El Niño and the shifting geography of cholera in Africa
Sean M. Moore
, Andrew S. Azman
, Benjamin F. Zaitchik
, Eric D. Mintz
, Joan Brunkard
, Dominique Legros
, Heather McKay
, Francisco J. Luquero
, David Olson
, and Justin Lessler
Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205;
Department of Biological Sciences, University of Notre
Dame, Notre Dame, IN 46556;
Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN 46556;
Department of Earth and Planetary Sciences, Johns
Hopkins University, Baltimore, MD 21218;
Division of Foodborne, Waterborne and Environmental Diseases, Centers for Disease Control and Prevention, Atlanta,
Department of Pandemic and Epidemic Diseases, World Health Organization, 1211, Geneva, Switzerland;
Epicentre, 75012 Paris, France;
of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205; and
Médecins Sans Frontières, 75011 Paris, France
Edited by Andrea Rinaldo, Laboratory of Ecohydrology (ECHO/IIE/ENAC), Ecole Polytechnique Federale Lausanne, and approved March 8, 2017 (receivedfor
review October 18, 2016)
The El Niño Southern Oscillation (ENSO) and other climate patterns
can have profound impacts on the occurrence of infectious dis-
eases ranging from dengue to cholera. In Africa, El Niño conditions
are associated with increased rainfall in East Africa and decreased
rainfall in southern Africa, West Africa, and parts of the Sahel.
Because of the key role of water supplies in cholera transmission,
a relationship between El Niño events and cholera incidence is
highly plausible, and previous research has shown a link between
ENSO patterns and cholera in Bangladesh. However, there is little
systematic evidence for this link in Africa. Using high-resolution
mapping techniques, we find that the annual geographic distribu-
tion of cholera in Africa from 2000 to 2014 changes dramatically,
with the burden shifting to continental East Africa—and away
from Madagascar and portions of southern, Central, and West
Africa—where almost 50,000 additional cases occur during El Niño
years. Cholera incidence during El Niño years was higher in regions
of East Africa with increased rainfall, but incidence was also higher
in some areas with decreased rainfall, suggesting a complex re-
lationship between rainfall and cholera incidence. Here, we show
clear evidence for a shift in the distribution of cholera incidence
throughout Africa in El Niño years, likely mediated by El Niño’s
impact on local climatic factors. Knowledge of this relationship
between cholera and climate patterns coupled with ENSO fore-
casting could be used to notify countries in Africa when they are
likely to see a major shift in their cholera risk.
El Niño Southern Oscillation
climate and health
Improvements to water and sanitation have eliminated the
threat of cholera throughout much of the world; however, each
year, millions are infected and over a hundred thousand die in
Asia, Africa, and the Caribbean (1). Cholera’s impact may be the
greatest in Africa, where there has been ongoing circulation
since the 1970s (2) and unexpected, explosive epidemics have
been associated with case fatality rates (CFRs) as high as 50%
(commonly 1–15%) (3, 4). Reported CFRs for Africa remain
twice as high as the 2014 global average of 1.2% (5). Cholera
epidemics in sub-Saharan Africa have proven difficult to fore-
cast, hampering prevention and control efforts (6, 7). However, it
has long been believed that climatic factors in general, and the El
Niño Southern Oscillation (ENSO) in particular, are important
drivers of cholera incidence (8, 9).
ENSO is a periodic, multiannual variation in sea surface
temperatures and winds in the tropical Pacific Ocean that in-
fluences weather patterns globally (10, 11). Warm phases in the
eastern Pacific Ocean (El Niño events) occur every 2–7yand
are associated with warm sea surface temperatures in parts of
the western Indian Ocean, above-average rainfall in East
Africa, and below-average rainfall in dry regions of southern
Africa and the Sahel (Fig. 1A). The 2015–2016 El Niño event is
only the third of the past 40 y to be classified as “strong”or
“very-strong”(joining 1982–1983 and 1997–1998; Fig. 1D) (12).
The global link between cholera and climate has been the
modern research has focused on South Asia, where interannual
variability in seasonal cholera epidemic size has been associated
with ENSO strength (8, 14, 15).
Warm sea surface temperatures in the Bay of Bengal that often
accompany El Niño events may facilitate the growth of environ-
mental reservoirs of Vibrio cholerae, increasing the severity of that
year’s epidemic (8, 14, 16). In sub-Saharan Africa, however, the
majority of cholera cases occur in inland regions (17), hence sea
surface temperatures are unlikely to play as direct a role in driving
seasonal and multiannual variations in cholera incidence. There is
some evidence that cholera incidence in the Great Lakes Region of
East Africa increases during abnormally warm El Niño events (18,
19). Large cholera epidemics in Africa are also associated with both
very dry and very wet conditions: dry conditions may force people to
use unsafe drinking water sources (17, 20, 21), whereas flooding
may facilitate fecal contamination of drinking water (22). This
complexity combined with the lack of fine-scale data on cholera
incidence and environmental covariates has limited our under-
standing of how climatic events, like El Niño, impact cholera inci-
dence on the continent.
Whereas vulnerability to cholera outbreaks is driven by local
conditions, including safe water and sanitation access, health
infrastructure, and socioeconomic factors, ENSO-related climate
perturbations may also modify the distribution of cholera risk.
