Additional file 1 of ‘Forest malaria’ in Myanmar? Tracking transmission landscapes in a diversity of environments
Creators: name: Legendre, Eva
name: Girond, Florian
name: Herbreteau, Vincent
name: Hoeun, Sokeang
name: Rebaudet, Stanislas
name: Thu, Aung Myint
name: Rae, Jade Dean
name: Lehot, Laurent
name: Dieng, Sokhna
name: Delmas, Gilles
name: Nosten, François
name: Gaudart, Jean
name: Landier, Jordi
Publisher: name: figshare
Year: 2024
Subjects: subject: Physical Sciences not elsewhere classified
subject: Medicine
subject: Environmental Sciences not elsewhere classified
subject: Ecology
subject: FOS: Biological sciences
subject: Cancer
subject: Infectious Diseases
subject: FOS: Health sciences
subject: Computational Biology

description: Additional file 1: Figure S1. Maps showing incidence profiles. Mapped villages in the malaria elimination task force (METF) target region according to malaria incidence profiles. A Plasmodium falciparum incidence profiles and B Plasmodium vivax incidence profiles; inset shows a map of the Hpapun/Mutraw administrative township (Northern Township). This original figure presents raw data and results published previously [14]. Method S1. Landscape. Figure S2. Villages receiving mass drug administration (MDA) according to the year. MDAs were conducted in high prevalence hotspots identified by prevalence surveys [13]. At the beginning of the METF program there was little knowledge about the location of malaria hotspots. Prevalence surveys were therefore conducted in randomly selected villages, and followed an east (closer to Thailand) to west pattern related to the gradual deployment of the program from the border with Thailand towards the interior of Myanmar. The spatial analysis of prevalence measured in the first surveys revealed spatial clustering of hotspot villages: there was a higher likelihood of finding a hotspot within 10 km of another one [12]. In addition, hotspots also displayed a higher incidence of clinical malaria. After 2015, surveys were targeted based on these two criteria. As a result of this, MDAs were concentrated in the Northern Township. Figure S3. Climate description. Ombrothermic diagram of the five climates identified at region scale. Temperature data of June, July and August were excluded from the analysis because of too many missing data points due to cloud cover in the rainy season (dotted lines). Rainfall data of November, December, January, February and March, corresponding to the cold dry season, were also excluded because of the occurrence of rare, local and intense thunderstorms. Figure S4. Land use and land cover classification description. A Map of the land use and land cover (LULC) classification. B Distribution of the LULC class at the region scale and the Northern township scale. Figure S5. Landscape map at region scale. A Landscape map. B Altitude map. Figure S6. Landscape description at region scale. LULC description of landscapes representing more than 1% of the region surface, included in association analysis (A) or excluded (B). C Description of landscapes identified at region scale according to altitude. Figure S7. Covariates selection. Landscapes were selected according to their median distance to villages at region (A) and township (B) scale. Landscapes with a median ≤ 10 km (red dotted and solid lines) were included. Sensitive analysis was conducted with cut-offs of 5 km (grey dotted line) and 15 km (blue dotted and solid lines). Landscapes selected with the 10-km and 5-km cut-offs were similar. Figure S8. Landscapes in the Northern Township. A Description of the two landscapes specifically identified in the Northern Township according to the LULC classification. The other landscapes were also identified at region scale and described in figure S5. B Map of the most common landscapes identified at the township scale. Figure S9. Study association between malaria incidence profiles and environment in the METF region by conditional random forests (CRF). A 10-km cut-off was applied to selected landscape covariates. A CRF of Plasmodium falciparum incidence profiles depending on landscapes, climates, and MDA before March 2016. B CRF of Plasmodium vivax incidence profiles depending on landscapes, climates, and MDA after February 2016. Figure S10. Study association between malaria incidence profiles and environment in the Northern Township by CRF. A 10-km cut-off was applied to select landscape covariates. A CRF of P. falciparum incidence profiles depending on landscapes, climates, and MDA before March 2016. B CRF of P. vivax incidence profiles depending on landscapes, climates, and MDA after February 2016. Figure S11. Association study between malaria incidence profiles and environment in METF region by conditional inference trees (CIT). A 15-km cut-off was applied to select landscape covariates. A CIT of P. falciparum incidence profiles depending on landscapes, climates, and MDA before March 2016. B Location of villages depending on their nodes obtained with P. falciparum CIT. C CIT of P. vivax incidence profiles depending on 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. Figure S12. Association study between malaria incidence profiles and environment in METF region by CRF. A 15-km cut-off was applied to select landscape covariates. A CRF of P. falciparum incidence profiles depending on landscapes, climates, and MDA before March 2016. B CRF of P. vivax incidence profiles depending on landscapes, climates, and MDA after February 2016. Figure S13. Association study between malaria incidence profiles and environment in the Northern Township by CIT. A 15-km cut-off was applied to select landscape covariates. A CIT of P. falciparum incidence profiles depending on landscapes, climates, and MDA before March 2016. B Location of villages depending on their nodes obtained with P. falciparum CIT. C CIT of P. vivax incidence profiles depending on 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. Figure S14. Association study between malaria incidence profiles and environment in the Northern Township by CRF. A 15-km cut-off was applied to select landscape covariates. A CRF of P. falciparum incidence profiles depending on landscapes, climates, and MDA before March 2016. B CRF of P. vivax incidence profiles depending on landscapes, climates, and MDA after February 2016. Figure S15. Description of eco-epidemiological zones according to age and gender malaria cases. Proportion of reported P. falciparum (A) and P. vivax (B) cases during the study period by malaria posts depending on gender (male, female), age, and eco-epidemiological zones. The total number of malaria cases is specified in the legend. Five classes described cases by age and gender: woman and men between 0 and 5 years (W/M 0-5), women between 5 and 15 years (W 5-15), women older than 15 years (W 15-99), men between 5 and 15 years (M 5-15), men older than 15 years (M 15-99). Table S1. Eco-epidemiological zone construction. Tables present the number of villages in each node and proportions in brackets. A Plasmodium falciparum zone construction by cross analysis between CIT results at region and township scale. B Plasmodium vivax zone construction by cross analysis between CIT results at region and township scale. C Cross analysis between P. falciparum and P. vivax. DSC Dense forest, sparse forest, cropland landscape; DSG dense forest, sparse forest, grassland landscape; SG sparse forest, grassland landscape; M midland climate; SM south midland climate; N node.