摘要：AbstractDespite progress toward malaria elimination in the Greater Mekong Subregion, challenges remain owing to the emergence of drug resistance and the persistence of focal transmission reservoirs. Malaria transmission foci in Myanmar are heterogeneous and complex, and many remaining infections are clinically silent, rendering them invisible to routine monitoring. The goal of this research is to define criteria for easy‐to‐implement methodologies, not reliant on routine monitoring, that can increase the efficiency of targeted malaria elimination strategies. Studies have shown relationships between malaria risk and land cover and land use (LCLU), which can be mapped using remote sensing methodologies. Here we aim to explain malaria risk as a function of LCLU for five rural villages in Myanmar's Rakhine State. Malaria prevalence and incidence data were analyzed through logistic regression with a land use survey of ~1,000 participants and a 30‐m land cover map. Malaria prevalence per village ranged from 5% to 20% with the overwhelming majority of cases being subclinical. Villages with high forest cover were associated with increased risk of malaria, even for villagers who did not report visits to forests. Villagers living near croplands experienced decreased malaria risk unless they were directly engaged in farm work. Finally, land cover change (specifically, natural forest loss) appeared to be a substantial contributor to malaria risk in the region, although this was not confirmed through sensitivity analyses. Overall, this study demonstrates that remotely sensed data contextualized with field survey data can be used to inform critical targeting strategies in support of malaria elimination.Plain Language SummaryWhile much progress has been made in recent years toward eliminating malaria in Myanmar, full elimination remains a challenge that is amplified by the emerging of drug‐resistant malaria parasites. The lack of clinical symptoms in many people infected with malaria makes it extremely challenging to find the remaining reservoirs of the disease in the country. Previous studies identified some linkages between the prevalence of malaria and land cover and land use (LCLU) patterns. Satellite monitoring of LCLU could thus help identify potential areas where malaria elimination activities should be deployed. In this study, blood samples and surveys on land use activities (farming, visiting forests, etc.), collected in five villages in Myanmar's Rakhine State, were used to establish and describe the relationship between satellite‐observed LCLU patterns surrounding the village and malaria prevalence. Results indicate that villages surrounded by lands with high amounts of forest cover were strongly associated with increased risk of malaria, even for villagers who did not report frequent visits to forested lands. Overall, this study demonstrates that satellite imagery data can be an important tool in support of targeted malaria elimination.Key PointsVillages in Ann Township, Myanmar, exhibiting high forest cover are strongly associated with increased risk of malariaAnn Township, Myanmar, villagers living in villages where croplands are the dominant land cover type experience decreased malaria riskRemote sensing offers a means to locate LCLU areas associated with high malaria risk to allow for more efficient targeted interventions