To understand how ENSO affects the geographic distribution of
cholera incidence in Africa, we mapped estimated cholera in-
cidence at the scale of 20 ×20 km grid cells throughout
the continent. Using a hierarchical Bayesian approach, we
In the wake of the 2015–2016 El Niño, multiple cholera epidemics
occurred in East Africa, including the largest outbreak since the
1997–1998 El Niño in Tanzania, suggesting a link between El
Niño and cholera in Africa. However, little evidence exists for
this link. Using high-resolution mapping techniques, we found
the cholera burden shifts to East Africa during and following El
Niño events. Throughout Africa, cholera incidence increased
three-fold in El Niño-sensitive regions, and 177 million people
experienced an increase in cholera incidence. Without treatment,
the case fatality rate can reach 50%, but accessible, appropriate
care nearly eliminates mortality. Climatic forecasts predicting El
Niño events 6–12 mo in advance could trigger public health
preparations and save lives.
Author contributions: S.M.M., A.S.A.,B.F.Z., E.D.M., F.J.L., andJ.L. designed research; S.M.M.,
A.S.A., B.F.Z., E.D.M., F.J.L. and J.L. conceived this study; S.M.M., A.S.A., B.F.Z., J.B., D.L.,
A.H., H.M., F.J.L., D.O., and J.L. performed research; S.M.M., A.S.A., B.F.Z., D.L., A.H., H.M.,
F.J.L.,D.O., and J.L. contributed to thecollection, assembly, andentry of data; S.M.M., A.S.A.,
B.F.Z., and J.L. analyzed data; and S.M.M., A.S.A., and J.L. wrote the paper.
The authors declare no conflict of interest.
This article is a PNAS Direct Submission.
Freely available online through the PNAS open access option.
To whom correspondence should be addressed. Email: firstname.lastname@example.org.
This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.
www.pnas.org/cgi/doi/10.1073/pnas.1617218114 PNAS Early Edition
integrated data from over 17,000 annual observations of chol-
era incidence from 2000 to 2014 in over 3,000 unique locations
of varying spatial extent, ranging from entire countries to
neighborhoods. The resulting maps reflect modeled cholera
incidence at a fine spatial resolution using reported counts of
cholera cases, key explanatory variables (population density,
access to improved drinking water, access to improved sanita-
tion, and distance to nearest major water body), and a spa-
tially dependent covariance term (Materials and Methods). We
then examined the potential mechanistic association between
ENSO-related changes in cholera incidence and several envi-
ronmental variables including rainfall.
El Niño events affect the distribution and magnitude of cholera
incidence throughout the African continent (Figs. 1Band 2).
Africa as a whole experienced a similar number of cholera cases
in El Niño years compared with non-El Niño years between
2000–2014 [215,546 (95% credible interval [CrI]: 209,770–
221,704) vs. 209,791 (95% CrI: 202,087–219,047)], but the geo-
graphic distribution of these cases fundamentally changed be-
tween El Niño and non-El Niño years, with most countries
experiencing areas of both decreased and increased incidence
(Fig. 1B). However, notable regional patterns were observed;
southern Africa experienced fewer cholera cases in El Niño years
(31,598 fewer cases; 95% CrI: 29,385–33,775), whereas conti-
nental East Africa (i.e., excluding Madagascar) had significant
increases in cholera incidence in El Niño years (Fig. 1B), with
48,670 (95% CrI: 45,192–52,053) excess cases (SI Appendix,
Table S4). Overall, 177 million people live in areas where annual
cholera incidence increased by at least 1 per 100,000 during El
Niño years (95% CrI: 166.0–189.5 million), and 81 million live in
areas where annual incidence increased by more than 1 per 10,000
(95% CrI: 75.8–86.4 million). Likewise, 137 million live in areas
where annual incidence declined by at least 1 per 100,000 (95%
CrI: 125.6–149.4 million) and 69 million live in areas where
annual incidence decreased by more than 1 per 10,000 (95%
CrI: 63.2–74.2 million).
1982 1986 1990 1994 1998 2002 2006 2010 2014
−2 −1 0 12
3−month ENSO Index
Fig. 1. Geographical distribution of cholera in El Niño and non-El Niño years. (A) Rainfall anomalies in El Niño years, 1980–2015. (B) Fine-scale geographic distribution
of cholera anomalies in El Niño years, 2000–2014. (C) Country-level cholera anomalies in El Niño years, based on WHO reports, 1980–2015. The colors represent the
likelihood ratio in support of a significant difference in cholera incidence between El Niño and non-El Niño years. (D) History of strength of ENSO anomalies, 1980–
2015. El Niño years are in red, and La Niña years are in blue. Red and blue outlines in A–Crepresent regions with positive (red) and negative (blue) sensitivity to El Niño
events in cholera incidence selected by smoothing the normalized difference in cholera incidence using a kernel smoothing algorithm with a bandwidth of 150 km and
then clustering areas into areas where cholera incidence is positively sensitive, negatively sensitive, and insensitive to El Niño events (Fig. 2).
east and southeast
Negatively Sensitive Insensitive Positively Sensitive
ENSO Sensitivity Clusters
Cases per 10,000
non El Niño
Fig. 2. Geographical distribution and incidence rates
for El Niño-sensitive regions. (A)Regionswithpositive
(red) and negative (blue) sensitivity to El Niño events
in cholera incidence. Areas selected by smoothing the
normalized difference in cholera incidence using a
kernel smoothing algorithm with a bandwidth of
150 km, then clustering areas into areas where cholera
incidence is positively sensitive (red), negatively sensi-
tive (blue) and insensitive (white) to El Niño events.
