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© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

Abstract

Remote sensing has been used for decades to produce vector-borne disease risk maps aiming at better targeting control interventions. However, the coarse and climatic-driven nature of these maps largely hampered their use in the fight against malaria in highly heterogeneous African cities. Remote sensing now offers a large panel of data with the potential to greatly improve and refine malaria risk maps at the intra-urban scale. This research aims at testing the ability of different geospatial datasets exclusively derived from satellite sensors to predict malaria risk in two sub-Saharan African cities: Kampala (Uganda) and Dar es Salaam (Tanzania). Using random forest models, we predicted intra-urban malaria risk based on environmental and socioeconomic predictors using climatic, land cover and land use variables among others. The combination of these factors derived from different remote sensors showed the highest predictive power, particularly models including climatic, land cover and land use predictors. However, the predictive power remained quite low, which is suspected to be due to urban malaria complexity and malaria data limitations. While huge improvements have been made over the last decades in terms of remote sensing data acquisition and processing, the quantity and quality of epidemiological data are not yet sufficient to take full advantage of these improvements.

Details

Title
The Multi-Satellite Environmental and Socioeconomic Predictors of Vector-Borne Diseases in African Cities: Malaria as an Example
Author
Morlighem, Camille 1   VIAFID ORCID Logo  ; Chaiban, Celia 1 ; Georganos, Stefanos 2 ; Brousse, Oscar 3   VIAFID ORCID Logo  ; Van de Walle, Jonas 4 ; Nicole P M van Lipzig 4 ; Wolff, Eléonore 5 ; Dujardin, Sébastien 1   VIAFID ORCID Logo  ; Linard, Catherine 6   VIAFID ORCID Logo 

 Department of Geography, University of Namur, 5000 Namur, Belgium; ILEE, University of Namur, 5000 Namur, Belgium 
 Division of Geoinformatics, KTH Royal Institute of Technology, 10044 Stockholm, Sweden; Department of Geoscience, Environment & Society, Université Libre de Bruxelles, 1050 Brussels, Belgium 
 Department of Earth and Environmental Sciences, KU Leuven, 3001 Leuven, Belgium; Institute of Environmental Design and Engineering, University College London, London WC1H 0NN, UK 
 Department of Earth and Environmental Sciences, KU Leuven, 3001 Leuven, Belgium 
 Department of Geoscience, Environment & Society, Université Libre de Bruxelles, 1050 Brussels, Belgium 
 Department of Geography, University of Namur, 5000 Namur, Belgium; ILEE, University of Namur, 5000 Namur, Belgium; NARILIS, University of Namur, 5000 Namur, Belgium 
First page
5381
Publication year
2022
Publication date
2022
Publisher
MDPI AG
e-ISSN
20724292
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2771659966
Copyright
© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.