Regionalization of a landscape-based hazard index of malaria transmission : an example of the State of Amapá, Brazil
Autor(a) principal: | |
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Data de Publicação: | 2017 |
Outros Autores: | , , , , , |
Tipo de documento: | Artigo |
Idioma: | por |
Título da fonte: | Repositório Institucional da UnB |
Texto Completo: | https://repositorio.unb.br/handle/10482/35936 https://doi.org/10.3390/data2040037 |
Resumo: | Identifying and assessing the relative effects of the numerous determinants of malaria transmission, at different spatial scales and resolutions, is of primary importance in defining control strategies and reaching the goal of the elimination of malaria. In this context, based on a knowledge-based model, a normalized landscape-based hazard index (NLHI) was established at a local scale, using a 10 m spatial resolution forest vs. non-forest map, landscape metrics and a spatial moving window. Such an index evaluates the contribution of landscape to the probability of human-malaria vector encounters, and thus to malaria transmission risk. Since the knowledge-based model is tailored to the entire Amazon region, such an index might be generalized at large scales for establishing a regional view of the landscape contribution to malaria transmission. Thus, this study uses an open large-scale land use and land cover dataset (i.e., the 30 m TerraClass maps) and proposes an automatic data-processing chain for implementing NLHI at large-scale. First, the impact of coarser spatial resolution (i.e., 30 m) on NLHI values was studied. Second, the data-processing chain was established using R language for customizing the spatial moving window and computing the landscape metrics and NLHI at large scale. This paper presents the results in the State of Amapá, Brazil. It offers the possibility of monitoring a significant determinant of malaria transmission at regional scale. |
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Zhichao, LiCatry, ThibaultDessay, NadineGurgel, Helen da CostaAlmeida, Cláudio Aparecido deBarcellos, ChristovamRoux, Emmanuel2019-12-11T12:27:16Z2019-12-11T12:27:16Z2017ZHICHAO, Li et al. Regionalization of a landscape-based hazard index of malaria transmission: an example of the State of Amapá, Brazil. Data, v. 2, n. 4, 37, 2017. DOI: https://doi.org/10.3390/data2040037. Disponível em: https://www.mdpi.com/2306-5729/2/4/37. Acesso em: 11 dez. 2019.https://repositorio.unb.br/handle/10482/35936https://doi.org/10.3390/data2040037MDPI© 2017 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 (http://creativecommons.org/licenses/by/4.0/).info:eu-repo/semantics/openAccessRegionalization of a landscape-based hazard index of malaria transmission : an example of the State of Amapá, Brazilinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleMaláriaAmazôniaIdentifying and assessing the relative effects of the numerous determinants of malaria transmission, at different spatial scales and resolutions, is of primary importance in defining control strategies and reaching the goal of the elimination of malaria. In this context, based on a knowledge-based model, a normalized landscape-based hazard index (NLHI) was established at a local scale, using a 10 m spatial resolution forest vs. non-forest map, landscape metrics and a spatial moving window. Such an index evaluates the contribution of landscape to the probability of human-malaria vector encounters, and thus to malaria transmission risk. Since the knowledge-based model is tailored to the entire Amazon region, such an index might be generalized at large scales for establishing a regional view of the landscape contribution to malaria transmission. Thus, this study uses an open large-scale land use and land cover dataset (i.e., the 30 m TerraClass maps) and proposes an automatic data-processing chain for implementing NLHI at large-scale. First, the impact of coarser spatial resolution (i.e., 30 m) on NLHI values was studied. Second, the data-processing chain was established using R language for customizing the spatial moving window and computing the landscape metrics and NLHI at large scale. This paper presents the results in the State of Amapá, Brazil. It offers the possibility of monitoring a significant determinant of malaria transmission at regional scale.porreponame:Repositório Institucional da UnBinstname:Universidade de Brasília (UnB)instacron:UNBORIGINALARTIGO_RegionalizationLandscapeBased.pdfARTIGO_RegionalizationLandscapeBased.pdfapplication/pdf8628706http://repositorio2.unb.br/jspui/bitstream/10482/35936/1/ARTIGO_RegionalizationLandscapeBased.pdffd9465398233d8eaa468a4f1e7ab2947MD51open accessLICENSElicense.txtlicense.txttext/plain163http://repositorio2.unb.br/jspui/bitstream/10482/35936/2/license.txtba54f8d1c5f5ec8df897ad678916c701MD52open access10482/359362023-05-26 21:32:49.97open accessoai:repositorio2.unb.br:10482/35936U3VibWlzc8OjbyBlZmV0aXZhZGEgcG9yIGludGVncmFudGUgZGEgZXF1aXBlIGRvIFJlcG9zaXTDs3JpbyBJbnN0aXR1Y2lvbmFsIGRhIFVuQiBkZSBhY29yZG8gY29tIGxpY2Vuw6dhIGNvbmNlZGlkYSBwZWxvIGF1dG9yIGUvb3UgZGV0ZW50b3IgZG9zIGRpcmVpdG9zIGF1dG9yYWlzLg==Biblioteca Digital de Teses e DissertaçõesPUBhttps://repositorio.unb.br/oai/requestopendoar:2023-05-27T00:32:49Repositório Institucional da UnB - Universidade de Brasília (UnB)false |
