Climate Variability and the Outbreaks of Cholera in Zanzibar, East Africa: A Time Series Analysis

Abstract

Global cholera incidence is increasing, particularly in sub-Saharan Africa. We examined the impact of climate and ocean environmental variability on cholera outbreaks, and developed a forecasting model for outbreaks in Zanzibar. Routine cholera surveillance reports between 1997 and 2006 were correlated with remotely and locally sensed environmental data. A seasonal autoregressive integrated moving average (SARIMA) model determined the impact of climate and environmental variability on cholera. The SARIMA model shows temporal clustering of cholera. A 1°C increase in temperature at 4 months lag resulted in a 2-fold increase of cholera cases, and an increase of 200 mm of rainfall at 2 months lag resulted in a 1.6-fold increase of cholera cases. Temperature and rainfall interaction yielded a significantly positive association (P < 0.04) with cholera at a 1-month lag. These results may be applied to forecast cholera outbreaks, and guide public health resources in controlling cholera in Zanzibar.

Full text

862
Am. J. Trop. Med. Hyg., 84(6), 2011, pp. 862–869
doi:10.4269/ajtmh.2011.10-0277
Copyright © 2011 by The American Society of Tropical Medicine and Hygiene
INTRODUCTION
Since the beginning of the millennium, global cholera inci-
dence has increased steadily with 24% more cases reported
between 2004 and 2008 compared with 2000 and 2004.
1 In the
last 30 years, the burden of cholera has shifted from Asia to
Africa, which is currently the source of over 94% of reported
global cases,
1, 2 Vibrio cholerae O1 El Tor strain is the causative
agent for the large majority of these cases.
2 In Africa, cholera
cases tend to occur sporadically or as small outbreaks, with
a constant threat for more explosive epidemics, as illustrated
in the recent Zimbabwe epidemic with 98,592 reported cases
and 4,288 deaths in 11 months.
3
Cholera outbreaks exhibit strong seasonality, tending to
occur after increased rainfall and warm temperatures. This
seasonality has been observed for centuries but it was not until
the late 1970s that the environmental habitat of V. cholerae was
discovered.
4 Pathogenic Vibrios inhabit coastal and estuarine
ecosystems, commensal with algae and the roots of aquatic
plants, phytoplankton, zooplankton, and in particular copep-
ods. A single copepod has been shown to host up to 10
3 –10 5
Vibrios, attached to the gut wall and so ingestion of just a few
copepods can have the potency to deliver an infective dose of
V. cholerae . 5, 6 The significance of copepods as a transmission
vector was demonstrated in Bangladesh where water filtration
through sari cloth removed 99% of V. cholerae , i.e., those cells
attached to the plankton.
6
During periods between epidemics V. cholerae is able to
adopt a viable but non-culturable state in response to nutrient
deprivation, allowing Vibrios to persist in their natural aquatic
habitat for long periods in a dormant state. Reversion back
to the culturable, infectious phase is triggered by favorable
environmental factors such as increased water temperature,
pH, seawater nutrients, and decrease in salinity.
7– 9 The same
environmental factors facilitate proliferation of copepods and
free-living V. cholerae populations, resulting in large and sud-
den increases in environmental Vibrio numbers. 8– 11
Three marine environmental variables, sea surface height
(SSH), sea surface temperature (SST), and ocean chlorophyll
concentration (OCC), have shown to be predictive of cholera
outbreaks in Kolkata, India; Matlab, Bangladesh; and Hue,
Vietnam.
10, 12, 13 The first of these studies, conducted in 2000 by
Lobitz and others,
10 used satellite imagery to derive the values
for ocean parameters and linked these with cholera incidence.
The SST was positively associated with cholera cases, affirm-
ing the hypothesis that a rise in SST encourages phytoplankton
populations to bloom, which is directly associated with greater
zooplankton populations and V. cholerae . 10 The positive asso-
ciation of SSH with cholera cases was attributed to increased
human to aquatic Vibrio contact. 10, 14 The indirect measure-
ment of OCC in Kolkata showed a positive association with
cholera, it is thought that the rise in OCC allows copepod pop-
ulations to grow and thus V. cholerae . 12 Measurement of OCC
is difficult, however, and possibly problematic for making pre-
dictions because of its variability across small areas.
