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


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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.
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Am. J. Trop. Med. Hyg., 84(6), 2011, pp. 862–869
Copyright © 2011 by The American Society of Tropical Medicine and Hygiene
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.
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
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.
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,
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.
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.
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:
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.
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).
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
Unguja airport weather station
Rainfall Meteorological department, Zanzibar
Unguja airport weather station
Humidity Meteorological department, Zanzibar
Unguja airport weather station
Satellite-derived environmental variables
Sea surface temperature AVHRR (2002–2008)
Sea surface height TOPEX/Poseidon (2002–2008) Jason-1
Ocean chlorophyll
MODIS (2002–2008), Source: SeaWiFS
Figure 2. Monthly cholera incidence rates in Unguja, Zanzibar, 2002–2008.
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
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]
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 ).
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
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.
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 .
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.
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,
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
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@ . Deok Ryun Kim, Lorenz von Seidlein, and Mohammad
Ali, International Vaccine Institute, SNU Research Park, Seoul, Korea,
E-mails: , , and . Michael
Emch, Department of Geography, University of North Carolina,
Saunders Hall, Chapel Hill, NC, E-mail: . Ahmed
Khatib, Ministry of Health and Social Welfare, Zanzibar, Tanzania,
E-mail: .
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... 14 Studies focused on diarrhoea and cholera incidences suggested the importance of strengthening household and community interventions for preventing weather-induced diarrhoea and cholera diseases in Africa. 32,[39][40][41] One study pointed out the potential benefits of conditional cash transfers to deal with vulnerability related to heat exposure 12 , while another alluded to the use of clothing suitable to hot climates 36 . ...
... Early warning system interventions were suggested several times, to ensure that the African populace is informed of impending extreme heat events before they occur. 13,35,[40][41][42][43] While being informed may not directly translate to action, early warning systems might enable the populations that will be affected by exposure to extreme heat to prepare in advance. This suggestion implicitly calls for the strengthening of climate information services for health on the continent. ...
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Temperature extremes vary across Africa. A continent-wide examination of the impacts of heat on health in Africa, and a synthesis of Africa-informed evidence is, however, lacking. A systematic review of articles published in peer-reviewed journals between January 1992 and April 2019 was conducted. To be eligible, articles had to be Africa-specific, in English, and focused on how heatwaves and high ambient temperatures affect morbidity and mortality. A secondary systematic analysis on policies and interventions comprising 17 studies was also conducted, and the findings synthesised together with those of the 20 primary studies. Eleven studies showed that high ambient temperatures and heat waves are linked with increased mortality rates in Africa. These linkages are characterised by complex, linear and non-linear (J or U) relationships. Eight of the nine primary studies of morbidity outcome reported that an increase in temperature was accompanied by raised disease incidence. Children and the elderly were the population groups most vulnerable to extreme heat exposure. Location-specific interventions and policy suggestions include developing early warning systems, creating heat-health plans, changing housing conditions and implementing heat-health awareness campaigns. In summary, this review demonstrates that, while heat-health relationships in Africa are complex, extreme temperatures are associated with high mortality and morbidity, especially amongst vulnerable populations. As temperatures increase across Africa, there is an urgent need to develop heat-health plans and implement interventions. Future studies must document intervention effectiveness and quantify the costs of action and inaction on extreme heatrelated mortality and morbidity. Significance: • Empirical evidence shows that the relationship between heat and human health is complex in the African This complexity has implications for the development of interventions and policies for heathealth on the continent. • This review is important for African policymakers, practitioners and others who support Africa’s adaptation to climate change. Through this review, a compendium of Africa-specific and relevant empirical information is aggregated and made readily available to various interested and affected parties.
... However, it is acknowledged that climate change has also played a critical role, with more frequent and intense weather events such as hurricanes and floods damaging critical water and sanitation infrastructure [5,6]. The outbreak was also exacerbated by increasing temperatures; for example, in Zanzibar, a 1 • C rise in temperature was found to be associated with a 2-fold increase in cholera [7]. Climate change was also suggested to play a role in the emergence and transmission of the novel coronavirus causing Severe Acute Respiratory Syndrome SARS-CoV-2 (COVID- 19), with pandemics generally predicted to be more frequent and more severe in the future unless climate changes are mitigated [8]. ...
