Published June 26, 2023 | Version v2
Software Open

Mapping malaria risk in sub-Saharan African cities

Authors/Creators

  • 1. University of Namur
  • 2. Karlstad University
  • 3. University College London
  • 4. KU Leuven
  • 5. Université Libre de Bruxelles

Description

This repository contains data and material to model and predict malaria risk (measured as PfPR2-10) in four sub-Saharan African cities, Dakar (Senegal), Ouagadougou (Burkina Faso), Kampala (Uganda) and Dar es Salaam (Tanzania), using a set of environmental and socio-economic predictors derived from remote sensing imagery. These predictors are multi-resolution variables depicting the urban climate, the land use and the land cover. PfPR2-10 modelling and prediction are achieved using a popular machine learning algorithm, namely random forest (RF).

Please refer to the ReadMe file for instructions on how to use this code, or to the GitHub repository it is derived from. 

Files

REACT2cities.zip

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Additional details

Is derived from
https://github.com/CamilleMorlighem/REACT2cities (URL)
Is supplement to
Journal article: 10.3390/rs14215381 (DOI)
References
Journal article: 10.1038/nature24059 (DOI)
Dataset: 10.7910/DVN/Z29FR0 (DOI)
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Journal article: 10.1002/joc.6477 (DOI)
Dataset: 10.5281/zenodo.3711905 (DOI)
Dataset: 10.5281/zenodo.3711903 (DOI)
Dataset: 10.5281/zenodo.3871497 (DOI)
Dataset: 10.5281/zenodo.3238302 (DOI)
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Dataset: 10.5281/zenodo.1290800 (DOI)
Dataset: 10.5281/zenodo.1290654 (DOI)