Abstract
Background
Malaria in the Amazon is driven by socioeconomic, environmental, and political factors like human mobility, land use, and ecology. While past research focused on municipal-level data, cases have become more localized over time, demanding local-level analysis. We propose a regionalization of Amazon malaria accounting for space-time heterogeneities and contingencies.
Materials and Methods
Using data from the Brazilian Malaria Surveillance System (SIVEP, 2007–2023), we applied textual similarity methods to identify community-level malaria patterns. Descriptive statistics, discrete-time multinomial models, Kernel Density, and SKATER cluster analysis were used to characterize frontier malaria and its spatial-temporal dynamics.
Results
We identified geographical shifts of malaria. Clusters of malaria hotspots suggest associations between high malaria and the movement of infected individuals from rural, indigenous, and mining areas (where malaria is primarily autochthonous), to urban residential areas where malaria is mostly imported. We identified different time-space evolution of malaria according to age, race/ethnicity and occupational background. Rural and autochthonous malaria decreased with the stabilization of the agricultural frontier, but significant malaria increase among residents in indigenous and mining areas after 2016 underscores the importance of changing policies addressing human settlements and the environment.
Conclusions
The article discusses a novel perspective of malaria epidemiology as a territorial expression of local-level interactions over time, including distinct governmental terms influences malaria dynamics. It allows understanding how these interactions between localities scale up and consolidate heterogeneous malaria regions embedded in distinct policy orientations that influence the spatial expansion or retraction of the frontier malaria.
Key messages
• We examine the influence of local characteristics on the spread of malaria, generating patterns that can be translated into larger areas where malaria exhibits distinct dissemination behaviors.
• Identifying localized surges in malaria in specific settings and vulnerable populations support the development of actions and measures to address the needs of these groups.