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El Niño and the shifting geography of cholera in Africa


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Significance 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.
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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
Alexandra Hill
, 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,
GA 30329;
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 Africaand away
from Madagascar and portions of southern, Central, and West
Africawhere 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 Niños
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
disease mapping
climate and health
Bayesian mapping
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). Choleras 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 115%) (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 27yand
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 20152016 El Niño event is
only the third of the past 40 y to be classified as strongor
very-strong(joining 19821983 and 19971998; 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
years 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 20152016 El Niño, multiple cholera epidemics
occurred in East Africa, including the largest outbreak since the
19971998 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 612 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:
This article contains supporting information online at
1073/pnas.1617218114/-/DCSupplemental. 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
20002014 [215,546 (95% credible interval [CrI]: 209,770
221,704) vs. 209,791 (95% CrI: 202,087219,047)], 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: 29,38533,775), 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: 45,19252,053) 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: 166.0189.5 million), and 81 million live in
areas where annual incidence increased by more than 1 per 10,000
(95% CrI: 75.886.4 million). Likewise, 137 million live in areas
where annual incidence declined by at least 1 per 100,000 (95%
CrI: 125.6149.4 million) and 69 million live in areas where
annual incidence decreased by more than 1 per 10,000 (95%
CrI: 63.274.2 million).
1982 1986 1990 1994 1998 2002 2006 2010 2014
−2 −1 0 12
3−month ENSO Index
deviation (%)
log incidence
Fig. 1. Geographical distribution of cholera in El Niño and non-El Niño years. (A) Rainfall anomalies in El Niño years, 19802015. (B) Fine-scale geographic distribution
of cholera anomalies in El Niño years, 20002014. (C) Country-level cholera anomalies in El Niño years, based on WHO reports, 19802015. 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 ACrepresent 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).
anda (
epublic of
east and southeast
(5) Zambia
anzania (
Negatively Sensitive Insensitive Positively Sensitive
ENSO Sensitivity Clusters
Cases per 10,000
El Niño
non El Niño
CHoldout analysis
cluster averages
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
201516 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).
| 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: 2.523.19], 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: 0.480.57), 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: 9,40349,049) 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: 2.57.8) or above average (RR:
2.4 in basin with rainfall anomalies in the upper quartile; P=
0.003; 95% CrI: 1.44.2) 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. S22S26). 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: 1.49.0; 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: 1.613.5), West Africa (RR: 6.8; 95% CrI: 2.3
20.3), and Central Africa (RR: 18.7; 95% CrI: 3.794.4) 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 20152016 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: 31,04338,018) 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 (2527).
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 Niños 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
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 20152016 El Niño event,
assumed to be April 1, 2015 (SI Appendix,TableS3). First, we performed a
query on (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 UNICEFsWestAfricaregionalofficeasofweek
6, 2016 (33).
Difference in
log incidence
Central Africa East Africa North Africa Souther n Africa West Africa
Difference in log cholera incidence
deviation (%)
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
| Moore et al.
Covariates and Climate Variables. Gridded population density at a 1km
resolution for the entire African continent were obtained from WorldPop
(; 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 rasterpackage 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°N5°S, 120°W170°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 19971998 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 19971998 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 20022003 and 20092010 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 NjXNjadjacency 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
ψjN0, 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:
where p
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
extractfunction 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
parameters β=(β
) 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 S2S5.
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 20062007 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 fieldsR 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:
where, C
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
data and are either directly available or available by request of the owning
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| Moore et al.
... This reduction was attributed to flooding, which can flush mosquito larvae from breeding sites, thereby decreasing the disease spread 151 . Two out of the three communities in Tanzania that reported an increase in malaria after the heavy rains were located next to a body of standing water that is an ideal breeding ground for mosquitoes 151 .Water-borne diseases such as cholera and typhoid also become more of a concern during specific shifts in rainfall and variations in temperature 143,152,153 . ...
... Future changes in rainfall can also be expected to influence human health and disease. There is a threshold of relative humidity (and temperature) that limits the transmission of malaria and arboviruses via their influence on the associated vectors (for example, mosquitoes) and pathogens 152,192,193 . Increases in relative humidity associated with more extreme wet seasons in the future can shorten the incubation and blood-feeding stages 193 of the mosquito lifecycle, but the net impact of these changes is unclear. ...
