Using transportability methods to map the local effectiveness of mass drug administration for malaria in Senegal.

Numerous trials have evaluated the effectiveness of mass drug administration (MDA) to rapidly reduce malaria transmission, but it is unknown whether estimated effects generalize to other populations eligible for MDA. A recent cluster-randomized trial in Senegal found that MDA reduced malaria incidence by 55% in areas routinely deploying seasonal malaria chemoprevention (SMC). Here, we used transportability models with machine learning to generalize trial effects to 116 non-trial Communes where SMC is standard-of-care. Accounting for differences in weather, vegetation, and population density between trial and non-trial areas, we estimated significant reductions in incidence of 36%-65% in 74 non-trial Communes, with larger reductions in areas with higher precipitation, denser vegetation, and lower temperatures. We found that MDA was not effective in the post-intervention year in non-trial Communes, supporting the notion that MDA effects are short-lived. Our approach offers a scalable framework for generalizing trial findings to target environmentally-mediated infectious disease interventions.