'Forest malaria' in Myanmar? Tracking transmission landscapes in a diversity of environments

Abstract

Background: In the Greater Mekong Subregion, case-control studies and national-level analyses have shown an association between malaria transmission and forest activities. The term 'forest malaria' hides the diversity of ecosystems in the GMS, which likely do not share a uniform malaria risk. 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 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.

Methods: 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). Environment was described independently of village localization by overlaying a 2-km hexagonal grid over the region. 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. We used conditional inference trees and random forests to study the association between the malaria incidence profile of each village, climate and landscape. Finally, we constructed eco-epidemiological zones to stratify and map malaria risk in the region by summarizing incidence and environment association information.

Results: We identified a high diversity of landscapes (n = 19) corresponding to a gradient from pristine to highly anthropogenically modified landscapes. Within this diversity of landscapes, only three were associated with malaria-affected profiles. These landscapes were composed of a mosaic of dense and sparse forest fragmented by small agricultural patches. A single climate with moderate rainfall and a temperature range suitable for mosquito presence was also associated with malaria-affected profiles. 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.

Conclusions: The term forest malaria covers a multitude of contexts of malaria persistence, dynamics and populations at risk. Intervention planning and surveillance could benefit from consideration of the diversity of landscapes to focus on those specifically associated with malaria transmission.

Keywords: Eco-epidemiological zones; Environment; Greater Mekong Subregion; Malaria; Mapping; Stratification.

Fig. 1

Malaria incidence profiles. A Plasmodium falciparum and B Plasmodium vivax incidence profiles of the 662 villages. Coloured lines correspond to the central village (medoid) of each cluster. Grey lines represent functional incidence rate of all villages included in the cluster across study period. This original figure presents data and results published previously [14]

Fig. 2

Schematic of the statistical plan. Hierarchical ascendant clustering (HAC) on principal components analysis (PCA) built up landscapes and climates based on land use land cover (LULC) or rainfall and temperature data. Conditional inference trees (CIT) and conditional random forests (CRF) realized at region and township scales explored associations between environmental factors and malaria incidence profiles. Eco-epidemiological zones were constructed based on climates and distance to landscapes significantly associated with Plasmodium falciparum and Plasmodium vivax incidence profiles in CIT and CRF. METF Malaria elimination task force

Fig. 3

Climate and landscapes covariates. A Map of climates constructed at the METF region scale. B Map of identified landscapes that were included in the association analysis at the METF region scale. C Map of identified landscapes included in the association analysis when restricting the analysis to the Hpapun/Mutraw administrative township (Northern Township). Black braces indicate landscapes with a median distance of ≤ 10 km from villages. Red brace indicates landscapes identified only in the Northern Township. For abbreviations, see Fig. 2

Fig. 4

Association study between malaria incidence profiles and environment in the METF region by CITs. A 10-km cut-off was applied to select landscape covariates. A CIT of Plasmodium falciparum incidence profiles with respect to five landscapes, climates, and mass drug administration (MDA) before March 2016. B Location of villages depending on their nodes obtained with P. falciparum CIT. C CIT of Plasmodium vivax incidence profiles depending on five landscapes, climates, and MDA after February 2016. D Location of villages depending on their nodes obtained with P. vivax CIT. (In CIT, ‘node’ refers to a group of villages.) For other abbreviations, see Fig. 2

Fig. 5

Association study between malaria incidence profiles and environment in Northern Township by CIT. A 10-km cut-off was applied to select landscape covariates. A CIT of Plasmodium falciparum incidence profiles depending on five landscapes, climates, and MDA before March 2016. B Location of villages depending on their nodes obtained with P. falciparum CIT. C CIT of Plasmodium vivax incidence profiles depending on five landscapes, climates, and MDA after February 2016. D Location of villages depending on their nodes obtained with P. vivax CIT. For abbreviations, see Figs. 2 and 4

Fig. 6

Description of eco-epidemiological zones. A Map of eco-epidemiological zones. Geographic localization of zones was determined at hexagon level according to their landscape and climate characteristics. B Description of the seven zones according to observed Plasmodium falciparum and Plasmodium vivax monthly incidence during the study period. DSC Dense forest, sparse forest, cropland landscape; DSG dense forest, sparse forest, grassland landscape; SG sparse forest, grassland landscape; HM high mountain climate; WP west plain climate; Z zone