... Increased future levels of rainfall and its variability might lead to more frequent and persistent flooding that will help establish more breeding sites for insects, although some vectors breed indoors and will not be directly affected by flooding. The relationship between flooding and waterborne diseases such as cholera and typhoid differs by region 152,192 . However, one of the biggest risks for future transmission of malaria and arboviruses in Eastern Africa is drug and insecticide resistance combined with warmer temperatures and lower relative humidity associated with climate change in the highland regions, where there is little immunity and insufficient health infrastructure [194][195][196][197] . ...
Full-text available
Eastern Africa exhibits bimodal rainfall consisting of long rains (March–May) and short rains (October–December), changes in which have profound socioeconomic and environmental impacts. In this Review, we examine the drivers and corresponding impacts of Eastern African rainfall variability. Remote teleconnections, namely the El Niño–Southern Oscillation and the Indian Ocean Dipole, exert a dominant influence on interannual variability. From the mid-1980s to 2010, the long rains have tended toward a drier state (trends of −0.65 to −2.95 mm season⁻¹year⁻¹), with some recovery thereafter, while the short rains have become wetter since the mid 1980s (1.44 to 2.36 mm season⁻¹ year⁻¹). These trends, overlain by substantial year-to-year variations, affect the severity and frequency of extreme flooding and droughts, the stability of food and energy systems, the susceptibility to water-borne and vector-borne diseases, and ecosystem stability. Climate model projections of rainfall changes differ, but there is some consensus that the short rains will deliver more rainfall than the long rains by 2030–2040, with implications for sustaining agricultural yields and triggering climate-related public health emergencies. Mitigating the impacts of future Eastern African climate requires continued investments in agriculture, clean water, medical and emergency infrastructures, and development and adoption of adaptation strategies, as well as targeted early-warning systems driven by improved meteorological observations.
... Additionally, there was another peak of cholera cases in 2022. The large number of cases observed in Kenya in 2015 was attributed to prolonged rainfall and subsequent flooding, which led to contamination of water sources and rapid spread of cholera (Moore et al., 2017). Conversely, the outbreak in 2022 was triggered by the impact of prolonged and severe drought, which resulted in water scarcity. ...
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Cholera outbreaks remain an important public health challenge in Kenya, especially in areas with poor sanitation and hygiene including urban informal settlements, refugee camps and rural areas bordering large water bodies. Even in endemic settings, the distribution of cases exhibits spatial and temporal variations. Utilizing a Poisson discrete space-time scan statistic (SaTScan), this study investigated the temporal trends and the nature of spatial spread of cholera within selected high-risk areas in Kenya. The study was conducted in an urban informal settlement in Nairobi (Mukuru), a refugee camp in Northern Kenya (Dadaab) and the four counties bordering Lake Victoria region. Retrospective cholera line list data from January 2012 to December 2022 in the selected high-risk areas was used. SaTScan v10.1.2 was used to carry out Spatiotemporal analysis and generate spatial clusters. Throughout the study period, a total of 7,372 cholera cases were reported, corresponding to a lower annual incidence rate of 12.2 per 100,000 people compared to a mean annual incidence of 25 cases per 100,000 population previously reported in Kenya. The highest number of cases (n=5934) were reported between 2015 and 2018 with an annual incidence rate of 27.0 per 100,000 people, indicating a relative risk (RR) of 7.22 and a log-likelihood ratio (LLR) of 3015.17 (p< 0.001). The risky clusters (RR>1) were in Dadaab, Fafi, Suna West, Nyatike, Ugunja, Ndhiwa and Suna East sub counties with annual cases of 111.6, 164.1, 28.4, 19.9, 19.4, 14.0 and 13.9 per 100,000, respectively. The sub-counties of Nyakach, Nyando, Rachuonyo East, Kisumu East and Kisumu Central were reported as low-risk clusters, with a relative risk of 0.055 and an annual incidence rate of 1.1 cases per 100,000 individuals. Out of the thirty-two sub-counties included in the study, ten of them did not report any cases of cholera during the study period. Cholera cases waxed every three years in the selected high-risk areas. This data on hotspots specific to endemic settings forms a basis for prompt public health response and resource allocation by prioritizing the significantly high-risk clusters to control and eventually eliminate the disease.