5
Locally measured variables such as temperature and rainfall
have been positively associated with increased cholera inci-
dence in multiple studies. One of which was in Zambia, located
in the same geographic region as Zanzibar, but lack of cholera
case data between epidemics prevented the development of an
appropriate predictive model.
15 Environmental variables are
linked to cholera outbreaks through the fluctuation of environ-
mental Vibrio populations. Lagged dynamics are typically seen
because of the time required between the change in the envi-
ronment and environmental Vibrio reaching sufficient num-
bers for ingestion of an infectious dose.
4 We hypothesize that
ambient temperature is linked to SST and at its optimum for
Vibrio growth, a lagged increase in cholera cases will occur.
Understanding environmental drivers of cholera outbreaks
could facilitate a degree of outbreak prediction, allowing gov-
ernments to prepare and respond early to potential outbreaks
through actions such as employing vaccines. In addition, it
offers insight into the local etiology of cholera, which would
be helpful in planning prevention strategies. This study exam-
ines the association between climate variability and cholera
outbreaks, and develops a climate-based forecasting model for
cholera in Zanzibar using a time series analysis.
METHODS
Study area. Zanzibar, an archipelago ~50 km off the eastern
coast of Tanzania mainland, consists of two main islands,
Climate Variability and the Outbreaks of Cholera in Zanzibar, East Africa:
A Time Series Analysis
Rita Reyburn ,* Deok Ryun Kim , Michael Emch , Ahmed Khatib , Lorenz von Seidlein , and Mohammad Ali
International Vaccine Institute, Seoul, Korea; University of North Carolina at Chapel Hill, Chapel Hill,
North Carolina; Ministry of Health and Social Welfare, Zanzibar, Tanzania
Abstract. Global cholera incidence is increasing, particularly in sub-Saharan Africa. We examined the impact of
climate and ocean environmental variability on cholera outbreaks, and developed a forecasting model for outbreaks
in Zanzibar. Routine cholera surveillance reports between 1997 and 2006 were correlated with remotely and locally
sensed environmental data. A seasonal autoregressive integrated moving average (SARIMA) model determined the
impact of climate and environmental variability on cholera. The SARIMA model shows temporal clustering of cholera.
A 1°C increase in temperature at 4 months lag resulted in a 2-fold increase of cholera cases, and an increase of 200 mm
of rainfall at 2 months lag resulted in a 1.6-fold increase of cholera cases. Temperature and rainfall interaction yielded a
significantly positive association ( P < 0.04) with cholera at a 1-month lag. These results may be applied to forecast cholera
outbreaks, and guide public health resources in controlling cholera in Zanzibar.
* Address correspondence to Rita Reyburn, IVI, CHOZAN Project,
PO Box 3524, Zanzibar, Tanzania. E-mail: rita_reyburn@hotmail.com

863
CHOLERA AND CLIMATE VARIABILITY IN ZANZIBAR
Unguja and Pemba ( Figure 1 ). The total population is about
1.1 million, of which 17% are < 5 years of age, 28% are 5 to 15
years of age, and 56% are > 15 years of age.
16 Life expectancy
is 57 years of age.
16 The economy of the islands depends largely
on agriculture, tourism, and fishing. The mean temperature
varies between 21°C/70°F and 33°C/91°F with monthly rainfall
between 25 mm/0.1 in. and 434 mm/1.7 in.
17 The long rains and
short rains typically occur between March and May, and from
October to December, respectively. We restricted our study to
Unguja where we collected data on the temporal distribution
of cholera. The study omitted Pemba as the cholera data
available was not enough to conduct a time series analysis. It is
likely that the identified environmental predictors would apply
to both islands as they are geographically, environmentally,
and socioeconomically similar other than one area of Unguja,
which is more urbanized than any part of Pemba.
