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<p>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.</p>

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A malaria seasonality dataset for sub-Saharan Africa

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年10月28日

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  • Francesca Sanna,
  • Suzanne H. Keddie,
  • Tara Boyhan,
  • Paulina A. Dzianach,
  • Michael McPhail,
  • Julia Seitz,
  • Thomas Nguyen,
  • Adrian Redpath,
  • Twatasha Chikolwa,
  • Annie J. Browne,
  • Jailos Lubinda,
  • Adam Saddler,
  • Sarah Hafsia,
  • Rubi Jayaseelen,
  • Hunter S. Baggen,
  • Jennifer A. Rozier,
  • Tasmin L. Symons,
  • Joseph Harris,
  • Sarah Connor,
  • Camilo Vargas,
  • Charles Whittaker,
  • Michele Nguyen,
  • Peter W. Gething,
  • Daniel J. Weiss

摘要

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.