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Mapping malaria risk in sub-Saharan African cities

Domain:

healthcaregeospatial

Record type:

datasetsoftware
Creator:
Camille MorlighemCelia ChaibanStefanos GeorganosOscar Brousse
Publisher:
Zenodo
Host:avatar

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 CamilleMorlighem/REACT2citi…. 

Visit

doi.org

Tags

urban malariasub-Saharan AfricaDHSremote sensingrandom forest

Licenses

info:eu-repo/semantics/openAccessMIT Licensehttps://opensource.org/licenses/MIT

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