Source data. Environmental data. Table 1 summarizes the
source information for the environmental data. Mean monthly
SST, SSH, and OCC were derived from satellite imagery. The
satellite data for the SST variables are available from the
beginning of January 1985, and are distributed by NASA’s Jet
Propulsion Laboratory. The Advanced Very High Resolution
Radiometer (AVHRR), a space-borne sensor of the National
Oceanic and Atmospheric Administration’s (NOAA) family
of polar orbiting platforms, collects data on 4-km areas. The
SSH, a measure used to detect sea-level anomalies, is derived
through satellite altimetry, and is available from 1992 from
the TOPEX/Poseidon sensor, and available from 2002 from
the Jason-1 sensor. Starting in 1997, OCC data were available
from SeaWiFS and MODIS (MODerate resolution Imaging
Spectroradiometer) sensors at a spatial resolution of 9 and
1 km, respectively. Approximately the same area is used to
compile data for the satellite-derived, monthly environmental
variables collected by all four sensors. Climate data from in
situ sources include monthly minimum temperature, maximum
temperature, rainfall, and humidity, which are collected by the
Meteorological Department of the Zanzibar Government.
Population and cholera data. The population data were
obtained from the national census of Zanzibar. Case reports
for cholera were based on the Zanzibar Ministry of Health
and Social Welfare’s (MoHSW) cholera surveillance records.
Reporting was based on the recommended World Health
Organization (WHO) guidelines of acute watery diarrhea
with microbiological confirmation; or after microbiological
confirmation of the first 10 cases in an outbreak, and clinical
criteria of severe watery diarrhea.
18, 19 All available local
surveillance records and WHO country reports for Tanzania
were reviewed for consistency, but only local reports were used
Figure 1. Study area: Zanzibar, East Africa.

864 REYBURN AND OTHERS
for this study, which were available from 2002 to 2008. The
large majority of cases were clinically diagnosed cholera with
a smaller proportion of laboratory-confirmed cases, consistent
with the WHO international guidelines. Both clinical- and
laboratory-confirmed cases were included in the analysis.
Ethics. The analysis was based on cholera reports collected
routinely by the MoHSW Zanzibar, from whom we obtained
the data for analysis. The reports did not include individual
patient identifiers, thus no Institutional Review Board
approval was sought for this study. Individual consent was
not obtained for this study, because this was a retrospective
analysis of existing data collected by MoHSW, hence, it was
not necessary or possible to obtain consent from participants.
Environmental data were obtained from open access sources.
Data analysis. We analyzed the data by month, producing 84
time points during the study period (2002–2008). Univariate
analysis was conducted for each of the climate and ocean
environment variables with cross-correlation analysis to assess
associations between cholera cases and covariates over a range
of time lags.
20, 21 The time lags chosen for the final model were
outcomes of the cross-correlation analysis using seasonal
differencing of the data. A multivariate seasonal autoregressive
integrated moving average (SARIMA) model was used to
examine the independent contribution of the cholera transmission
covariates, because its integrated functions for controlling
seasonal variation, autocorrelation, and long-term trends make
it the most appropriate model for this time series analysis.
22 The
numbers of terms in the SARIMA model were determined by
autocorrelation function (ACF) and partial autocorrelation
function (PACF). The outcome variable (number of cases) was
transformed into a square root, as recommended by Johnson
and Wichern,
23 to address issues with zero-inflated count data.
To create an appropriate stationary time series for the anal-
ysis, all dependent and independent variables were seasonally
differenced with regard to yearly periodicity. We determined
the need for differencing the monthly cholera cases by check-
ing stationarity (trends in the mean and variance), and the
order of both seasonal and non-seasonal autoregressive and
moving average indicators by using the ACF and PACF. The
model used in this study was SARIMA( p , d , q )( P , D , Q ) s , where
p is the order of autoregression; d , the degree of difference;
q , the order of moving average; P , the seasonal autoregres-
sion; D , the seasonal integration; Q , the seasonal moving aver-
age; and s , the length of the seasonal period. We used the
stepwise SARIMA method to select covariates associated
with cholera cases at P < 0.1. Our main criterion for judging
the superiority of the model against other models was based
on the lowest value in the Akaike’s information criterion
(AIC), a measure of the goodness-of-fit of an estimated sta-
tistical model.