... Findings may apply to human diseases, including human cholera and mosquito-borne diseases such as malaria and dengue (50). Climaterelated factors -such as El Niño -induce ocean warming, which promotes long-distance dissemination of infectious agents (51)(52)(53). While the approach here explored is not necessarily applicable to epidemics of low morbidity, such as Ebola (54), it may apply in rapidly disseminating infectious diseases (46). ...
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Introduction: Physical and non-physical processes that occur in nature may influence biological processes, such as dissemination of infectious diseases. However, such processes may be hard to detect when they are complex systems. Because complexity is a dynamic and non-linear interaction among numerous elements and structural levels in which specific effects are not necessarily linked to any one specific element, cause-effect connections are rarely or poorly observed. Methods: To test this hypothesis, the complex and dynamic properties of geo-biological data were explored with high-resolution epidemiological data collected in the 2001 Uruguayan foot-and-mouth disease (FMD) epizootic that mainly affected cattle. County-level data on cases, farm density, road density, river density, and the ratio of road (or river) length/county perimeter were analyzed with an open-ended procedure that identified geographical clustering in the first 11 epidemic weeks. Two questions were asked: (i) do geo-referenced epidemiologic data display complex properties? and (ii) can such properties facilitate or prevent disease dissemination? Results: Emergent patterns were detected when complex data structures were analyzed, which were not observed when variables were assessed individually. Complex properties-including data circularity-were demonstrated. The emergent patterns helped identify 11 counties as 'disseminators' or 'facilitators' (F) and 264 counties as 'barriers' (B) of epidemic spread. In the early epidemic phase, F and B counties differed in terms of road density and FMD case density. Focusing on non-biological, geographical data, a second analysis indicated that complex relationships may identify B-like counties even before epidemics occur. Discussion: Geographical barriers and/or promoters of disease dispersal may precede the introduction of emerging pathogens. If corroborated, the analysis of geo-referenced complexity may support anticipatory epidemiological policies.
... To date, various climate models have been employed to evaluate the different effects of climate change on infectious diseases (Moore et al. 2017;Wu et al. 2018;Parham and Michael 2010). One of these climate models is the general circulation models (GCMs). ...
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Climate change can increase the spread of infectious diseases and public health concerns. Malaria is one of the endemic infectious diseases of Iran, whose transmission is strongly influenced by climatic conditions. The effect of climate change on malaria in the southeastern Iran from 2021 to 2050 was simulated by using artificial neural networks (ANNs). Gamma test (GT) and general circulation models (GCMs) were used to determine the best delay time and to generate the future climate model under two distinct scenarios (RCP2.6 and RCP8.5). To simulate the various impacts of climate change on malaria infection, ANNs were applied using daily collected data for 12 years (from 2003 to 2014). The future climate of the study area will be hotter by 2050. The simulation of malaria cases elucidated that there is an intense increasing trend in malaria cases under the RCP8.5 scenario until 2050, with the highest number of infections occurring in the warmer months. Rainfall and maximum temperature were identified as the most influential input variables. Optimum temperatures and increased rainfall provide a suitable environment for the transmission of parasites and cause an intense increase in the number of infection cases with a delay of approximately 90 days. ANNs were introduced as a practical tool for simulating the impact of climate change on the prevalence, geographic distribution, and biological activity of malaria and for estimating the future trend of the disease in order to adopt protective measures in endemic areas. Graphical abstract
... The incidence rate of cholera in Bengal over the last century has shown a definite, though weak correlation with ENSO cycles between 1893 to 1920, but a very strong correlation from 1980 to 2001 [8]. Recent publications have reported that in Africa, during El Niño years, cholera incidence shifts to the eastern part of the continent, and in the United States, the incidence of enteric diseases increases markedly in the Northeastern region which is less tele-connected to El Niño than the western parts [9,10]. ...