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Climate change is the most urgent and significant public health risk facing the globe. In Australia, it has been identified that Environmental Health Officers/Practitioners (EHOs/EHPs, hereafter EHOs) are a currently underutilized source of knowledge and skills that can contribute to climate change adaptation planning at the local government level. The ability of local government EHOs to utilize their local knowledge and skills in human health risk assessment during a public health emergency was demonstrated through their role in the response to COVID-19. This study used a survey and follow up interviews to examine the roles and responsibilities of EHOs during the COVID-19 pandemic and used the results to examine the potential of the workforce to tackle climate change and health related issues. What worked well, what regulatory tools were helpful, how interagency collaboration worked and what barriers or hindering factors existed were also explored. A workforce review of EHOs in South Australia was also undertaken to identify current and future challenges facing EHOs and their capacity to assist in climate change preparedness. The findings demonstrated that the workforce was used in the response to COVID-19 for varying roles by councils, including in education and communication (both internally and externally) as well as monitoring and reporting compliance with directions. Notably, half the workforce believed they could have been better utilized, and the other half thought they were well utilized. The South Australian Local Government Functional Support Group (LGFSG) was praised by the workforce for a successful approach in coordinating multiagency responses and communicating directions in a timely fashion. These lessons learnt from the COVID-19 pandemic should be incorporated into climate change adaptation planning. To ensure consistent messaging and a consolidated information repository, a centralized group should be used to coordinate local government climate change adaptation plans in relation to environmental health and be included in all future emergency management response plans. The surveyed EHOs identified environmental health issues associated with climate change as the most significant future challenge; however, concerningly, participants believe that a lack of adequate resourcing, leading to workforce shortages, increasing workloads and a lack of support, is negatively impacting the workforce’s preparedness to deal with these emerging issues. It was suggested that the misperception of environmental health and a failure to recognize its value has resulted in a unique dilemma where EHOs and their councils find themselves caught between managing current workload demands and issues, and endeavouring to prepare, as a priority, for emerging environmental health issues associated with climate change and insufficient resources.
... Studies have shown that even a small increase in temperature can significantly increase the risk of cholera outbreaks; for example, for every 1 • C increase in sea surface temperature, the number of cholera cases increases up to fivefold in some regions. Higher temperatures can lead to the proliferation of V. cholerae in water and food sources, increasing the risk of transmission to humans [17]. One example of a new threat that has emerged partly due to climate change is Candida auris, a multidrug-resistant fungal pathogen that existed in the environment but has now been isolated from human patients. ...
... Despite adapting the methodology to account for this, a potential limitation may be lagged effects of the covariates on cholera [67,68]. Both long-term and short-term changes to the population may take time before changes in cholera transmission are evident. ...
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Nigeria currently reports the second highest number of cholera cases in Africa, with numerous socioeconomic and environmental risk factors. Less investigated are the role of extreme events, despite recent work showing their potential importance. To address this gap, we used a machine learning approach to understand the risks and thresholds for cholera outbreaks and extreme events, taking into consideration pre-existing vulnerabilities. We estimated time varying reproductive number (R) from cholera incidence in Nigeria and used a machine learning approach to evaluate its association with extreme events (conflict, flood, drought) and pre-existing vulnerabilities (poverty, sanitation, healthcare). We then created a traffic-light system for cholera outbreak risk, using three hypothetical traffic-light scenarios (Red, Amber and Green) and used this to predict R. The system highlighted potential extreme events and socioeconomic thresholds for outbreaks to occur. We found that reducing poverty and increasing access to sanitation lessened vulnerability to increased cholera risk caused by extreme events (monthly conflicts and the Palmers Drought Severity Index). The main limitation is the underreporting of cholera globally and the potential number of cholera cases missed in the data used here. Increasing access to sanitation and decreasing poverty reduced the impact of extreme events in terms of cholera outbreak risk. The results here therefore add further evidence of the need for sustainable development for disaster prevention and mitigation and to improve health and quality of life.
... Global weather patterns have been implicated in the geographic distribution of illnesses. [31][32][33][34][35][36][37] During this period, El Niño, a complex series of weather patterns affecting the equatorial Pacific, brought wetter and warmer weather to East Africa, contributing to a 3-fold increase in cholera incidence in the region. 38 Regional and global cooperation can facilitate targeting of vulnerable populations for prevention campaigns, including cholera vaccination, which may help limit transmission. ...