24 The goodness-of-fit of the models were deter-
mined for appropriate modeling, using both ACF and PACF of
residuals, and checking the normality of the residuals.
Data from 2002 to 2008 were used for parameter estima-
tions. For validation of the model, we used the data from 2002
to 2007 as the training period, and the data from 2008 as the
validation period. Finally, the data from 2008 was used to test
the forecasting ability of the method using the root mean
square (RMS) error criterion. The smaller the RMS error, the
better the model is for forecasting. All analyses were carried
out using SAS version 9.1 (SAS Institute, Inc., Cary, NC).
RESULTS
In Unguja, 3,245 cholera cases were reported between 2002
and 2008, and the temporal patterns of cases are shown in
Figure 2 . The characteristics of the study data are shown in
Table 1
Climate and environmental variables and data sources
Variable Data source and availability
In situ climate variables
Temperature Meteorological department, Zanzibar
Government:
Unguja airport weather station
(2002–2008)
Rainfall Meteorological department, Zanzibar
Government:
Unguja airport weather station
(2002–2008)
Humidity Meteorological department, Zanzibar
Government:
Unguja airport weather station
(2002–2008)
Satellite-derived environmental variables
Sea surface temperature AVHRR (2002–2008)
Source: http://poet.jpl.nasa.gov/
Sea surface height TOPEX/Poseidon (2002–2008) Jason-1
(2002–2003),
Source: http://www.aviso.oceanobs.com/
Ocean chlorophyll
concentration
MODIS (2002–2008), Source: SeaWiFS
(2002–2008),
Source: http://oceancolor.gsfc.nasa.gov/
Figure 2. Monthly cholera incidence rates in Unguja, Zanzibar, 2002–2008.

865
CHOLERA AND CLIMATE VARIABILITY IN ZANZIBAR
Table 2 . We calculated 12 months of seasonally differenced
values for each variable to ensure the time series of climate
and environmental variables, and the square root transformed
cholera outbreaks were stationary. The results of the cross-
correlations using seasonal differencing of the data show
that cholera outbreaks were significantly positively associ-
ated ( P < 0.05) with minimum temperature at lags of 2 and
4 months, negatively associated with maximum temperature
at a lag of 2 months, positively associated with humidity at a
lag of 5 months, and positively associated with rainfall at a lag
of 1 month. None of the ocean environmental variables show
significant association with the cholera outbreaks ( Table 3 ).
Covariates indicating significant correlation are: minimum
temperature with positive SST ( P < 0.01), maximum tempera-
ture with negative rainfall ( P < 0.01) and negative humidity
( P < 0.01), rainfall with positive humidity ( P < 0.01), humid-
ity with negative SST ( P < 0.05), and OCC from MODIS with
positive OCC from SeaWiFS ( P < 0.01) ( Table 4 ). These vari-
ables were included separately in the models to avoid multi-
collinearity. The seasonally differenced minimum temperature
and rainfall, respectively, are positively associated with chol-
era outbreaks ( Figure 3 ). However, a few cases did not line
up well, because of the nature of the outbreak, reporting sys-
tem, and lagged effects of climate and ocean environment
variability.
SARIMA model. The best fit model without covariates (null
model) was SARIMA([1,0,0][0,1,1]
12 ), and the best fit model
with covariates (covariate model) was SARIMA([1,4],[0,0]
[0,1,1]
12 ) based on the AIC values. A backward elimination
method selected variables most suitable for the model. The
results of the null model showed temporal clustering of cholera
at a 1-month lag. The patterns of the temporal clustering of
cholera were modified by the climatic variables, as obtained in
the covariate model, yielding clustering at a 1-month lag and at
a 4-month lag ( Table 5 ). The temporal lag relation of cholera
and minimum temperature is 4 months, and the temporal lag
relation of cholera and rainfall is 2 months. An increase of 1°C
in minimum temperature at a 4-month lag resulted in a 2-fold
increase of cholera cases; and an increase of 200 mm in rainfall
at a 2-month lag resulted in a 1.6-fold increase of cholera
cases. The interaction of temperature and rainfall yielded a
significant association ( P = 0.04) with cholera at a 1-month lag
( Table 3 ). The goodness-of-fit analysis shows that there was no
significant autocorrelation between residuals at different lags
in the SARIMA model and the model fits the data well.