Callouts indicate major reported outbreaks of the
2015–16 cholera season (SI Appendix,1. SI Materials
and Methods). (B) Kernel density (violin) plot of cases
per 10,000 in different ElNiño-sensitive regions during
El Niño and non-El Niño years. Black circles are grid
cell-level medians ±1 SD and blue diamonds are grid-
cell level means. (C) Overlay of El Niño sensitive clus-
ters from holding out each El Niño or non-El Niño pair
of years with negatively sensitive clusters =−1(blue)
and positively sensitive clusters =1(red).
www.pnas.org/cgi/doi/10.1073/pnas.1617218114 Moore et al.
To delineate areas that can expect significant increases or
reductions in cholera incidence during El Niño years, we classi-
fied areas, irrespective of political boundaries, based on the
sensitivity of local cholera incidence to El Niño using clustering
and smoothing algorithms. We identified those areas with the
largest increase in expected incidence during El Niño events (the
top quartile) and those with the largest decrease (the bottom
quartile, see Materials and Methods). The largest positively sen-
sitive cholera cluster extends through continental East Africa
from the horn of Africa down to Mozambique, with smaller
clusters scattered throughout the continent (Fig. 2A). The most
distinct negatively sensitive clusters are in Madagascar and
portions of Central and West Africa (Fig. 2A). Overall, 45.8% of
people in sub-Saharan Africa live in El Niño-sensitive areas:
263.6 million in positively sensitive clusters and 203.7 million in
negatively sensitive ones. During El Niño years, cholera inci-
dence within positively sensitive clusters increased, on average,
almost threefold from 1.1 per 10,000 to 3.3 per 10,000 [relative
rate (RR): 2.91; 95% CrI: 2.52–3.19], corresponding to almost
55,000 excess cases (Fig. 2B). In negatively sensitive clusters in-
cidence decreased from 4.2 per 10,000 to 2.2 per 10,000 (RR:
0.53, 95% CrI: 0.48–0.57), a reduction of nearly 40,000 cases. A
sensitivity analysis showed that the locations of these clusters
were not driven by a single year or El Niño event, (Fig. 2C).
Because we were only able to perform fine-scale mapping of
cholera incidence from 2000 to 2014, there are a limited number
of El Niño events captured in these analyses (2 weak, 2 moder-
ate, and 0 strong/very-strong events; SI Appendix, Table S2). To
confirm that recently observed geographic patterns hold over a
longer time scale, we analyzed country-level incidence data
reported to the WHO dating back to 1980 (23). Similar to the
fine-scale analyses, increases in cholera incidence are concen-
trated in continental East Africa, specifically Tanzania and
Kenya, where El Niño events are associated with increased
rainfall and generally wet conditions (Fig. 1A), whereas the
largest decreases are in Madagascar and Namibia. There is a
significant positive association between ENSO strength and
cholera incidence in continental East Africa with above-average
rainfall during El Niño events (Fig. 1 Aand C), with incidence
increasing by 29,226 (95% CrI: 9,403–49,049) cases for every unit
increase in the annual peak ENSO index value (SI Appendix, Fig.
S45 and 2. SI Further Results).
At the continental scale there was no strong association be-
tween rainfall patterns and cholera incidence. However, areas of
higher cholera incidence during El Niño years were concentrated
in river basins where rainfall anomalies were either below aver-
age (RR: 4.4 in basins with rainfall anomalies in the lowest
quartile; P<0.001; 95% CrI: 2.5–7.8) or above average (RR:
2.4 in basin with rainfall anomalies in the upper quartile; P=
0.003; 95% CrI: 1.4–4.2) compared with areas where rainfall
anomalies were insignificant (Fig. 3). In addition, higher cholera
incidence during El Niño years was also concentrated in river
basins with an above-average normalized difference vegetation
index (NDVI), evapotranspiration, and temperature (SI Appen-
dix, Figs. S22–S26). The positive association between higher
rainfall and higher cholera incidence was concentrated in con-
tinental East and southern Africa (encompassing much of the
positively sensitive regions in Fig. 1A), although this association
was only significant in continental East Africa (RR: 3.5; 95%
CrI: 1.4–9.0; Fig. 3C). Cholera incidence in West and Central
Africa showed no clear positive association with rainfall, but
incidence was significantly higher in areas of East Africa (RR:
4.7; 95% CrI: 1.6–13.5), West Africa (RR: 6.8; 95% CrI: 2.3–
20.3), and Central Africa (RR: 18.7; 95% CrI: 3.7–94.4) with
below-average rainfall during El Niño years (Fig. 3C).
Positive El Niño-associated rainfall anomalies were concen-
trated in positively sensitive cholera clusters. This association
was due to positive rainfall anomalies in continental East Africa,
as rainfall during El Niño years was either normal or below av-
erage in the positively sensitive cholera clusters in the other
geographic regions (SI Appendix, Fig. S28). The local association
between cholera incidence and rainfall anomalies was not con-
stant across the continent and varied by mean annual rainfall
levels. In drier areas, cholera incidence increased during both low
and high rainfall years, whereas in the wettest areas, cholera in-
cidence tends to be below average during high rainfall years (SI
Appendix, Fig. S27). The concentration of high-rainfall areas in
West and Central Africa may explain the lack of correlation be-
tween increased rainfall and cholera incidence in these regions,
whereas the large region of low to moderate rainfall in East
Africa may be responsible for the positive relationship between
increased rainfall and cholera incidence observed during El Niño
events. Cholera incidence in coastal areas is positively associated
with sea surface temperature anomalies, particularly where
anomalies are >0.2 °C; however, these regions also have positive
rainfall anomalies (SI Appendix,2. SI Further Results).