dc.title.pt_BR.fl_str_mv |
Regionalization of a landscape-based hazard index of malaria transmission : an example of the State of Amapá, Brazil |
title |
Regionalization of a landscape-based hazard index of malaria transmission : an example of the State of Amapá, Brazil |
spellingShingle |
Regionalization of a landscape-based hazard index of malaria transmission : an example of the State of Amapá, Brazil Zhichao, Li Malária Amazônia |
title_short |
Regionalization of a landscape-based hazard index of malaria transmission : an example of the State of Amapá, Brazil |
title_full |
Regionalization of a landscape-based hazard index of malaria transmission : an example of the State of Amapá, Brazil |
title_fullStr |
Regionalization of a landscape-based hazard index of malaria transmission : an example of the State of Amapá, Brazil |
title_full_unstemmed |
Regionalization of a landscape-based hazard index of malaria transmission : an example of the State of Amapá, Brazil |
title_sort |
Regionalization of a landscape-based hazard index of malaria transmission : an example of the State of Amapá, Brazil |
author |
Zhichao, Li |
author_facet |
Zhichao, Li Catry, Thibault Dessay, Nadine Gurgel, Helen da Costa Almeida, Cláudio Aparecido de Barcellos, Christovam Roux, Emmanuel |
author_role |
author |
author2 |
Catry, Thibault Dessay, Nadine Gurgel, Helen da Costa Almeida, Cláudio Aparecido de Barcellos, Christovam Roux, Emmanuel |
author2_role |
author author author author author author |
dc.contributor.author.fl_str_mv |
Zhichao, Li Catry, Thibault Dessay, Nadine Gurgel, Helen da Costa Almeida, Cláudio Aparecido de Barcellos, Christovam Roux, Emmanuel |
dc.subject.keyword.pt_BR.fl_str_mv |
Malária Amazônia |
topic |
Malária Amazônia |
description |
Identifying and assessing the relative effects of the numerous determinants of malaria transmission, at different spatial scales and resolutions, is of primary importance in defining control strategies and reaching the goal of the elimination of malaria. In this context, based on a knowledge-based model, a normalized landscape-based hazard index (NLHI) was established at a local scale, using a 10 m spatial resolution forest vs. non-forest map, landscape metrics and a spatial moving window. Such an index evaluates the contribution of landscape to the probability of human-malaria vector encounters, and thus to malaria transmission risk. Since the knowledge-based model is tailored to the entire Amazon region, such an index might be generalized at large scales for establishing a regional view of the landscape contribution to malaria transmission. Thus, this study uses an open large-scale land use and land cover dataset (i.e., the 30 m TerraClass maps) and proposes an automatic data-processing chain for implementing NLHI at large-scale. First, the impact of coarser spatial resolution (i.e., 30 m) on NLHI values was studied. Second, the data-processing chain was established using R language for customizing the spatial moving window and computing the landscape metrics and NLHI at large scale. This paper presents the results in the State of Amapá, Brazil. It offers the possibility of monitoring a significant determinant of malaria transmission at regional scale. |
publishDate |
2017 |
dc.date.issued.fl_str_mv |
2017 |
dc.date.accessioned.fl_str_mv |
2019-12-11T12:27:16Z |
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2019-12-11T12:27:16Z |
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format |
article |
status_str |
publishedVersion |
dc.identifier.citation.fl_str_mv |
ZHICHAO, Li et al. Regionalization of a landscape-based hazard index of malaria transmission: an example of the State of Amapá, Brazil. Data, v. 2, n. 4, 37, 2017. DOI: https://doi.org/10.3390/data2040037. Disponível em: https://www.mdpi.com/2306-5729/2/4/37. Acesso em: 11 dez. 2019. |
dc.identifier.uri.fl_str_mv |
https://repositorio.unb.br/handle/10482/35936 |
dc.identifier.doi.pt_BR.fl_str_mv |
https://doi.org/10.3390/data2040037 |
identifier_str_mv |
ZHICHAO, Li et al. Regionalization of a landscape-based hazard index of malaria transmission: an example of the State of Amapá, Brazil. Data, v. 2, n. 4, 37, 2017. DOI: https://doi.org/10.3390/data2040037. Disponível em: https://www.mdpi.com/2306-5729/2/4/37. Acesso em: 11 dez. 2019. |
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