Validation model. The model developed using the data for
the 2002–2007 periods was used to predict cholera cases, and
then validated using the data between January and December
2008. The validation results show the SARIMA model to
be an appropriate model for forecasting cholera outbreaks
in Zanzibar (RMS errors for both training and validation
period are 3.92 and 4.50, respectively). In the training period,
it had lower predicted values in August and December 2006,
and in the validation period, it had a higher observed value
in December 2008 ( Figure 4 ). Except for these points, the
predicted and observed cases were matched reasonably well,
and there was consistency in the trend. The time series plot
of cumulative sums of observed and predicted cholera cases
showed only a small gap in August 2006, and that was retained
during the rest of the study period ( Figure 5 ).
DISCUSSION
This study provides reassuring evidence that rainfall and
temperature, among various climate and ocean environmental
variables are the key drivers of cholera outbreak, consistent
Table 2
Characteristics of the study data, Unguja, Zanzibar *
Variables (monthly average) N (months) Mean SD Minimum Maximum
Monthly cholera cases 84 38.63 77.17 0 534
Minimum temperature 84 23.15 1.37 20.2 25.3
Maximum temperature 84 30.68 1.47 28.2 33.8
Rainfall 84 141.35 156.36 3 705.4
Humidity 84 78.30 5.95 57 89
Sea surface temperature 79 27.64 1.44 25.065 29.894
Sea surface height 79 −0.17 3.96 −9.504 10.172
Ocean chlorophyll concentration from MODIS 78 0.13 0.05 0.068 0.353
Ocean chlorophyll concentration from Sea-Wifs 84 0.16 0.06 0.086 0.346
* The variables (except cholera cases) are monthly averages.
Table 3
Cross-correlation coefficients of square root of observed cholera cases and climate and environmental variability using 12-month seasonal
differencing, Unguja, Zanzibar
Variables
Lag (month)
01 234 5
Minimum temperature 0.00 0.08 0.25 ** 0.07 0.23 ** −0.02
Maximum temperature −0.11 −0.19 * −0.12 −0.05 0.07 −0.02
Rainfall 0.05 0.24 ** 0.19 * 0.09 −0.01 0.00
Humidity 0.00 0.20 * 0.08 −0.09 −0.04 0.22 **
Sea surface temperature −0.11 −0.01 −0.04 −0.17 −0.15 0.07
Sea surface height 0.07 0.04 0.16 −0.01 −0.06 −0.05
Ocean chlorophyll concentration from MODIS 0.08 0.00 0.06 −0.01 −0.07 −0.02
Ocean chlorophyll concentration from Sea-Wifs −0.14 −0.12 0.18 * 0.12 −0.17 * −0.01
* P < 0.1; ** P < 0.05.

866 REYBURN AND OTHERS
with results from other studies.
14 In Nha Trang, Viet Nam,
Emch and coworkers
13 showed that the probability of out-
breaks was higher with increased rainfall, river height, and dis-
charge. In Bangladesh, Hashizume and coworkers
25 showed
the risk of cholera increased with high rainfall, and conversely,
with decreased rainfall suggesting that river level was on the
causal pathway. Their findings illustrate the variability of envi-
ronmental predictors across and within study sites, hence the
importance of conducting thorough time series analysis by site
if a plausible predictive model is to be reached. Consistent
across the two studies was the attribution of the positive asso-
ciation of cholera with rainfall, to increased Vibro to human
contact caused by flooding and an inundation effect where the
environment is inundated with the bacteria.
