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Data 2017, 2(4), 37; doi:10.3390/data2040037

Regionalization of a Landscape-Based Hazard Index of Malaria Transmission: An Example of the State of Amapá, Brazil

Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 100084, China
ESPACE-DEV, UMR 228 IRD/UM/UR/UG, Institut de Recherche pour le Développement (IRD), 500 rue Jean-François Breton, Montpellier 34000, France
Department of Geography, University of Brasilia, 70910-900 Brasilia, Brazil
National Institute for Space Research (INPE)—Image Processing Division, Av. dos Astronautas, 1758, 12227-010 São José dos Campos, Brazil
Institute of Scientific and Technological Communication and Information in Health (ICICT), Oswaldo Cruz Foundation (FIOCRUZ), Av. Brasil 4365, Manguinhos, Rio de Janeiro RJ 21045-900, Brazil
Authors to whom correspondence should be addressed.
Received: 19 September 2017 / Revised: 30 October 2017 / Accepted: 1 November 2017 / Published: 2 November 2017
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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. View Full-Text
Keywords: malaria; landscape-based hazard index; large-scale; Amazon malaria; landscape-based hazard index; large-scale; Amazon

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Li, Z.; Catry, T.; Dessay, N.; da Costa Gurgel, H.; Aparecido de Almeida, C.; Barcellos, C.; Roux, E. Regionalization of a Landscape-Based Hazard Index of Malaria Transmission: An Example of the State of Amapá, Brazil. Data 2017, 2, 37.

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