Regional-scale climate-variability synchrony of cholera epidemics in West Africa
© Constantin de Magny et al; licensee BioMed Central Ltd. 2007
Received: 20 October 2006
Accepted: 19 March 2007
Published: 19 March 2007
The relationship between cholera and climate was explored in Africa, the continent with the most reported cases, by analyzing monthly 20-year cholera time series for five coastal adjoining West African countries: Côte d'Ivoire, Ghana, Togo, Benin and Nigeria.
We used wavelet analyses and derived methods because these are useful mathematical tools to provide information on the evolution of the periodic component over time and allow quantification of non-stationary associations between time series.
The temporal variability of cholera incidence exhibits an interannual component, and a significant synchrony in cholera epidemics is highlighted at the end of the 1980's. This observed synchrony across countries, even if transient through time, is also coherent with both the local variability of rainfall and the global climate variability quantified by the Indian Oscillation Index.
Results of this study suggest that large and regional scale climate variability influence both the temporal dynamics and the spatial synchrony of cholera epidemics in human populations in the Gulf of Guinea, as has been described for two other tropical regions of the world, western South America and Bangladesh.
Epidemics of new and old infectious diseases periodically emerge and these emergences reveal the complex dynamical relationships among humans, pathogens and the environment [1, 2]. Connections between weather, climate and diseases are well established , with many diseases occurring during certain seasons or erupting from unseasonable flood or drought conditions . With new concerns about global warming, accompanied by greater climate variability, many recent studies have focused on disease fluctuations related to interannual climate oscillations (e.g., El Niño) (see [3, 5–8]). One of the major underlying questions of these recent studies is: Are climatic oscillations that occur at medium or low time frequency responsible for global patterns of recent reemergence of disease?
Evidence for influence of climate on cholera dynamics in Asia (Bangladesh) [5–7, 9, 10] and South America (Peru) [6, 11] has been published. Cholera, an ancient and devastating acute diarrheal illness caused by the ingestion of toxigenic Vibrio cholerae, occurs in widespread epidemics that remain a major public health problem in many developing countries, most often localized in the intertropical belt [12, 13]. Studies usually have focused on the influence of climate on cholera dynamics across regions of cholera endemicity, mainly because they can provide environmental or climatic factors that promote epidemics through analysis of long-term historical records [5–7, 14, 15]. In these regions, cholera dynamics display regular seasonal cycles and pronounced interannual variability. In Bangladesh, as in Peru, nonstationary links have been shown with climate interannual variability (e.g., the El Niño event that occurs every 3–7 years) [5, 7, 16–18].
The question of what interannual climate variability triggers the disease dynamics patterns in non-endemic cholera regions in Africa, however, remains unanswered. Analyses of long-term climatic and epidemiological data allow exploration of this issue at both the local (e.g., country) and regional scale (e.g., contiguous coastal countries). Africa appears to be the continent most affected by the disease, with more than 95,000 cases reported in 2004 .
The new findings provided by this study are useful for the development of an effective early warning system that is based on climate data over an extended intertropical littoral zone. In the future, it will be possible to integrate realtime monitoring of oceanic regions, climate variability and epidemiological and demographic population dynamics to predict cholera outbreaks.
Cholera and climatic data
Summary of the cholera cases dataset.
Total number of cases (1975–2002)
Total number of reports
Number of reports interpolated (%)
Number of report lengths > 2 months (%)
Rainfall data were extracted from an historical monthly precipitation data set, 1975 to 1996, available on the Climate Research unit website of the University of East Anglia at Norwich (UK) . Five zones were selected as the most representative of human community settlements, with most of the given country population concentrated near the coastline (see Figure 1). For three of the five countries, we used a mean between rainfall time series because these countries were related to two or more rainfall time series. We computed the mean for (i) Ghana between Ghan132 and Ben270, (ii) Benin between Ben270 and Ben201, and (iii) Nigeria between Ben201 and Nig037 rainfall time series.
The Indian Oscillation Index (IOI)  is based on the variability in sealevel atmospheric pressure (SLP) between Mahe in the Seychelles (4°S, 55°E) in the West Indian Ocean, and Darwin (10°S, 130°E) in the East Indian Ocean. It is realised by computing the differences between the monthly standardized anomalies of SLP at both sites (Mahe minus Darwin), from 1975 to 2002. IOI warm events (increase in the sea surface temperature and strengthening of easterly winds at the equator) are associated with IOI values less than -1. In contrast, values greater than +1 indicate cold events .
Wavelet Analysis: Pattern characterization of Cholera Epidemics and Climate Variability
Wavelet analysis [21, 22], in contrast to Fourier analysis, is useful for biological time series analyses, mainly because of the non-stationarity (i.e., the oftenobserved changes in the periodic behaviour) of such series. Wavelet analysis allows detection of periodicity, as well as local variation with time, indicating temporal evolution of the periodic components . Over the past five years, wavelet analysis has increasingly been used in ecology [30, 31] and epidemiology [8, 21, 32, 33] to explore spatial and temporal dynamics of disease.
In this study, we used wavelet analysis to determine the significant oscillating modes of disease time series based on wavelet decomposition and wavelet power spectra , and we used the phase angles of the disease time series analyses to characterize the pattern of epidemic synchrony [8, 21, 24, 33]. The pattern of disease outbreak synchrony was tested and based on the comparison between the observed distribution of the phase difference and that obtained by bootstrapping. Wavelet coherency analyses identified and quantified possible statistical associations between the two time series, e.g., between the disease time series and climatic indices. Coherency is roughly similar to a classical correlation, but it is relevant to oscillating components in a given frequency mode for a given time period [8, 33]. Statistical analyses were performed using Matlab (version 6.5, The MathWorks, Naticks, Massachusetts, United States).