Heavy rainfall and consequent flooding increases the risk of
sewage contaminating the drinking water. In Zanzibar the most
common toilet facility are pit latrines,
26 where the untreated
waste water can overflow or seep through the ground into
the drinking water found in wells or pipes. Many piped water
lines contain leaks caused by age and lack of maintenance and
Table 4
Inter-correlations between climate and environmental variables using 12-month seasonal differencing, Unguja, Zanzibar
Variables Maximum temperature Rainfall Humidity SST SSH OCC from MODIS OCC from Sea-Wifs
Minimum temperature 0.16 0.20 0.01 0.35 *** 0.12 −0.15 0.01
Maximum temperature −0.53 *** −0.52 *** 0.22 0.14 0.01 0.16
Rainfall 0.48 *** 0.09 −0.001 0.14 −0.13
Humidity −0.26 ** 0.001 0.06 −0.23 *
SST −0.12 −0.11 −0.01
SSH −0.06 −0.09
OCC from Modis 0.69 ***
SST = sea surface temperature; SSH = sea surface height; OCC = ocean chlorophyll concentration.
* P < 0.1; ** P < 0.05; *** P < 0.01.
Figure 3. Relationship between square root of monthly observed cholera cases and ( A ) minimum temperature and ( B ) rainfall using seasonal
differencing in Unguja, Zanzibar during the period 2003–2008 .

867
CHOLERA AND CLIMATE VARIABILITY IN ZANZIBAR
during times of low water pressure the surrounding ground
water may be actively sucked into the pipes. Poor drainage
is common in many cholera-affected areas, and during heavy
rains, residential areas can be submerged in water for pro-
longed periods of time, promoting human Vibrio contact.
The relationship between temperature and amplification of
cholera incidence is well documented.
5, 8, 12, 14, 27– 30 Warmer tem-
peratures increase SST facilitating Vibrio reproduction and
hence the probability of ingesting an infectious dose.
5, 14 Our
concurring findings are important in light of the current global
warming phenomenon, Climatologists predict a 1.4°C/35°F to
5.8°C/42°F rise in mean temperature over the next 100 years.
30
Increased sea temperatures and levels associated with global
warming intuitively suggest the possibility of increased chol-
era incidence in many resource-poor regions of the world.
The main limitation of this study was the use of routine sur-
veillance data. The majority of cases were clinically diagnosed
with only a small proportion laboratory-confirmed. However,
it is likely that these outbreaks were indeed caused by V. chol-
erae as indicated by laboratory confirmation of initial cases
during each outbreak and by the case fatality rates. Consistent
with international guidelines, not every suspected case was
laboratory-confirmed, and this may have resulted in an over-
estimation of actual cholera cases. On the other hand, there is
the possibility that the number of cases were under-reported
as patients may not have sought care.
There is potential to develop an early warning system for
cholera in Zanzibar using a predictive model, which would
give public health authorities sufficient time to prepare med-
ical equipment and mobilization of staff in the event of an
Figure 4. ( A ) Predicted vs. observed monthly cholera cases in Unguja, Zanzibar during 2003–2008. ( B ) Validation model for the period January
to December 2008 with monthly cholera cases.
Table 5
Regression coefficients of seasonal autoregressive integrated moving average (SARIMA) on monthly cholera cases in Unguja, Zanzibar,
2002–2008
Variables
Model without covariates Model with covariates
Estimate Standard error P value Estimate Standard error P value
Seasonal moving average 0.750 0.097 < 0.0001 0.749 0.120 < 0.0001
Autoregression at 1 month lag 0.526 0.105 < 0.0001 0.493 0.109 < 0.0001
Autoregression at 4 months lag 0.291 0.109 0.010
Minimum temperature at 4 months lag – – – 2.208 1.021 0.034
Rainfall at 2 months lag 0.008 0.003 0.025
Minimum temperature × rainfall at 1 month lag 0.0003 0.0001 0.044
Akaike’s information criterion (AIC) 423.35 390.62

868 REYBURN AND OTHERS
outbreak. Prediction of outbreaks is imperative in order for
cholera prevention strategies to be efficient and cost-effective
such as targeted pre-emptive mass oral cholera vaccination.