Full-text available
Objective: To explore the relationship between climate variables and enteric fever in the city of Ahmedabad and report preliminary findings regarding the influence of El Niño Southern Oscillations and Indian Ocean Dipole over enteric fever incidence. Method: A total of 29 808 Widal positive enteric fever cases reported by the Ahmedabad Municipal Corporation and local climate data in 1985-2017 from Ahmedabad Meteorology Department were analysed. El Niño, La Niña, neutral and Indian Ocean Dipole years as reported by the National Oceanic and Atmospheric Administration for the same period were compared for the incidence of enteric fever. Results: Population-normalized average monthly enteric fever case rates were the highest for El Niño years (25.5), lower for La Niña years (20.5) and lowest for neutral years (17.6). A repeated measures ANOVA analysis showed no significant difference in case rates during the three yearly El Niño Southern Oscillations categories. However, visual profile plot of estimated marginal monthly means showed two distinct characteristics: an early rise and peaking of cases in the El Niño and La Niña years, and a much more restrained rise without conspicuous peaks in neutral years. Further analysis based on monthly El Niño Southern Oscillations categories was conducted to detect differences in median monthly case rates. Median case rates in strong and moderate El Niño months and strong La Niña months were significantly dissimilar from that during neutral months (P
... O El Niño de 2015-2016 foi considerado um dos três eventos mais fortes já registrados, comparável aos de 1982de -1983de e 1997de -1998de (REN et al., 2017WMO, 2016). Além disso, ele ainda foi classificado como um evento de extrema intensidade (MOORE et al., 2017). ...
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Climate change represents an unprecedented threat to humanity and will be the ultimate challenge of the 21st century. As a public health consequence, the World Health Organization estimates an additional 250,000 deaths annually by 2030, with resource-poor countries being predominantly affected. Although climate change’s direct and indirect consequences on human health are manifold and far from fully explored, a growing body of evidence demonstrates its potential to exacerbate the frequency and spread of transmissible infectious diseases. Effective, high-impact mitigation measures are critical in combating this global crisis. While vaccines and vaccination are among the most cost-effective public health interventions, they have yet to be established as a major strategy in climate change-related health effect mitigation. In this narrative review, we synthesize the available evidence on the effect of climate change on vaccine-preventable diseases. This review examines the direct effect of climate change on water-related diseases such as cholera and other enteropathogens, helminthic infections and leptospirosis. It also explores the effects of rising temperatures on vector-borne diseases like dengue, chikungunya, and malaria, as well as the impact of temperature and humidity on airborne diseases like influenza and respiratory syncytial virus infection. Recent advances in global vaccine development facilitate the use of vaccines and vaccination as a mitigation strategy in the agenda against climate change consequences. A focused evaluation of vaccine research and development, funding, and distribution related to climate change is required.
One of the greatest challenges to mankind has been the spread of infectious diseases that emerge or re-emerge at the interfaces of animals, humans, and the ecosystems in which they live. There are several contributing factors for this situation such as the exponential growth in human and livestock populations, rapid urbanisation, change in farming systems, close interaction between livestock and wildlife, encroachment in forest, ecosystems changes, and globalisation of trade in animals and animal product.The exponential increase in the human population is not only putting pressure on land use, but also led to encroachment of natural forests and their rich and diverse fauna, thus, exposing mans and domestic animals to novel pathogens.To minimise the global impact of zoonoses, especially those with pandemic potential, One Health concept evolved, which has been defined as ‘the collaborative effort of multiple disciplines—working locally, nationally, and globally—to attain optimal health for people, animals and the environment’.This will augment healthcare and public health efficiency; improve medical education and clinical care. Millions of lives will be protected. It is required that One Health stakeholders are connected, strategic networks /partnerships are created. Individuals and groups from around the world work together in the form of an organised team. The concept is accepted and implemented in one or the other forms in some advanced nations, others follow.
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Predicting effects of climate change on plant disease is critical for protecting ecosystems and food production. Climate change could exacerbate plant disease because parasites may be quicker to acclimate and adapt to novel climatic conditions than their hosts due to their smaller body sizes and faster generation times. Here we show how disease pressure responds to the anomalous weather that will increasingly occur with climate change by compiling a global database (5380 plant populations; 437 unique plant-disease combinations; 2,858,795 individual plant-disease samples) of disease incidence in both agricultural and wild plant systems. Because wild plant populations are assumed to be adapted to local climates, we hypothesized that large deviations from historical conditions would increase disease incidence. By contrast, since agricultural plants have been transported globally, we did not expect the historical climate where they are currently grown to be as predictive of disease incidence. Supporting these hypotheses, we found that disease outbreaks tended to occur during periods of warm temperatures in agricultural and cool-climate wild plant systems, but also occurred in warm-adapted wild (but not agricultural) plant systems experiencing anomalously cool weather. Outbreaks were additionally associated with higher rainfall in wild systems, especially those with historically wet climates. Our results suggest that historical climate affects susceptibility to disease for wild plant-disease systems, while warming drives risks for agricultural plant disease outbreaks regardless of historical climate.