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From August 15, 2015 to March 5, 2016, Tanzania reported 16,521 cholera cases and 251 deaths, with 4,596 cases and 44 deaths in its largest city, Dar es Salaam. To evaluate outbreak response efforts, we conducted a household survey with drinking water testing in the five most affected wards in Dar es Salaam. We interviewed 641 households 6 months after the beginning of the outbreak. Although most respondents knew that cholera causes diarrhea (90%) and would seek care if suspecting cholera (95%), only 45% were aware of the current outbreak in the area and only 5% would use oral rehydration salts (ORS) if ill. Of 200 (31%) respondents reporting no regular water treatment, 46% believed treatment was unnecessary and 18% believed treatment was too expensive. Fecal contamination was found in 45% of water samples and was associated with water availability ( P = 0.047). Only 11% of samples had detectable free chlorine residual, which was associated with water availability ( P = 0.025), reported current water treatment ( P = 0.006), and observed free chlorine product in the household ( P = 0.015). The provision of accessible, adequately chlorinated water supply, and implementation of social mobilization campaigns advocating household water treatment and use of ORS should be prioritized to address gaps in cholera prevention and treatment activities.
Throughout history, cholera has posed a public health risk, impacting vulnerable populations living in areas with contaminated water and poor sanitation. Many studies have found a high correlation between the occurrence of cholera and environmental issues such as geographical location and climate change. Developing a cholera forecasting model might be possible if a relationship exists between the cholera epidemic and meteorological elements. Given the auto-regressive character of cholera as well as its seasonal patterns, a seasonal-auto-regressive-integrated-moving-average (SARIMA) model was utilized for time-series study from 2017 to 2022 cholera datasets obtained from the NCDC. Cholera incidence correlates positively to humidity, precipitation, minimum temperature, and maximum temperature with r = 0.1045, r = 0.0175, r = 0.0666, and r = 0.0182 respectively. Improving a SARIMA model, autoregressive integrated moving average (ARIMA), and Long short-term memory (LSTM) with the k-means clustering and discrete wavelet transform (DWT) for feature selection, the improved model is known as MODIFIED SARIMA outperforms both the LSTM, ARIMA, and SARIMA and also outperformed the improved LSTM and ARIMA with an RSS = 0.502 and an accuracy = 97% .
In African public health systems, Listeria monocytogenes is a pathogen of relatively low priority. Yet, the biggest listeriosis outbreak recorded to date occurred in Africa in 2018. This review highlights the factors that potentially impact L. monocytogenes transmission risks through African food value chains (FVCs). With the high rate of urbanisation, African FVCs have become spatially longer yet still informal. At the same time, dietary diversifications have resulted in increased consumption of processed ready‐to‐eat (RTE) meat, poultry, fishery and dairy products typically associated with a higher risk of L. monocytogenes consumer exposure. With frequent cold chain challenges, the potential of L. monocytogenes growth in contaminated RTE foods can further amplify consumer exposure risks. Moreover, the high prevalence of untreated HIV infections, endemic anaemia, high fertility rate and a gradually increasing proportion of elderly persons expands the fraction of listeriosis‐susceptible groups among African populations. With already warmer tropical conditions, the projected climate change‐induced increases in ambient temperatures are likely to exacerbate listeriosis risks in Africa. As precautionary approaches, African countries should implement systems for the detection and reporting of listeriosis cases and food safety regulations that provide L. monocytogenes standards and limits in high‐risk RTE foods.
Purpose: Cholera is among the leading causes of death in Nigeria. The main predictors of cholera transmission remain the lack of access to potable water and good sanitary conditions. Cholera is also linked to weather variables such as maximum temperatures, high Rainfall, and humidity. The relationship between cholera cases and weather variables depends on location, time, or season; hence, it is a time series dataset. This research aims to enhance the seasonal autoregressive integrated moving average (SARIMA) model by incorporating the discrete wavelet transform (DWT). Methods: This research proposed a novel approach to forecasting cholera using the SARIMA model by incorporating DWT as a dimensionality reduction technique and a K-means clustering algorithm for outlier detection. The enhanced model is termed the "Enhanced seasonal autoregressive integrated moving average" (ESARIMA). DWT is a good dimensionality reduction technique for time series data and extracts the best features for forecasting to have better prediction accuracy and minimal error. Result: The results show that ESARIMA (accuracy = 97%, RSS = 0.502) outperformed the existing model, SARIMA (accuracy = 91.61%, RSS = 0.60). Conclusion: Nigeria's weekly and monthly cholera outbreaks exhibit stochastic seasonal time series behavior that becomes stationary after the first seasonal differencing; hence, it could be forecasted with specific time series models.
