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The Multi-Satellite Environmental and Socioeconomic Predictors of Vector-Borne Diseases in African Cities : Malaria as an Example

Morlighem, Camille (author)
Univ Namur, Dept Geog, B-5000 Namur, Belgium.;Univ Namur, ILEE, B-5000 Namur, Belgium.
Chaiban, Celia (author)
Univ Namur, Dept Geog, B-5000 Namur, Belgium.;Univ Namur, ILEE, B-5000 Namur, Belgium.
Georganos, Stefanos (author)
KTH,Geoinformatik,Univ Libre Bruxelles, Dept Geosci Environm & Soc, B-1050 Brussels, Belgium.
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Brousse, Oscar (author)
Katholieke Univ Leuven, Dept Earth & Environm Sci, B-3001 Leuven, Belgium.;UCL, Inst Environm Design & Engn, London W H 0NN, England.
Van de Walle, Jonas (author)
Katholieke Univ Leuven, Dept Earth & Environm Sci, B-3001 Leuven, Belgium.
van Lipzig, Nicole P. M. (author)
Katholieke Univ Leuven, Dept Earth & Environm Sci, B-3001 Leuven, Belgium.
Wolff, Eleonore (author)
Univ Libre Bruxelles, Dept Geosci Environm & Soc, B-1050 Brussels, Belgium.
Dujardin, Sebastien (author)
Univ Namur, Dept Geog, B-5000 Namur, Belgium.;Univ Namur, ILEE, B-5000 Namur, Belgium.
Linard, Catherine (author)
Univ Namur, Dept Geog, B-5000 Namur, Belgium.;Univ Namur, ILEE, B-5000 Namur, Belgium.;Univ Namur, NARILIS, B-5000 Namur, Belgium.
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Univ Namur, Dept Geog, B-5000 Namur, Belgium;Univ Namur, ILEE, B-5000 Namur, Belgium. Geoinformatik (creator_code:org_t)
2022-10-27
2022
English.
In: Remote Sensing. - : MDPI AG. - 2072-4292. ; 14:21
Related links:
https://doi.org/10.3...
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https://urn.kb.se/re...
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  • Journal article (peer-reviewed)
Abstract Subject headings
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  • 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.

Subject headings

NATURVETENSKAP  -- Geovetenskap och miljövetenskap -- Naturgeografi (hsv//swe)
NATURAL SCIENCES  -- Earth and Related Environmental Sciences -- Physical Geography (hsv//eng)

Keyword

vector-borne diseases
malaria
African cities
random forest
multi-satellite

Publication and Content Type

ref (subject category)
art (subject category)

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