselin L. Under the hood: issues in the specification and interpretation ofspatial regression models. Agricultural Economics 2002;27(3):247–67.
16] Anselin L. In: Laboratory SA, editor. GeoDA 0. 9 User’s Guide. IL: University ofIllinois, Urbana-Champaign; 2003.
17] Anselin L, Bera A. Spatial dependence in linear regression models with anintroduction to spatial econometrics. In: Ullah A, Giles D, editors. Handbookof applied economic statistics. New York: Marcel Dekker; 1988. p. 237–89.
18] Anselin L. In: Laboratory SA, editor. Exploring spatial data with GeoDA: a work-book. Department of Geography, University of Illinois, Urbana-Champaign;2005.
[
[
2009) 3724–3729 3729
19] Bouma M, Pascual M. Seasonal and interannual cycles of endemic cholerain Bengal 1891–1940 in relation to climate and geography. Hydrobiologia2001;460:147–56.
20] Koelle K, Rodo X, Pascual M, Yunus M, Mostafa G. Refractory periods and climateforcing in cholera dynamics. Nature 2005;436(August (7051)):696–700.
21] Samadi AR, Chowdhury MK, Huq MI, Khan MU. Seasonality of classical and ElTor cholera in Dhaka, Bangladesh: 17-year trends. Transactions of the Royal
Society of Tropical Medicine and Hygiene 1983;77(6):853–6.
22] Clemens J, Brenner R, Rao M, Tafari N, Lowe C. Evaluating new vaccines for devel-oping countries. Efficacy or effectiveness? JAMA 1996;275(February (5)):390–7.
23] Longini Jr IM, Nizam A, Ali M, Yunus M, Shenvi N, Clemens JD. Control-ling endemic cholera with oral vaccines. PLoS Medicine 2007;4(November(11)):e336.