However, more work is needed on refining such a model
before it is ready for routine use. In addition, this study pro-
vides useful clues on further elucidating the risk factors for
cholera that can be used to direct public health strategies.
In conclusion, this study supports the findings of previous
studies illustrating the link between cholera incidence and cli-
matic factors. Rainfall and temperature observations could
facilitate a warning signal for cholera outbreaks in Zanzibar.
Our model contributes to the growing literature on the link
between cholera and environmental factors, which can eventu-
ally be used to predict and prepare for cholera epidemics.
Received May 14, 2010. Accepted for publication October 20, 2010.
Acknowledgments: We are grateful to the staff members of the
Ministry of Health and Meteorological Department of Zanzibar for
providing us the data for conducting this study.
Financial support: Support for this study was provided by the
Bill & Melinda Gates Foundation through the Cholera Vaccine
Initiative (CHOVI) Program, administered by the International
Vaccine Institute (IVI), and the Swedish International Development
Cooperation Agency (SIDA).
Authors’ addresses: Rita Reyburn, International Vaccine Institute
(IVI), CHOZAN project, Zanzibar, Tanzania, E-mail: rita_reyburn@
hotmail.com . Deok Ryun Kim, Lorenz von Seidlein, and Mohammad
Ali, International Vaccine Institute, SNU Research Park, Seoul, Korea,
E-mails: drkim@ivi.int , lseidlein@ivi.int , and mali@ivi.int . Michael
Emch, Department of Geography, University of North Carolina,
Saunders Hall, Chapel Hill, NC, E-mail: emch@email.unc.edu . Ahmed
Khatib, Ministry of Health and Social Welfare, Zanzibar, Tanzania,
E-mail: ahmedbenga@yahoo.com .
REFERENCES
1. WHO , 2009 .Weekly Epidemiological Record.
2. Gaffga NH , Tauxe RV , Mintz ED , 2007 . Cholera: a new homeland
in Africa? Am J Trop Med Hyg 77: 705 – 713 .
3. United Nations Office for the Coordination of Humanitarian
Affairs , 2009 . Daily cholera update. Afghanistan : OCHA.
4. Colwell RR , 1996 . Global climate and infectious disease: the chol-
era paradigm . Science 274: 2025 – 2031 .
5. Sack DA , Sack RB , Nair GB , Siddique AK , 2004 . Cholera. Lancet
363 : 223 – 233 .
6. Colwell RR , Huq A , Islam MS , Aziz KM , Yunus M , Khan NH ,
Mahmud A , Sack RB , Nair GB , Chakraborty J , Sack DA ,
Russek-Cohen E , 2003 . Reduction of cholera in Bangladeshi
villages by simple filtration . Proc Natl Acad Sci USA 100:
1051 – 1055 .
7. Huq A , Whitehouse CA , Grim CJ , Alam M , Colwell RR , 2008 .
Biofilms in water, its role and impact in human disease trans-
mission . Curr Opin Biotechnol 19: 244 – 247 .
8. Colwell RR , 2000 . Viable but nonculturable bacteria: a survival
strategy . J Infect Chemother 6: 121 – 125 .
9. Sack DA , 1997 . Cholera. Lancet 349: 1825 – 1830 .
10. Lobitz B , Beck L , Huq A , Wood B , Fuchs G , Faruque AS , Colwell
R , 2000 . Climate and infectious disease: use of remote sensing
for detection of Vibrio cholerae by indirect measurement . Proc
Natl Acad Sci USA 97: 1438 – 1443 .
11. Ali M , Emch M , Donnay JP , Yunus M , Sack RB , 2002 . The spatial
epidemiology of cholera in an endemic area of Bangladesh . Soc
Sci Med 55: 1015 – 1024 .
12. Constantin de Magny G , Murtugudde R , Sapiano MR , Nizam A ,
Brown CW , Busalacchi AJ , Yunus M , Nair GB , Gil CL , Lanata
CF , Calkins J , Manna B , Rajendran K , Bhattacharya MK , Huq
A , Sack RB , Colwell RR , 2008 . Environmental signatures asso-
ciated with cholera epidemics . Proc Natl Acad Sci USA 105:
17676 – 17681 .
