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Sökning: onr:"swepub:oai:DiVA.org:kth-322204" > The Multi-Satellite...

LIBRIS Formathandbok  (Information om MARC21)
FältnamnIndikatorerMetadata
00004099naa a2200469 4500
001oai:DiVA.org:kth-322204
003SwePub
008221206s2022 | |||||||||||000 ||eng|
0247 a https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-3222042 URI
0247 a https://doi.org/10.3390/rs142153812 DOI
040 a (SwePub)kth
041 a engb eng
042 9 SwePub
072 7a ref2 swepub-contenttype
072 7a art2 swepub-publicationtype
1001 a Morlighem, Camilleu Univ Namur, Dept Geog, B-5000 Namur, Belgium.;Univ Namur, ILEE, B-5000 Namur, Belgium.4 aut
2451 0a The Multi-Satellite Environmental and Socioeconomic Predictors of Vector-Borne Diseases in African Cities :b Malaria as an Example
264 c 2022-10-27
264 1b MDPI AG,c 2022
338 a print2 rdacarrier
500 a QC 20221206
520 a 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.
650 7a NATURVETENSKAPx Geovetenskap och miljövetenskapx Naturgeografi0 (SwePub)105072 hsv//swe
650 7a NATURAL SCIENCESx Earth and Related Environmental Sciencesx Physical Geography0 (SwePub)105072 hsv//eng
653 a vector-borne diseases
653 a malaria
653 a African cities
653 a random forest
653 a multi-satellite
7001 a Chaiban, Celiau Univ Namur, Dept Geog, B-5000 Namur, Belgium.;Univ Namur, ILEE, B-5000 Namur, Belgium.4 aut
7001 a Georganos, Stefanosu KTH,Geoinformatik,Univ Libre Bruxelles, Dept Geosci Environm & Soc, B-1050 Brussels, Belgium.4 aut0 (Swepub:kth)u1qeaces
7001 a Brousse, Oscaru Katholieke Univ Leuven, Dept Earth & Environm Sci, B-3001 Leuven, Belgium.;UCL, Inst Environm Design & Engn, London W H 0NN, England.4 aut
7001 a Van de Walle, Jonasu Katholieke Univ Leuven, Dept Earth & Environm Sci, B-3001 Leuven, Belgium.4 aut
7001 a van Lipzig, Nicole P. M.u Katholieke Univ Leuven, Dept Earth & Environm Sci, B-3001 Leuven, Belgium.4 aut
7001 a Wolff, Eleonoreu Univ Libre Bruxelles, Dept Geosci Environm & Soc, B-1050 Brussels, Belgium.4 aut
7001 a Dujardin, Sebastienu Univ Namur, Dept Geog, B-5000 Namur, Belgium.;Univ Namur, ILEE, B-5000 Namur, Belgium.4 aut
7001 a Linard, Catherineu Univ Namur, Dept Geog, B-5000 Namur, Belgium.;Univ Namur, ILEE, B-5000 Namur, Belgium.;Univ Namur, NARILIS, B-5000 Namur, Belgium.4 aut
7102 a Univ Namur, Dept Geog, B-5000 Namur, Belgium.;Univ Namur, ILEE, B-5000 Namur, Belgium.b Geoinformatik4 org
7730 t Remote Sensingd : MDPI AGg 14:21q 14:21x 2072-4292
8564 u https://doi.org/10.3390/rs14215381y Fulltext
8564 8u https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-322204
8564 8u https://doi.org/10.3390/rs14215381

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