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Regionalization of a landscape-based hazard index of malaria transmission : an example of the State of Amapá, Brazil

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-base...

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Main Authors: Zhichao, Li, Catry, Thibault, Dessay, Nadine, Gurgel, Helen da Costa, Almeida, Cláudio Aparecido de, Barcellos, Christovam, Roux, Emmanuel
Format: Artigo
Language: Português
Published: MDPI 2019
Subjects:
Malária
Amazônia
Online Access: https://repositorio.unb.br/handle/10482/35936
https://doi.org/10.3390/data2040037
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spelling ir-10482-359362019-12-11T12:27:16Z Regionalization of a landscape-based hazard index of malaria transmission : an example of the State of Amapá, Brazil Zhichao, Li Catry, Thibault Dessay, Nadine Gurgel, Helen da Costa Almeida, Cláudio Aparecido de Barcellos, Christovam Roux, Emmanuel Malária Amazônia 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. 2019-12-11T12:27:16Z 2019-12-11T12:27:16Z 2017 Artigo 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. https://repositorio.unb.br/handle/10482/35936 https://doi.org/10.3390/data2040037 Português Acesso Aberto © 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/). application/pdf MDPI
institution REPOSITORIO UNB
collection REPOSITORIO UNB
language Português
topic Malária
Amazônia
spellingShingle Malária
Amazônia
Zhichao, Li
Catry, Thibault
Dessay, Nadine
Gurgel, Helen da Costa
Almeida, Cláudio Aparecido de
Barcellos, Christovam
Roux, Emmanuel
Regionalization of a landscape-based hazard index of malaria transmission : an example of the State of Amapá, Brazil
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.
format Artigo
author Zhichao, Li
Catry, Thibault
Dessay, Nadine
Gurgel, Helen da Costa
Almeida, Cláudio Aparecido de
Barcellos, Christovam
Roux, Emmanuel
author_sort Zhichao, Li
title Regionalization of a landscape-based hazard index of malaria transmission : an example of the State of Amapá, Brazil
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
publisher MDPI
publishDate 2019
url https://repositorio.unb.br/handle/10482/35936
https://doi.org/10.3390/data2040037
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score 13.657419

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