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...
| Main Authors: | Zhichao, Li, Catry, Thibault, Dessay, Nadine, Gurgel, Helen da Costa, Almeida, Cláudio Aparecido de, Barcellos, Christovam, Roux, Emmanuel |
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| Format: | Artigo |
| Language: | Português |
| Published: |
MDPI
2019
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| Subjects: | |
| Online Access: |
https://repositorio.unb.br/handle/10482/35936 https://doi.org/10.3390/data2040037 |
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| Summary: |
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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