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                <forename type="first">Mauricianot</forename>
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                <forename type="first">Tokiniaina</forename>
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                <title level="m">ESOVE 2024 - the 23rd European Society for Vector Ecology Conference. Book of Abstracts</title>
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                  <title>ESOVE 2024 - 23rd European Society for Vector Ecology Conference</title>
                  <date type="start">2024-10-14</date>
                  <date type="end">2024-10-17</date>
                  <settlement>Montpellier</settlement>
                  <country key="FR">France</country>
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              <p>Progress in malaria elimination has been hindered by recent changes in mosquito behaviour and increases in insecticide resistance in response to traditional vector control measures, such as indoor residual spraying and long-lasting insecticide-treated nets. There is 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 resolutions to ensure interventions are well-placed and well-timed. The most compelling approach to achieve this high-resolution information is the application of remote sensing via the use of drones, which are limited in their spatial reach and temporal frequency, preventing their routine use at scale. Here, we propose a method to monitor flooding dynamics in individual rice fields, a primary larval habitat, over very large geographic areas relevant to national malaria control programs aiming to implement LSM at scale. We demonstrate this for a 3,971 km² malaria-endemic district in Madagascar with over 17,000 rice fields. We trained a classification model of surface reflectance on over 200 field observations from over 50 rice fields to produce time-series of bi-weekly flooding dynamics for thousands of rice fields, by combining rice field mapping on OpenStreetMap with Sentinel-1 satellite imagery (radar, 10m) from 2016 - 2022. From these time-series, we obtained key indicators of each rice field useful for LSM implementation such as the timing and frequency of flooding seasons. These monitoring tools were integrated into an interactive GIS dashboard for operational use by vector control programs, 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). 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.</p>
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