The annual reported incidence of cholera in sub-Saharan Africa did
not differ significantly between El Niño and non-El Niño years from
2000 to 2014. However, we found evidence of a large-scale shift in
incidence within the continent, highlighted by a large increase in
cholera incidence in East Africa during and after El Niño events
resulting in almost 50,000 additional annual cases. In addition to
this large positively sensitive region, we identified several other re-
gions with increased cholera incidence during El Niño events, as
well as several regions in Central and West Africa that appear to be
negatively sensitive to El Niño events, with decreased incidence
during these years. Although we did not find a strong, continent-
wide association between El Niño-associated meteorological
anomalies and changes in cholera incidence, increased incidence
was associated with positive rainfall anomalies in eastern and
southern Africa, suggesting that these relationships may vary from
place to place and should be assessed at a small scale.
The 2015–2016 surge in cholera cases in East Africa was not
used in these analyses to define cholera clusters, but, neverthe-
less, these outbreaks are concentrated in positively El Niño-
sensitive areas (Fig. 2A). Since August 2015, Tanzania has
experienced a nation-wide outbreak that has infected 25,482 and
killed 299 as of May 17, 2016. Somalia, Kenya, Malawi, Uganda,
and Mozambique have also experienced outbreaks since the
summer of 2015 (SI Appendix,1. SI Materials and Methods).
These outbreaks (including Tanzania but excluding Somalia; SI
Appendix,1. SI Materials and Methods) have resulted in at least
34,800 reported cases since August 2015, a finding that does not
include the peak 2016 cholera season in most of these countries.
This result equates to nearly 25,000 more cases than would be
expected over the same time period during non-El Niño years,
and in-line with the 34,713 cases (95% CrI: 31,043–38,018) es-
timated from our analyses for these countries during a moderate
El Niño year. At the edge of the East African positively sensitive
cluster, eastern Democratic Republic of Congo has been expe-
riencing high cholera incidence, although this trend started be-
fore the current El Niño event. Cholera has not, however, been
confined to positively sensitive clusters. El Niño-insensitive re-
gions of western Uganda have recently experienced several small
outbreaks, as has the Ekiti state of Nigeria and Lusaka, Zambia.
The presented shifts in cholera burden associated with El Niño
events are based on estimates of reported incidence, not true
incidence, due to a lack of specific information regarding spatial
and temporal reporting biases. Although evidence suggests there
are country-specific reporting biases for cholera that lead to both
overestimation of cholera incidence during some epidemics and
the underestimation of true cholera incidence in other settings
(24), as long as these biases are not temporally variable they
should not influence the association of cholera with ENSO.
However, if the size of outbreaks are systematically under-
reported, then the magnitude of the shift in cholera incidence
during El Niño events may be underestimated due to missed
cases. Case over- or underestimation would not shift the di-
rection of El Niño-sensitivity unless the quality of surveillance
and reporting efforts were associated with the occurrence of El
Moore et al. PNAS Early Edition
Niño events. We have not found any evidence of increased or
decreased surveillance due to ENSO patterns. However, regions
where cholera cases are underreported or not reported at all may
be incorrectly categorized as ENSO-insensitive due to a lack of
information about when cholera cases occur in these areas.
Several recent studies have found that extreme El Niño events
could become more frequent due to climate change (25–27).
This finding suggests that shifts in cholera associated with El
Niño events may become more pronounced in the future, per-
haps shifting the cholera burden toward East Africa. The be-
havior of ENSO under climate change and its future impacts on
Africa are, however, active topics of research, so projections are
highly uncertain (28). Moreover, ENSO is only one way in which
greenhouse gas induced warming influences the African climate.
In the Horn of Africa, for example, El Niño is associated with
wet conditions and higher rates of cholera (Fig. 1). However, the
Horn of Africa has experienced significant drying in recent de-
cades (29). Furthermore, although the majority of global climate
models project that the region will get wetter in the 21st century,
these models have systematic errors in representing the seasonal
distribution of Horn of Africa rainfall and could be producing
spurious projections (30). Thus, it is not clear how a potential
increase in wet El Niño extremes superimposed on a warming-
induced drying trend in the Horn of Africa would affect overall
cholera risk in that region.
Predicting local cholera incidence is a difficult task. However,
here we show clear evidence for a shift in the distribution of cholera
incidence throughout regions of Africa in El Niño compared with
non-El Niño years, likely mediated by El Niño’s impact on local
climatic factors. Recent ENSO forecasting models warn of developing
El Niño conditions up to 12 mo in advance (31). As predictive ability
of ENSO anomalies improves (31, 32), our findings provide hope
that we may be able to provide early cholera-risk forecasts. Be-
cause effective case management dramatically decreases mortality
in cholera outbreaks, and new control tools (e.g., oral cholera
vaccines) may prevent, or at least limit, outbreaks, the ability to
step up surveillance, preparedness and response when local risk is
high can have a significant impact on saving lives. A better under-
standing of the mechanisms by which El Niño changes the distri-
bution of cholera incidence will help us elucidate the effect of
climatic change on the global distribution of cholera risk.
Materials and Methods
Cholera Data. The cholera data used to generate the fine-scale maps of
cholera incidence were collated from 360 separate datasets (details and data
are available at www.iddynamics.jhsph.edu/projects/cholera-dynamics/data).
Annual case counts reported to the WHO from 2000 to 2014 were included
for each country in sub-Saharan Africa (23). Further details on data sources
are provided in SI Appendix,1. SI Materials and Methods. The datasets in-
cluded in our analysis included cholera case counts aggregated at various
time scales from daily to yearly. To estimate annual incidence rates, obser-
vations at subannual time scales were aggregated to the annual level, al-
though many aggregated annual observations cover only part of a calendar
year. A total of 17,033 annual observations from 3,071 unique locations
from 2000 to 2014 were included in the main analysis (Dataset S1). These
3,071 unique locations include 44 different countries, 327 first-level admin-
istrative units, 1,948 second-level administrative units, and 752 locations at
the third-level administrative unit or lower (SI Appendix, Fig. S1). A summary
of the cholera data used to model the spatial distribution of cholera in-
cidence is provided by country (SI Appendix, Tables S1 and S2 and 1. SI
Materials and Methods).
