Table 1.
Trapping calendar: Number of nights and schedule.
Fig 1.
Participants and trap localisation.
The basemap shapefile is sourced from https://www.openstreetmap.org/ OpenStreetMap is open data, licensed under the Open Data Commons Open Database License (ODbL), available at https://www.openstreetmap.org/copyright.
Table 2.
Daily mean number and hourly evening median number (from 6PM to midnight) of Anopheles darlingi per month and per trap.
Fig 2.
Average number of Anopheles darlingi per hour by Mosquito Magnet (MM) trap and month year in Trois-Palétuviers, French Guiana.
Data for November 2017 are not available on an hourly basis. Reading explanation: No MM traps were used between 17:00 (5 PM) and 18:00 (6 PM) in 2017. In contrast, MM traps were used during the same time slot in 2018, but no Anopheles darlingi were caught in any of the traps, regardless of their location. The basemap shapefile is sourced from https://www.openstreetmap.org/ OpenStreetMap is open data, licensed under the Open Data Commons Open Database License (ODbL), available at https://www.openstreetmap.org/copyright.
Table 3.
Sociodemographic characteristics of the study population.
Table 4.
Potential exposure inside and outside the village and protection used among participants.
Fig 3.
Human exposure to Anopheles darlingi and nighttime protective measures deployed in Trois-Palétuviers, French Guiana.
Fig 4.
Distribution of Plasmodium spp. episodes (health centre) or positive PCR tests (PALUSTOP) in 2017 (top) and 2018 (bottom) by age group in Trois-Palétuviers, French Guiana.
Fig 5.
Dynamics of malaria cases, Anopheles darlingi caught at the health centre, average monthly temperature and cumulative monthly rainfall in 2017 and 2018, Trois-Palétuviers, French Guiana.
Mean monthly temperature and cumulative monthly rainfall data were collected for the study period using Google Earth Engine using RGEE package from R software [31,32]. Rainfall data were obtained from the CHIRPS dataset, developed by the Climate Hazards Group at the University of California, Santa Barbara (UCSB) [33]. Temperature data come from the ERA5 dataset, which integrates multiple observations into a model that tracks climate change on an almost daily basis [34].
Table 5.
Exposure and protection compared to malaria carriage per year.
Fig 6.
Barplot of the distance to the forest according to malaria status in 2017 with its Generalized additive model result (p = 0.023).
Fig 7.
Study findings regarding potential risk factors for malaria: One Health summary.