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Climate teleconnections (CT) remotely influence weather conditions in many regions on Earth, entailing changes in primary drivers of fire activity such as vegetation biomass accumulation and moisture. We reveal significant relationships between the main global CTs and burned area that vary across and within continents and biomes according to both synchronous and lagged signals, and marked regional patterns. Overall, CTs modulate 52.9% of global burned area, the Tropical North Atlantic mode being the most relevant CT. Here, we summarized the CT-fire relationships into a set of six global CT domains that are discussed by continent, considering the underlying mechanisms relating weather patterns and vegetation types with burned area across the different world’s biomes. Our findings highlight the regional CT-fire relationships worldwide, aiming to further support fire management and policy-making.
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The Climate Hazards group Infrared Precipitation with Stations (CHIRPS) dataset builds on previous approaches to ‘smart’ interpolation techniques and high resolution, long period of record precipitation estimates based on infrared Cold Cloud Duration (CCD) observations. The algorithm i) is built around a 0.05° climatology that incorporates satellite information to represent sparsely gauged locations, ii) incorporates daily, pentadal, and monthly 1981-present 0.05° CCD-based precipitation estimates, iii) blends station data to produce a preliminary information product with a latency of about 2 days and a final product with an average latency of about 3 weeks, and iv) uses a novel blending procedure incorporating the spatial correlation structure of CCD-estimates to assign interpolation weights. We present the CHIRPS algorithm, global and regional validation results, and show how CHIRPS can be used to quantify the hydrologic impacts of decreasing precipitation and rising air temperatures in the Greater Horn of Africa. Using the Variable Infiltration Capacity model, we show that CHIRPS can support effective hydrologic forecasts and trend analyses in southeastern Ethiopia.
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The recent decline in Horn of Africa rainfall during the March-May "long rains" season has fomented drought and famine, threatening food security in an already vulnerable region. Some attribute this decline to anthropogenic forcing, whereas others maintain that it is a feature of internal climate variability. We show that the rate of drying in the Horn of Africa during the 20th century is unusual in the context of the last 2000 years, is synchronous with recent global and regional warming, and therefore may have an anthropogenic component. In contrast to 20th century drying, climate models predict that the Horn of Africa will become wetter as global temperatures rise. The projected increase in rainfall mainly occurs during the September-November "short rains" season, in response to large-scale weakening of the Walker circulation. Most of the models overestimate short rains precipitation while underestimating long rains precipitation, causing the Walker circulation response to unrealistically dominate the annual mean. Our results highlight the need for accurate simulation of the seasonal cycle and an improved understanding of the dynamics of the long rains season to predict future rainfall in the Horn of Africa.
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Extraordinarily strong El Niño events, such as those of 1982/1983 and 1997/1998, cause havoc with weather around the world, adversely influence terrestrial and marine ecosystems in a number of regions and have major socio-economic impacts. Here we show by means of climate model integrations that El Niño events may be boosted by global warming. An important factor causing El Niño intensification is warming of the western Pacific warm pool, which strongly enhances surface zonal wind sensitivity to eastern equatorial Pacific sea surface temperature anomalies. This in conjunction with larger and more zonally asymmetric equatorial Pacific upper ocean heat content supports stronger and longer lasting El Niños. The most intense events, termed Super El Niños, drive extraordinary global teleconnections which are associated with exceptional surface air temperature and rainfall anomalies over many land areas.
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The global burden of cholera is largely unknown because the majority of cases are not reported. The low reporting can be attributed to limited capacity of epidemiological surveillance and laboratories, as well as social, political, and economic disincentives for reporting. We previously estimated 2.8 million cases and 91,000 deaths annually due to cholera in 51 endemic countries. A major limitation in our previous estimate was that the endemic and non-endemic countries were defined based on the countries' reported cholera cases. We overcame the limitation with the use of a spatial modelling technique in defining endemic countries, and accordingly updated the estimates of the global burden of cholera. Countries were classified as cholera endemic, cholera non-endemic, or cholera-free based on whether a spatial regression model predicted an incidence rate over a certain threshold in at least three of five years (2008-2012). The at-risk populations were calculated for each country based on the percent of the country without sustainable access to improved sanitation facilities. Incidence rates from population-based published studies were used to calculate the estimated annual number of cases in endemic countries. The number of annual cholera deaths was calculated using inverse variance-weighted average case-fatality rate (CFRs) from literature-based CFR estimates. We found that approximately 1.3 billion people are at risk for cholera in endemic countries. An estimated 2.86 million cholera cases (uncertainty range: 1.3m-4.0m) occur annually in endemic countries. Among these cases, there are an estimated 95,000 deaths (uncertainty range: 21,000-143,000). The global burden of cholera remains high. Sub-Saharan Africa accounts for the majority of this burden. Our findings can inform programmatic decision-making for cholera control.
