Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Malaria Risk Drivers in the Brazilian Amazon: Land Use: Land Cover Interactions and Biological Diversity

Version 1 : Received: 28 April 2023 / Approved: 29 April 2023 / Online: 29 April 2023 (04:29:50 CEST)

A peer-reviewed article of this Preprint also exists.

Gonzalez Daza, W.; Muylaert, R.L.; Sobral-Souza, T.; Lemes Landeiro, V. Malaria Risk Drivers in the Brazilian Amazon: Land Use—Land Cover Interactions and Biological Diversity. Int. J. Environ. Res. Public Health 2023, 20, 6497. Gonzalez Daza, W.; Muylaert, R.L.; Sobral-Souza, T.; Lemes Landeiro, V. Malaria Risk Drivers in the Brazilian Amazon: Land Use—Land Cover Interactions and Biological Diversity. Int. J. Environ. Res. Public Health 2023, 20, 6497.

Abstract

Malaria is a prevalent disease in several tropical and subtropical regions, including Brazil, where remains a significant public health concern. Despite control efforts, reintroduction of endemics in areas without cases for decades poses a challenge. To assess factors influencing ma-laria risk, regional outbreak cluster analysis and a spatio-temporal models were developed for the Brazilian Amazon, incorporating climate, land use/cover interactions, endemic bird, and amphibian richness. Results showed that amphibian, bird richness and endemism correlated with a reduction in malaria risk. Presence of forest had a positive effect on risk, but it depended on its juxtaposition with anthropic land uses. Biodiversity and landscape composition, rather than forest formation presence alone, modulated malaria risk in the period. Areas with low en-demic species diversity and high human activity, predominantly anthropogenic landscapes posed high malaria risk. This study underscores the importance of considering the broader eco-logical context in malaria control efforts.

Keywords

Malaria; Amazon biome; INLA; Land use/cover interactions; Bird and amphibian rich-ness-endemics; Landscape composition; Biological diversity; Spatio-temporal modeling

Subject

Biology and Life Sciences, Life Sciences

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