The seasonality of cholera in sub-Saharan Africa: a statistical modelling study

Background Cholera remains a major threat in Sub-Saharan Africa (SSA) where some of the highest case fatality risks are reported. Knowing in what months and where cholera tends to occur across the continent can aid in improving efforts to eliminate cholera as a public health concern; though largely due to lack of unified large-scale datasets, no continent-wide estimates exist. In this study we aim to estimate cholera seasonality across SSA. Methods We leverage the Global Task Force on Cholera Control (GTFCC) global cholera database with statistical models to synthesize data across spatial and temporal scale in order to infer the seasonality of excess suspected cholera occurrence in SSA. We developed a Bayesian statistical model to infer the monthly risk of excess cholera at the first and/or second administrative levels. Seasonality patterns were then grouped into spatial clusters. Finally, we studied the association between seasonality estimates and hydro-climatic variables. Findings The majority of studied countries (24/34) have seasonal patterns in excess cholera, corresponding to approximately 85% of the SSA population. Most countries (19/24) also had sub-national differences in seasonality patterns, with strong differences in seasonality strength between regions. Seasonality patterns clustered into two macro-regions (West Africa and the Sahel vs. Eastern and Southern Africa), which were composed of sub-regional clusters with varying degrees of seasonality. Exploratory association analysis found most consistent and positive correlations between cholera seasonality and precipitation, and to a lesser extent with temperature and flooding. Interpretation Widespread cholera seasonality in SSA offers opportunities for intervention planning. Further studies are needed to study the association between cholera and climate. Funding The NASA Applied Sciences Program and the Bill and Melinda Gates Foundation.