Frequency, Synchronicity of Cholera Epidemics, and Climate Variability
Results of the test of synchrony for incidence time series and rainfall between 1989 and 1994.
Entropy = 0.7043
p-value < 0.0001
Entropy = 0.7407
p-value = 0.0004
Entropy = 0.7542
p-value = 0.0360
Entropy = 0.7276
p-value < 0.0001
Entropy = 0.7091
p-value = 0.0041
Entropy = 0.6327
p-value = 0.0669
Association Between Cholera Incidence and Climate Variability
Cholera interannual periodicity and the link between cholera dynamics and climate variability remain incompletely understood and generally focused only on endemic regions [7, 9, 14, 15]. Pascual et al.  and Rodo et al.  described a role of El Niño/Southern Oscillation in the dynamics of cholera in Bangladesh. In addition, the complex relationship between largescale climatic variability and spatiotemporal patterns under local environmental conditions and weather contributes to the dynamics of local pathogen populations in aquatic ecosystems , and/or disease transmission [35, 36]. In this context, using a comparative approach developed for macroecology applications , the relationship between cholera incidence in five different African countries and climate interannual variability was explored. Indeed, analyses of long-term monthly disease time series underline both the complex, nonstationary dynamics of cholera epidemics in West Africa, and a relationship with large-scale climate variability.
From 1989 to 1994, (i) four of five cholera dynamics (i.e., Benin, Ghana, Nigeria, and Togo), rainfall, and IOI displayed a significant 2- to 3-yr periodicity, (ii) cholera incidence time series, as well as rainfall time series, were highly synchronous across the five African countries, and (iii) the same four of five incidence time series, rainfall and IOI were significantly coherent in the 2- to 3-yr periodic band. The 2- to 3-yr periodicity detected in this study was in harmony with results obtained in Asia and in South America [5, 7, 14]. This remarkable observed synchrony between incidences in the Gulf of Guinea perfectly matches the spatio-temporal synchrony of rainfall, supporting a link between cholera epidemics and climatic variability. Indeed, the influence of rainfall increase on cholera incidence can best be explained by flood waters on disease transmission . Furthermore, the coherency between disease incidence, rainfall and climate variability in the Indian Ocean describes a direct or indirect link, as reported by Rodo et al. for Bangladesh.
On the other hand, the Côte d'Ivoire showed no periodicity between 1987 and 1994, and a lack of coherency between incidence and two climatic variables (rainfall and IOI). This could be explained by interaction between the two main drivers of the disease, namely, extrinsic factors such as variability in climate or health policy, and intrinsic factors, such as the patterns of immunity in human population. Koelle et al.  explored interannual cholera cycles in Bangladesh and highlighted the critical interplay of environmental forcing and temporary immunity. Even if local environmental conditions such as rainfall or ambient temperature, influenced by global climate variability, initiate an outbreak, an observed coherency between IOI and rainfall in Côte d'Ivoire, the refractory period of the disease dynamic, when the population of susceptibles is low, can prevent outbreaks. Furthermore, sanitary conditions and access to health care centers reduce the susceptible population size and transmission probability, thus resulting in a decline in the sensitivity of cholera dynamics to climate variability.
In fact, the question raised from the results of this study concerns the dynamics of cholera: Is synchronization of cholera a response of local populations, according to ecological theory, to climate interannual variability? The presence of global synchrony among the countries for cholera, a disease highly susceptible to climatic factors, supports the hypothesis of a common external forcing, namely climatic factors, explaining synchronicity, and supporting a key prediction of the Moran theorem , a phenomenon described in population biology [38–41] and in epidemiology [8, 42]. The hypothesis consists of an intricate, hierarchical mechanism with climatic variability at a large scale quantified by IOI at the origin of the synchronization of both the cholera incidence and rainfall over all of the West African countries included in this study. Indeed, Janicot (1997) described that empirical studies have shown that warm El Niño/Southern Oscillation (ENSO) episodes are associated with the intertropical convergence zone (ITCZ) over the tropical Atlantic which is related to the rainfall in West Africa. It is also likely that many other factors, e.g., the level of poverty and human population density, influence the spatial and temporal distribution of cholera [13, 36, 43], but perhaps now, cholera dynamics are more strongly associated with climate [9, 14, 44, 45]. 
In conclusion, besides the two inter-tropical regions of the world, Asia and South America, global climate change may well impact cholera diseases in many other parts of the inter-tropical zone [5, 7, 8, 14, 15]. The study reported here is an important step toward a long-term study of the spatial and temporal dynamics of cholera in Africa at the regional level. The main perspective of this work should be to focus on a local scale, as has been done in Bangladesh, using collected data from diarrhoeal surveillance programs in selected African areas. The benefit of the precision of this type of study will open a new field of research and allow a reliable model for cholera predictions [14, 15, 45, 47, 48]. Development in the near future of a concrete and useful plan of action for health policy should be based coupling both realistic epidemiological models, including intrinsic factors such as level of immunity or cross immunity of the population, and environmental parameters monitored by remote sensing, such as sea surface temperature, sea surface height or land surface temperature [10, 15, 49].
The work was supported by a "Gestion et Impacts du Changement Climatique" grant (CHOLCLIM) from the French Ministry of Ecology and Sustainable Resource (FMESR). GCdM thanks the French authorities and the Centre National d'Etudes Spatiales (CNES) for PhD fellowships. BC is supported by the Centre National de la Recherche Scientifique (CNRS). J-FG thanks both the Institut de Recherche pour le Développement (IRD) and the CNRS. We gratefully acknowledge Dr Arthur Delcher of the Center for Bioinformatics and Computational Biology, University of Maryland, for his useful comments on manuscript.
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