A malaria seasonality dataset for sub-Saharan Africa

Malaria imposes a significant global health burden and remains a major cause of child mortality in sub-Saharan Africa. In many countries, malaria transmission varies seasonally. The use of seasonally-deployed interventions is expanding, and the effectiveness of these control measures hinges on quantitative and geographically-specific characterisations of malaria seasonality. Malariometric timeseries from routine surveillance data and scientific and programmatic literature offer a resource for modelling patterns of malaria seasonality. This study creates and makes publicly available a geolocated dataset of historical timeseries describing malaria seasonality published since 2000 for sub-Saharan Africa. We used three approaches to assemble the dataset: i) an extensive literature review that included novel natural language processing to accelerate screening of published articles, ii) extractions from a routine surveillance dataset that contains geolocated data from all malaria-endemic countries, and iii) cross-referencing and incorporation of timeseries from a key entomological dataset. The resulting data include malaria prevalence, incidence, mortality, and entomological timeseries; and a novel assembly of qualitative descriptions of malaria seasonality extracted from published literature.