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.

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Publication Details

Journal
Scientific Data
Published
2025-10-28
DOI
https://doi.org/10.1038/s41597-025-05996-5
Primary Topic
Malaria Research and Control
Type
article
Field-Weighted Citation Impact
0.00

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article

A malaria seasonality dataset for sub-Saharan Africa

Sarah Hafsia, Suzanne Keddie, Jailos Lubinda, Charles A. Whittaker et al.
Scientific Data
Malaria Research and Control
article

A malaria seasonality dataset for sub-Saharan Africa

Sarah Hafsia, Suzanne Keddie, Jailos Lubinda, Charles A. Whittaker, Sarah E. Connor, Annie J. Browne, Jennifer Rozier, Camilo Vargas, Ann Redpath, Francesca Sanna, Thomas Nguyen, Tasmin L. Symons, Paulina A. Dzianach, Adam Saddler, Huong Lan Thi Nguyen, Peter W. Gething, Tara F. Boyhan, Joseph Harris, Jean‐François Seitz, Daniel J. Weiss, Michael McPhail, Hunter Baggen, Rubi Jayaseelen, Twatasha Chikolwa
article en

Abstract

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.

Scientific DataVol. 12(1)
Nanyang Technological University (SG), The Kids Research Institute Australia (AU), King's College London (GB), Curtin University (AU), Berkeley Public Health Division (US), Imperial College London (GB), National Institute of Malaria Research (IN)
Bill and Melinda Gates Foundation, Australian Government, Medical Research Council, National Health and Medical Research Council
Openalex Percentile: Top 59%
Malaria Research and Control
DOIOpen Access PDFOpenAlex
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