13. Emch M , Feldacker C , Yunus M , Streatfield PK , Dinh Thiem V ,
Canh do G , Ali M , 2008 . Local environmental predictors of
cholera in Bangladesh and Vietnam . Am J Trop Med Hyg 78:
823 – 832 .
14. Colwell RR , 2002 . A voyage of discovery: cholera, climate and
complexity . Environ Microbiol 4: 67 – 69 .
15. Luque Fernandez MA , Bauernfeind A , Jimenez JD , Gil CL , El
Omeiri N , Guibert DH , 2009 . Influence of temperature and
rainfall on the evolution of cholera epidemics in Lusaka,
Zambia, 2003–2006: analysis of a time series . Trans R Soc Trop
Med Hyg 103: 137 – 143 .
16. MoHSW Z , 2008 . Country Health Profile . Zanzibar : Ministry of
Health and Social Welfare.
17. Meterorological Department Z , 2009 . Monthly weather data.
Zanzibar town: data on weather variables.
18. Health Management Information System M , 2008 . National
Guidelines for Integrated Disease Surveillance and Response
Guidelines (IDRS) . Zanzibar : Ministry of Health and Social
Welfare.
19. WHO, Global Task Force on Cholera Outbreak 2004 . Cholera out-
break: assessing the outbreak response and improving pre-
paredness. Geneva, Switzerland : WHO .
20. Chatfield C , 1975 . The Analysis of Time Series: Theory and Practice .
London, UK : Chapman and Hall .
21. Chatfield WR , Rogers TG , Brownlee BE , Rippon PE , 1975 .
Placental scanning with computer-linked gamma camera to
detect impaired placental blood flow and intrauterine growth
retardation . BMJ 2: 120 – 122 .
22. Zhang Y , Bi P , Hiller J , 2008 . Climate variations and salmonellosis
transmission in Adelaide, South Australia: a comparison
between regression models . Int J Biometeorol 52: 179 – 187 .
Figure 5. Cumulative sums for observed and predicted cholera cases in Unguja, Zanzibar during 2003–2008.

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23. Johnson RA , Wichern DW , 1998 . Applied Multivariate Statistical
Analysis . Englewood Cliffs, NJ : Prentice Hall .
24. Naish S , Hu W , Nicholls N , Mackenzie JS , McMichael AS , Dale P ,
Tong S , 2006 . Weather variability, tides, and Barmah Forest virus
disease in the Gladstone region, Australia . Environ Health
Perspect 114: 678 – 683 .
25. Hashizume M , Armstrong B , Hajat S , Wagatsuma Y , Faruque AS ,
Hayashi T , Sack DA , 2008 . The effect of rainfall on the inci-
dence of cholera in Bangladesh . Epidemiology 19: 103 – 110 .
2 6 . Houshold Budget Survey , 2006 . Zanzibar : Ministry of Health and
Social Welfare .
27. Alam M , Sultana M , Nair GB , Siddique AK , Hasan NA , Sack
RB , Sack DA , Ahmed KU , Sadique A , Watanabe H , Grim CJ ,
Huq A , Colwell RR , 2007 . Viable but nonculturable Vibrio
cholerae O1 in biofilms in the aquatic environment and their
role in cholera transmission . Proc Natl Acad Sci USA 104:
17801 – 17806 .
28. Colwell RR , 2004 . Infectious disease and environment: cholera as
a paradigm for waterborne disease . Int Microbiol 7: 285 – 289 .
29. Louis VR , Russek-Cohen E , Choopun N , Choopun N , Rivera IN ,
Gangle B , Jiang SC , Rubin A , Patz JA , Huq A , Colwell RR ,
2003 . Predictability of Vibrio cholerae in Chesapeake Bay . Appl
Environ Microbiol 69: 2773 – 2785 .
30. Lipp EK , Huq A , Colwell RR , 2002 . Effects of global climate on
infectious disease: the cholera model . Clin Microbiol Rev 15:
757 – 770 .