Although our database does not cover 2015 and 2016, we used three
primary sources for data to provide an overview of the main cholera ep-
idemics that have occurred since the onset of the 2015–2016 El Niño event,
assumed to be April 1, 2015 (SI Appendix,TableS3). First, we performed a
query on HealthMap.org (March 21, 2016) for the disease cholera using all
sources and restricted to Africa. Second, we obtained updated situation
reports from the WHO for all available countries. Finally, we used the
weekly data reported from UNICEF’sWestAfricaregionalofficeasofweek
6, 2016 (33).
Central Africa East Africa North Africa Souther n Africa West Africa
Difference in log cholera incidence
Fig. 3. Association between cholera and rainfall by river basin. (Aand B)
River basin-level rainfall anomalies (anomalies smaller than ±5% not shown)
(A) and (log) cholera anomalies (B) between El Niño and non-El Niño years.
(C) River basin-level cholera incidence anomalies by region and the strength
of rainfall anomalies. Positive cholera anomalies are associated with nega-
tive rainfall anomalies (lowest quartile) in every region and positive rainfall
anomalies (highest quartile) in East and Southern Africa. *The difference in
log cholera incidence between El Niño and non-El Niño years for a given
rainfall anomaly quartile and geographical region is significantly different
from the difference in log incidence for areas within that region with rainfall
anomalies that fall in the low to mid or mid to high ranges (second and third
www.pnas.org/cgi/doi/10.1073/pnas.1617218114 Moore et al.
Covariates and Climate Variables. Gridded population density at a 1km
resolution for the entire African continent were obtained from WorldPop
(www.worldpop.org.uk; accessed March 30, 2016). Distance to the coast was
calculated from a shapefile of the African coastline using the “rgeos”
package in R (34). Distances to the nearest large lake or reservoir (surface
area: >50 km
) and to the nearest permanent smaller water bodies including
rivers (surface area: >0.1 km
) were calculated using the level one and level
two layers from the Global Lakes and Wetlands Database (35). The pro-
portion of the population with access to improved drinking water (SI Ap-
pendix, Fig. S2), improved sanitation (SI Appendix, Fig. S3), and open
defecation were obtained from ref. 36. All covariate data were resampled to
the same 20 km resolution using the “raster”package in R (37).
Gridded rainfall at a spatial resolution of 0.05° from 1981 through 2015 was
obtained from Climate Hazards Group InfraRed Precipitation with Station data
version 2.0 (38). Rainfall totals were aggregated at an annual time scale run-
ning according to the El Niño cycle, which runs from July to June rather than
the calendar year. Gridded mean temperature, soil moisture, and evapo-
transpiration data at a 0.25° spatial resolution were obtained from the Global
Land Data Assimilation System (39). Annual NDVI values at 0.05° from 2000 to
2014 were aggregated from monthly MODIS data (40). Monthly sea surface
temperatures at a 2.0° spatial resolution from 2000 to 2014 were obtained
from the Extended Reconstructed Sea Surface Temperature dataset (41). For
each climate variable long-term annual means were calculated for the periods
from 2000 to 2014 and 1981 to 2014 (rainfall only), and deviations from these
long-term means were calculated for El Niño years. All land-based climate
variables were resam pled to 20 ×20 km using the raster package in R (37).
El Niño Analyses. We classified each year as having no El Niño anomaly, or as
being a weak, moderate or strong El Niño year based on the Oceanic Niño
Index (ONI) used by the National Oceanic and Atmospheric Administration.
Monthly ONI values are calculated from the 3-mo running mean of sea
surface temperatures anomalies in the Niño 3.4 region of the Pacific Ocean
(5°N–5°S, 120°W–170°W). A year with at least 5 consecutive months with an
ONI value ≥0.5 °C is classified as an El Niño event; and El Niño events with
at least 3 consecutive months ≥1.0 °C, 1.5 °C, or 2.0 °C are classified as
moderate, strong, or very strong events, respectively (Fig. 1D). El Niño
events typically overlap calendar years, so we classified El Niño years as
running from July through June of the following calendar year. Because
the majority of the available cholera data are aggregated at an annual
level we classified cholera cases occurring during both calendar years
overlapping an El Niño event as associated with an El Niño year. For ex-
ample, the very strong 1997–1998 El Niño event translates to an 1997 El
Niño year and cholera cases from both 1997 and 1998 were considered to
be associated with the 1997–1998 event.
Using ONI values, from 2000 to 2014, the years 2000, 2001, 2008, 2011,
2012, 2013, and 2014 were classified as non-El Niño years. The years from
2004 to 2007 were classified as weak El Niño events with ONI values
of ≥0.5 °C but <1 °C, and 2002–2003 and 2009–2010 were classified as
moderate-to-strong El Niño years with ONI values ≥1 °C for a minimum of
3 mo. The analysis presented in the main text included both weak and
moderate-to-strong El Niño events as El Niño years. In SI Appendix,2.3
Sensitivity to the Definition of El Niño Years, we present an analysis using
only moderate-to-strong El Niño years to understand how the main results
vary with different El Niño classifications.