Knowing the seasonality of COVID-19 helps decision-makers to take suitable interventions against the pandemic. In this study, we performed the Brown-Forsythe variance analysis on seasonal variations on different indicators based on the data on COVID-19 for the United States provided publicly by WHO. Our study finds that the seasonality of weekly cases and deaths of COVID-19 are strongly statistically supported by the data. The weekly total cases(/deaths) in winter are three to seven times(/two to three times) more than the other three single seasons. The ICU patients in winter and autumn are four to five times more than spring. The weekly hospital admissions in winter are four times more than spring. The mean of the positive rate in winter is five times more than spring. The findings of this research can be a reference in decision-making when taking interventions against the pandemic, such as taking stricter interventions in winter while considering less strict interventions in summer, etc.KeywordsCOVID-19SeasonalityVariance analysis
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Understanding the mechanism of biofilm formation is the first step in determining its function and, thereby, its impact and role in the environment. Extensive studies accomplished during the past few years have elucidated the genetics and biochemistry of biofilm formation. Cell-to-cell communication, that is, quorum sensing, is a key factor in the initiation of biofilm. Occurrence of viable but nonculturable bacteria, including Vibrio cholerae in biofilms has been reported and most likely such cells were overlooked previously because appropriate methods of detection were not employed. For this reason discovery and investigation of this important bacterial ecological niche in the environment were impeded.
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The causative agent of cholera, Vibrio cholerae, has been shown to be autochthonous to riverine, estuarine, and coastal waters along with its host, the copepod, a significant member of the zooplankton community. Temperature, salinity, rainfall and plankton have proven to be important factors in the ecology of V. cholerae, influencing the transmission of the disease in those regions of the world where the human population relies on untreated water as a source of drinking water. In this study, the pattern of cholera outbreaks during 1998-2006 in Kolkata, India, and Matlab, Bangladesh, and the earth observation data were analyzed with the objective of developing a prediction model for cholera. Satellite sensors were used to measure chlorophyll a concentration (CHL) and sea surface temperature (SST). In addition, rainfall data were obtained from both satellite and in situ gauge measurements. From the analyses, a statistically significant relationship between the time series for cholera in Kolkata, India, and CHL and rainfall anomalies was determined. A statistically significant one month lag was observed between CHL anomaly and number of cholera cases in Matlab, Bangladesh. From the results of the study, it is concluded that ocean and climate patterns are useful predictors of cholera epidemics, with the dynamics of endemic cholera being related to climate and/or changes in the aquatic ecosystem. When the ecology of V. cholerae is considered in predictive models, a robust early warning system for cholera in endemic regions of the world can be developed for public health planning and decision making.
In this study, we aimed to describe the evolution of three cholera epidemics that occurred in Lusaka, Zambia, between 2003 and 2006 and to analyse the association between the increase in number of cases and climatic factors. A Poisson autoregressive model controlling for seasonality and trend was built to estimate the association between the increase in the weekly number of cases and weekly means of daily maximum temperature and rainfall. All epidemics showed a seasonal trend coinciding with the rainy season (November to March). A 1 degrees C rise in temperature 6 weeks before the onset of the outbreak explained 5.2% [relative risk (RR) 1.05, 95% CI 1.04-1.06] of the increase in the number of cholera cases (2003-2006). In addition, a 50 mm increase in rainfall 3 weeks before explained an increase of 2.5% (RR 1.02, 95% CI 1.01-1.04). The attributable risks were 4.9% for temperature and 2.4% for rainfall. If 6 weeks prior to the beginning of the rainy season an increase in temperature is observed followed by an increase in rainfall 3 weeks later, both exceeding expected levels, an increase in the number of cases of cholera within the following 3 weeks could be expected. Our explicative model could contribute to developing a warning signal to reduce the impact of a presumed cholera epidemic.
By retrospective analysis of 65 placental localization studies by a computer-linked gamma camera the isotope studies by a computer-linked gamma camera the isotope uptake patterns were correlated with the eventual outcome of the pregnancies. The uptakes by anterior and lateral placentae were reduced in pregnancies which resulted in growth-retarded babies and statistically unrelated to the gestation of the pregnancy. This simple representation of placental blood flow could be a clinically useful index of placental function.