Monitoring individual rice field flooding dynamics over a large scale to improve mosquito surveillance and control

Le résumé fourni par la source

BACKGROUND: Progress in malaria elimination has been hindered by recent changes in mosquito behaviour and increased insecticide resistance in response to traditional vector control measures, such as indoor residual spraying and long-lasting insecticidal nets. There is, therefore, increasing interest in the use of larval source management (LSM) to supplement current insecticide-based interventions. However, LSM implementation requires the characterization of larval habitats at fine spatial and temporal scales to ensure interventions are well-placed and well-timed. Remotely sensed optical imagery captured via drones or satellites offers one way to monitor larval habitats remotely, but its use at large spatio-temporal scales has important limitations. METHODS: malaria-endemic district in Madagascar with over 17,000 rice fields. Rice field mapping on OpenStreetMap was combined with Sentinel-1 satellite imagery (radar, 10 m) from 2016 to 2022 to train a classification model of radar backscatter to identify rice fields with vegetated and open water, resulting in a time-series of weekly flooding dynamics for thousands of rice fields. RESULTS: From these time-series, over a dozen indicators useful for LSM implementation, such as the timing and frequency of flooding seasons, were obtained for each rice field. These monitoring tools were integrated into an interactive GIS dashboard for operational use by vector control programmes, with results available at multiple scales (district, sub-district, rice field) relevant for different phases of LSM intervention (e.g. prioritization of sites, implementation, follow-up). CONCLUSIONS: Scale-up of these methods could enable wider implementation of evidence-based LSM interventions and reduce malaria burdens in contexts where irrigated agriculture is a major transmission driver.

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