Mapping Methodology. A hierarchical Bayesian modeling framework was
used to map aggregated cholera observational data to underlying incidence
rates. The entire study region was divided into N
=73,979 20 ×20 km grid
cells, with any grid cells entirely covered by water or with a population
density of 0 excluded from analysis. The annual cholera incidence in each
grid cell, λ
, was modeled using a log-linear regression equation,
with covariates X
. The random effects, ψ
, account for any overdispersion
and spatial correlation in the data and are modeled by a conditional
autoregressive distribution (42, 43). Spatial correlation between random
effects is determined by a binary Nj X Njadjacency matrix, A, with element
aj,kequal to one if grid cells ðj,kÞare neighbors (sharing an edge), and zero
otherwise (and for j=k). The joint distribution of ψis an Nj-dimensional
multivariate normal distribution given by
ψj∼ N0, σ2
where ρis a parameter representing the relative strength of spatial
dependence with 0 ≤ρ<1 and Dis a diagonal matrix with entries
aj,k, where dj,jrepresents the number of neighbors for grid cell j
Each observation, Yi, was mapped to the underlying grid cells that are
within area iand were modeled by a Poisson process:
The expected number of cases, Ei, for each observation is the sum of the
expected number of cases in each grid cell:
is the size of the population in grid cell j. Each area-based obser-
vation was associated with a polygon and the determination of which grid
cells were associated with each observation was performed by using the
’extract’function from the raster library in R (37). For point-based obser-
vations, such as geolocated case data or cases from a single refugee camp,
the grid cell containing that GPS point was used. Multiple observations for
the same area covering different temporal periods or from different sources
were treated as independent observations, and data from different, but
overlapping, spatial scales were also treated as independent observations.
The intercept term of the log-linear regression model, β
and the regression
) were assigned weakly informative Gaussian prior dis-
tributions, N(0,10). The spatial autocorrelation parameter ρwas assigned a β(2,1)
prior and the precision parameter τ
from the spatial autocorrelation term ψ
assigned a Γ(0.5,0.0005) prior distribution. The covariates included in our analysis
were level of access to improved drinking water, level of access to improved
sanitation, population density, distance to the nearest coastline, and distance to
the nearest major waterbody. The relationship between cholera incidence and
these covariates was considered separately for El Niño and non-El Niño years to
determine whether their relationship was altered by weather patterns associated
with the ENSO cycle. Details on model implementation are provided in SI Ap-
pendix, and summary model outputs are provided in Datasets S2–S5.
To test the sensitivity of our results to single El Niño or La Niña events, we
reran the model while holding out each single pair of years representing
either an El Niño event or a non-El Niño event (eight pairs of years; with the
exception of the non-El Niño year 2008, between the 2006–2007 and 2009–
2010 El Niño events, where only a single year was withheld from the anal-
ysis). The variation in incidence when different El Niño and non-El Niño years
were withheld are presented in SI Appendix, Figs. S14 and S15. The sensi-
tivity of the shift in incidence during El Niño events to holding out different
years is presented in SI Appendix, Fig. S16.
Clustering of Cholera El Niño-Sensitive Regions. El Niño-sensitive regions were
identified by smoothing maps of normalized cholera incidence and then
clustering the smoothed incidence by quartiles. Cholera incidence was first
normalized by dividing the difference in cholera incidence in El Niño versus
non-El Niño years by the square root of the summed variance from El Niño
and non-El Niño years. The distribution function of the normalized cholera
incidence was then smoothed using a kernel smoother with a bandwidth of
150 km and grid cells weighted by population density (47, 48). Smoothing
was implemented using the image.smooth function in the “fields”R pack-
age (49). The results of the smoothing and subsequent clustering with al-
ternative bandwidth sizes ranging from 50 to 300 km are shown in SI
Appendix, Fig. S17. Grid cells in the lowest quartile were classified as neg-
atively sensitive clusters, whereas grid cells in the upper quartile were clas-
sified as positively sensitive clusters. Grid cells in the middle quartiles were
classified as insensitive. The sensitivity of the clustering results to holding out
each pairing of El Niño and non-El Niño years is presented in Fig. 2C.
Country-Level ENSO Anomalies. In addition to using the detailed incidence
estimates data from 2000 to 2014, we also used official country-level annual
reports from the WHO dating back to 1980 to understand the relationship
between El Niño years and cholera incidence (23). The results presented in
Fig. 1Care based on a linear regression models for each country of the form:
is the number of cases in year yand EN represents the set of years
with an El Niño event. We explored variants of this model that included a
linear term for trends in reporting over time and those using EN sets
(i.e., weak and stronger or moderate and stronger). Within Fig. 1C,we
Moore et al. PNAS Early Edition
highlighted countries based on the likelihood ratio comparing models with
and without β
, with the darker colors illustrating larger support for a sig-
nificant different between El Niño and non-El Niño years.