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Cholera is generally regarded as the prototypical waterborne and environmental disease. In Africa, available studies are scarce, and the relevance of this disease paradigm is questionable. Cholera outbreaks have been repeatedly reported far from the coasts: from 2009 through 2011, three-quarters of all cholera cases in Africa occurred in inland regions. Such outbreaks are either influenced by rainfall and subsequent floods or by drought- and water-induced stress. Their concurrence with global climatic events has also been observed. In lakes and rivers, aquatic reservoirs of Vibrio cholerae have been evocated. However, the role of these reservoirs in cholera epidemiology has not been established. Starting from inland cholera-endemic areas, epidemics burst and spread to various environments, including crowded slums and refugee camps. Human displacements constitute a major determinant of this spread. Further studies are urgently needed to better understand these complex dynamics, improve water and sanitation efforts, and eliminate cholera from Africa.
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During the current seventh cholera pandemic, Africa bore the major brunt of global disease burden. More than 40 years after its resurgence in Africa in 1970, cholera remains a grave public health problem, characterized by large disease burden, frequent outbreaks, persistent endemicity, and high CFRs, particularly in the region of the central African Great Lakes which might act as reservoirs for cholera. There, cases occur year round with a rise in incidence during the rainy season. Elsewhere in sub-Saharan Africa, cholera occurs mostly in outbreaks of varying size with a constant threat of widespread epidemics. Between 1970 and 2011, African countries reported 3,221,050 suspected cholera cases to the World Health Organization, representing 46 % of all cases reported globally. Excluding the Haitian epidemic, sub-Saharan Africa accounted for 86 % of reported cases and 99 % of deaths worldwide in 2011. The number of cholera cases is possibly much higher than what is reported to the WHO due to the variation in modalities, completeness, and case definition of national cholera data. One source on country specific incidence rates for Africa, adjusting for underreporting, estimates 1,341,080 cases and 160,930 deaths (52.6 % of 2,548,227 estimated cases and 79.6 % of 209,216 estimated deaths worldwide). Another estimates 1,411,453 cases and 53,632 deaths per year, respectively (50 % of 2,836,669 estimated cases and 58.6 % of 91,490 estimated deaths worldwide). Within Africa, half of all cases between 1970 and 2011 were notified from only seven countries: Angola, Democratic Republic of the Congo, Mozambique, Nigeria, Somalia, Tanzania, and South Africa. In contrast to a global trend of decreasing case fatality ratios (CFRs), CFRs have remained stable in Africa at approximately 2 %. Early propagation of cholera outbreaks depends largely on the extent of individual bacterial shedding, host and organism characteristics, the likelihood of people coming into contact with an infectious dose of Vibrio cholerae and on the virulence of the implicated strain. Cholera transmission can then be amplified by several factors including contamination of human water- or food sources; climate and extreme weather events; political and economic crises; high population density combined with poor quality informal housing and poor hygiene practices; spread beyond a local community through human travel and animals, e.g., water birds. At an individual level, cholera risk may increase with decreasing immunity and hypochlorhydria, such as that induced by Helicobacter pylori infection, which is endemic in much of Africa, and may increase individual susceptibility and cholera incidence. Since contaminated water is the main vehicle for the spread of cholera, the obvious long-term solution to eradicate the disease is the provision of safe water to all African populations. This requires considerable human and financial resources and time. In the short and medium term, vaccination may help to prevent and control the spread of cholera outbreaks. Regardless of the intervention, further understanding of cholera biology and epidemiology is essential to identify populations and areas at increased risk and thus ensure the most efficient use of scarce resources for the prevention and control of cholera.