However, the term ‘forest’ is generic and covers a diversity of ecosystems in the GMS. ‘Forest malaria’ is often reported to be responsible for the remaining foci of transmission in the region, and is considered an obstacle to malaria elimination. Individual-scale studies consistently identified occupation-related risks factors, such as male sex, age over 15 years, or ‘going into the forest’. In the GMS, studies based on aggregated surveillance data associated malaria transmission with forested areas. The GMS now aims to eliminate all forms of malaria. These interventions were focused on Plasmodium falciparum to prevent the spread of multidrug-resistant parasite lineages. Between 20, the number of malaria cases declined sevenfold, dropping from approximately 2,400,000 cases to 325,000 due to improvements in early access to diagnosis and treatment, and targeted interventions for the most affected populations (i.e. The Greater Mekong Subregion (GMS) has made progress towards malaria elimination during the past decades. Intervention planning and surveillance could benefit from consideration of the diversity of landscapes to focus on those specifically associated with malaria transmission. The term forest malaria covers a multitude of contexts of malaria persistence, dynamics and populations at risk. Based on these environmental associations, we identified three eco-epidemiological zones marked by later persistence of Plasmodium falciparum, high Plasmodium vivax incidence after 2018, or a seasonality pattern in the rainy season. A single climate with moderate rainfall and a temperature range suitable for mosquito presence was also associated with malaria-affected profiles. These landscapes were composed of a mosaic of dense and sparse forest fragmented by small agricultural patches. Within this diversity of landscapes, only three were associated with malaria-affected profiles. We identified a high diversity of landscapes ( n = 19) corresponding to a gradient from pristine to highly anthropogenically modified landscapes. Finally, we constructed eco-epidemiological zones to stratify and map malaria risk in the region by summarizing incidence and environment association information. We used conditional inference trees and random forests to study the association between the malaria incidence profile of each village, climate and landscape. Specifically, hierarchical classification on principal components, using remote sensing data of high spatial resolution, was used to assign a landscape and a climate type to each grid cell. Environment was described independently of village localization by overlaying a 2-km hexagonal grid over the region. We characterized malaria incidence profiles at village scale based on intra- and inter-annual variations in amplitude, seasonality, and trend over 4 years (2016–2020). The aim of this ecological study is to characterize the association between malaria dynamics and detailed ecological environments determined at village level over a period of several years in Kayin State, Myanmar. To reach malaria elimination goals, it is crucial to document accurately (both spatially and temporally) the influence of environmental factors on malaria to improve resource allocation and policy planning within given areas. The term ‘forest malaria’ hides the diversity of ecosystems in the GMS, which likely do not share a uniform malaria risk. In the Greater Mekong Subregion, case–control studies and national-level analyses have shown an association between malaria transmission and forest activities.
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