Association of Cholera with Local Climate. We examined the spatial distri-
bution of the following six climatic factors estimated from remote sensing
and climate reanalysis in El Niño and non-El Niño years between 2000 and
2014: rainfall, temperature, NDVI, soil moisture, evapotranspiration, and sea
surface temperature. Of the five land-based factors, deviations in rainfall
differed by >20% over the largest proportion of the sub-Saharan African
land area (5.0%; Fig. 3A). Because of its strong association with El Niño
events, high correlation with other climatic factors, and clear mechanistic
relationship with cholera transmission, here we focus primarily on the re-
lationship between rainfall and cholera (analyses of other climatic factors
are included in SI Appendix,2. SI Further Results). The association between
rainfall and cholera was examined by aggregating ENSO-associated rainfall
anomalies and incidence at the river-basin scale because of the impact of
rainfall within a river basin on local surface water and flooding. The major
river basins of Africa, along with their subbasins used in this analysis, were
obtained from the World Wildlife Fund HydroSHEDS project (50). Because
we did not observe a simple linear relationship between ENSO-associated
shifts in climate and cholera at the continental- or basin-scale, the relationship
between climate measures and shifts in cholera incidence at the basin-scale
were tested by aggregating climate anomalies into quartiles and then com-
paring shifts in cholera incidence in the areas with climate anomalies in the
lower and upper quartiles to areas not experiencing significant anomalies
(middle quartiles) using one-way ANOVA tests. These statistical tests allow us
to determine whether large ENSO-associated negative or positive shifts in
climate variables such as rainfall are associated with shifts in cholera incidence.
ACKNOWLEDGMENTS. We thank Abdinasar Abubakar for providing in-
formation about current cholera outbreaks; Carla Zelaya, Marisa Hast, Kerry
Shannon, Susan Fallon, Chris Troeger, and Yuru Huang for assistance with
identifying, extracting, and entering data; and the Johns Hopkins Infectious
Disease Dynamics group for feedback on study design and analysis. Research
by S.M.M., A.S.A., H.M., and J.L. was supported by Bill and Melinda Gates
Foundation Grant OPP1127318. B.F.Z. was supported by National Science
Foundation Grant 1639214 [“Innovations at the Nexus of Food, Energy, and
Water Systems (Track 1): Understanding multi-scale resilience options for
vulnerable regions”]. Cholera data were provided by the Ministries of Health
of Benin, Democratic Republic of Congo, Mozambique, South Sudan, and
Nigeria. Additional cholera data were provided by Médecins Sans Frontières
(MSF) and MSF/Epicentre, the WHO, and the United Nations Relief Agency.
Data are indexed at www.iddynamics.jhsph.edu/projects/cholera-dynamics/
data and are either directly available or available by request of the owning
1. World Health Organization (2015) Cholera, 2014. Wkly Epidemiol Rec 90:517–528.
2. Mutreja A, et al. (2011) Evidence for several waves of global transmission in the
seventh cholera pandemic. Nature 477:462–465.
3. Siddique AK, et al. (1995) Why treatment centres failed to prevent cholera deaths
among Rwandan refugees in Goma, Zaire. Lancet 345:359–361.
4. Mengel MA, Delrieu I, Heyerdahl L, Gessner BD (2014) Cholera outbreaks in Africa.
Curr Top Microbiol Immunol 379:117–144.
5. WHO (2015) Cholera case fatality rate: Situations and trends. Available at www.who.
int/gho/epidemic_diseases/cholera/case_fatality_rate_text/en. Accessed May 5, 2016.
6. Constantin de Magny G, Guégan J-F, Petit M, Cazelles B (2007) Regional-scale climate-
variability synchrony of cholera epidemics in West Africa. BMC Infect Dis 7:20.
7. Reyburn R, et al. (2011) Climate variability and the outbreaks of cholera in Zanzibar,
East Africa: A time series analysis. Am J Trop Med Hyg 84:862–869.
8. Pascual M, Rodó X, Ellner SP, Colwell R, Bouma MJ (2000) Cholera dynamics and El
Niño-Southern Oscillation. Science 289:1766–1769.
9. Patz JA, Campbell-Lendrum D, Holloway T, Foley JA (2005) Impact of regional climate
change on human health. Nature 438:310–317.
10. Rasmusson EM, Wallace JM (1983) Meteorological aspects of the el nino/southern
oscillation. Science 222:1195–1202.
11. Ropelewski CF, Halpert MS (1987) Global and Regional Scale P recipitation Patterns
Associated with the El Niño/Southern Oscillation. Mon Weather Rev 115:1606–1626.
12. National Oceanic and Atmospheric Administration (2016) Climate Prediction Center: ENSO
diagnostic discussion. Available at www.cpc.ncep.noaa.gov/products/analysis_monitoring/
enso_advisory/ensodisc.html. Accessed April 6, 2016.
13. Bellew HW (1885) The History of Cholera in India from 1862-1881: Being a Descriptive
and Statistical Account of the Disease as Derived from the Published Official Reports
of the Several Provincial Governments During that Period and Mainly in Illustration of
the Relation Between Cholera Activity and Climatic Conditions Together with Origi-
nal Observations on the Causes and Nature of Cholera (Trubner & Co., London).
14. Colwell RR (1996) Global climate and infectious disease: The cholera paradigm.
15. Colwell RR, Huq A (1998) Global microbial ecology: Biogeography and diversity of
Vibrios as a model. J Appl Microbiol 85:134S–137S.
16. Koelle K, Rodó X, Pascual M, Yunus M, Mostafa G (2005) Refractory periods and cli-
mate forcing in cholera dynamics. Nature 436:696–700.
17. Rebaudet S, Sudre B, Faucher B, Piarroux R (2013) Environmental determinants of
cholera outbreaks in inland Africa: A systematic review of main transmission foci and
propagation routes. J Infect Dis 208:S46–S54.
18. Bompangue Nkoko D, et al. (2011) Dynamics of cholera outbreaks in Great Lakes
region of Africa, 1978-2008. Emerg Infect Dis 17:2026–2034.
19. Olago D, et al. (2007) Climatic, socio-economic, and health factors affecting human
vulnerability to cholera in the Lake Victoria basin, East Africa. Ambio 36:350–358.
