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Remote sensing of environmental risk factors for malaria in different geographic contexts
BACKGROUND: Despite global intervention efforts, malaria remains a major public health concern in many parts of the world. Understanding geographic variation in malaria patterns and their environmental determinants can support targeting of malaria control and development of elimination strategies. M...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8201719/ https://www.ncbi.nlm.nih.gov/pubmed/34120599 http://dx.doi.org/10.1186/s12942-021-00282-0 |
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author | McMahon, Andrea Mihretie, Abere Ahmed, Adem Agmas Lake, Mastewal Awoke, Worku Wimberly, Michael Charles |
author_facet | McMahon, Andrea Mihretie, Abere Ahmed, Adem Agmas Lake, Mastewal Awoke, Worku Wimberly, Michael Charles |
author_sort | McMahon, Andrea |
collection | PubMed |
description | BACKGROUND: Despite global intervention efforts, malaria remains a major public health concern in many parts of the world. Understanding geographic variation in malaria patterns and their environmental determinants can support targeting of malaria control and development of elimination strategies. METHODS: We used remotely sensed environmental data to analyze the influences of environmental risk factors on malaria cases caused by Plasmodium falciparum and Plasmodium vivax from 2014 to 2017 in two geographic settings in Ethiopia. Geospatial datasets were derived from multiple sources and characterized climate, vegetation, land use, topography, and surface water. All data were summarized annually at the sub-district (kebele) level for each of the two study areas. We analyzed the associations between environmental data and malaria cases with Boosted Regression Tree (BRT) models. RESULTS: We found considerable spatial variation in malaria occurrence. Spectral indices related to land cover greenness (NDVI) and moisture (NDWI) showed negative associations with malaria, as the highest malaria rates were found in landscapes with low vegetation cover and moisture during the months that follow the rainy season. Climatic factors, including precipitation and land surface temperature, had positive associations with malaria. Settlement structure also played an important role, with different effects in the two study areas. Variables related to surface water, such as irrigated agriculture, wetlands, seasonally flooded waterbodies, and height above nearest drainage did not have strong influences on malaria. CONCLUSION: We found different relationships between malaria and environmental conditions in two geographically distinctive areas. These results emphasize that studies of malaria-environmental relationships and predictive models of malaria occurrence should be context specific to account for such differences. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12942-021-00282-0. |
format | Online Article Text |
id | pubmed-8201719 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-82017192021-06-15 Remote sensing of environmental risk factors for malaria in different geographic contexts McMahon, Andrea Mihretie, Abere Ahmed, Adem Agmas Lake, Mastewal Awoke, Worku Wimberly, Michael Charles Int J Health Geogr Research BACKGROUND: Despite global intervention efforts, malaria remains a major public health concern in many parts of the world. Understanding geographic variation in malaria patterns and their environmental determinants can support targeting of malaria control and development of elimination strategies. METHODS: We used remotely sensed environmental data to analyze the influences of environmental risk factors on malaria cases caused by Plasmodium falciparum and Plasmodium vivax from 2014 to 2017 in two geographic settings in Ethiopia. Geospatial datasets were derived from multiple sources and characterized climate, vegetation, land use, topography, and surface water. All data were summarized annually at the sub-district (kebele) level for each of the two study areas. We analyzed the associations between environmental data and malaria cases with Boosted Regression Tree (BRT) models. RESULTS: We found considerable spatial variation in malaria occurrence. Spectral indices related to land cover greenness (NDVI) and moisture (NDWI) showed negative associations with malaria, as the highest malaria rates were found in landscapes with low vegetation cover and moisture during the months that follow the rainy season. Climatic factors, including precipitation and land surface temperature, had positive associations with malaria. Settlement structure also played an important role, with different effects in the two study areas. Variables related to surface water, such as irrigated agriculture, wetlands, seasonally flooded waterbodies, and height above nearest drainage did not have strong influences on malaria. CONCLUSION: We found different relationships between malaria and environmental conditions in two geographically distinctive areas. These results emphasize that studies of malaria-environmental relationships and predictive models of malaria occurrence should be context specific to account for such differences. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12942-021-00282-0. BioMed Central 2021-06-13 /pmc/articles/PMC8201719/ /pubmed/34120599 http://dx.doi.org/10.1186/s12942-021-00282-0 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research McMahon, Andrea Mihretie, Abere Ahmed, Adem Agmas Lake, Mastewal Awoke, Worku Wimberly, Michael Charles Remote sensing of environmental risk factors for malaria in different geographic contexts |
title | Remote sensing of environmental risk factors for malaria in different geographic contexts |
title_full | Remote sensing of environmental risk factors for malaria in different geographic contexts |
title_fullStr | Remote sensing of environmental risk factors for malaria in different geographic contexts |
title_full_unstemmed | Remote sensing of environmental risk factors for malaria in different geographic contexts |
title_short | Remote sensing of environmental risk factors for malaria in different geographic contexts |
title_sort | remote sensing of environmental risk factors for malaria in different geographic contexts |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8201719/ https://www.ncbi.nlm.nih.gov/pubmed/34120599 http://dx.doi.org/10.1186/s12942-021-00282-0 |
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