20. Umoh JU, Adesiyun AA, Adekeye JO, Nadarajah M (1983) Epidemiological features of
an outbreak of gastroenteritis/cholera in Katsina, Northern Nigeria. J Hyg (Lond) 91:
21. Tauxe RV, Holmberg SD, Dodin A, Wells JV, Blake PA (1988) Epidemic cholera in Mali:
High mortality and multiple routes of transmission in a famine area. Epidemiol Infect
22. Griffith DC, Kelly-Hope LA, Miller MA (2006) Review of reported cholera outbreaks
worldwide, 1995-2005. Am J Trop Med Hyg 75:973–977.
23. World Health Organization (2014) Weekly epidemiological record: Cholera articles.
Available at www.who.int/cholera/statistics/en. Accessed March 30, 2016.
24. Ali M, Nelson AR, Lopez AL, Sack DA (2015) Updated global burden of cholera in
endemic countries. PLoS Negl Trop Dis 9:e0003832.
25. Cai W, et al. (2014) Increasing frequency of extreme El Niño events due to greenhouse
warming. Nat Clim Chang 4:111–116.
26. Latif M, Semenov VA, Park W (2015) Super El Niños in response to global warming in a
climate model. Clim Change 132:489–500.
27. Santoso A, et al. (2013) Late-twentieth-century emergence of the El Niño propagation
asymmetry and future projections. Nature 504:126–130.
28. Wang C, Deser C, Yu J-Y, DiNezio P, Clement A (2017) Coral reefs of the eastern
tropical Pacific. El Niño and Southern Oscillation (ENSO): A Review. Coral Reefs of the
Eastern Tropical Pacific, eds Glynn PW, Manzello DP, Enochs IC (Springer, Dordrecht,
The Netherlands), pp 85–106.
29. Williams AP, et al. (2011) Recent summer precipitation trends in the Greater Horn of
Africa and the emerging role of Indian Ocean sea surface temperature. Clim Dyn 39:
30. Tierney JE, Ummenhofer CC, deMenocal PB (2015) Past and future rainfall in the Horn
of Africa. Sci Adv 1:e1500682.
31. Ludescher J, et al. (2014) Very early warning of next El Niño. Proc Natl Acad Sci USA
32. Barnston AG, Tippett MK, L’Heureux ML, Shuhua L, DeWitt DG (2012) Skill of real-
time seasonal ENSO model predictions during 2002–11: Is our capability increasing?
Bull Am Meteorol Soc 93:631–651.
33. United Nations International Children’s Emergency Fund (2016) Cholera outbreaks in
Central and West Africa: 2016 regional update - Week 06. Available at https://www.
unicef.org/cholera/files/WCA_Cholera_Update_W6.pdf. Accessed April 6, 2016.
34. Bivand R, Rundel C (2014) rgeos: Interface to Geometry Engine - Open Source (GEOS).
Available at cran.r-project.org/web/packages/rgeos/index.html Accessed November
35. Lehner B, Bernhard L, Petra D (2004) Development and validation of a global data-
base of lakes, reservoirs and wetlands. J Hydrol (Amst) 296:1–22.
36. Pullan RL, Freeman MC, Gething PW, Brooker SJ (2014) Geographical inequalities in
use of improved drinking water supply and sanitation across sub-Saharan Africa:
Mapping and spatial analysis of cross-sectional survey data. PLoS Med 11:e1001626.
37. Hijmans RJ (2015) raster: Geographic Data Analysis and Modeling. Available at https://
cran.r-project.org/web/packages/raster/index.html. Accessed November 16, 2016.
38. Funk C, et al. (2015) The climate hazards infrared precipitation with stations–A new
environmental record for monitoring extremes. Sci Data 2:150066.
39. Rodell M, et al. (2004) The Global Land Data Assimilation System. Bull Am Meteorol
40. Didan K (2015) MOD13C2 MODIS/Terra Vegetation Indices Monthly L3 Global
0.05Deg CMG V006, https://dx.doi.org/10.5067/MODIS/MOD13C2.006.
41. Smith TM, Reynolds RW, Peterson TC, Jay L (2008) Improvements to NOAA’s historical
merged land–ocean surface temperature analysis (1880–2006). J Clim 21:2283–2296.
42. Besag J, Julian B, Jeremy Y, Annie M (1991) Bayesian image restoration, with two
applications in spatial statistics. Ann Inst Stat Math 43:1–20.
43. Banerjee S, Carlin BP, Gelfand AE (2014) Hierarchical Modeling and Analysis for
Spatial Data (CRC Press, Boca Raton, FL), 2nd Ed.
44. Stern HS, Cressie N (2000) Posterior predictive model checks for disease mapping
models. Stat Med 19:2377–2397.
45. White G, Ghosh SK (2009) A stochastic neighborhood conditional autoregressive
model for spatial data. Comput Stat Data Anal 53:3033–3046.
46. Lee D (2011) A comparison of conditional autoregressive models used in Bayesian
disease mapping. Spat Spatio-Temporal Epidemiol 2:79–89.
47. Nadaraya EA (1964) On estimating regression. Theory Probab Appl 9:141–142.
48. Watson GS (1964) Smooth regression analysis. Sankhya Ser A 26:359–372.
49. Nychka D, Furrer R, Sain S (2015) fields: Tools for Spatial Data. Available at https://
cran.r-project.org/web/packages/fields/index.html. Accessed November 16, 2016.
50. Lehner B, Bernhard L, Günther G (2013) Global river hydrography and network
routing: Baseline data and new approaches to study the world’s large river systems.
Hydrol Processes 27:2171–2186.
www.pnas.org/cgi/doi/10.1073/pnas.1617218114 Moore et al.