Targeted treatments against Aedes albopictus (Stegomyia) (Diptera: Culicidae) in heterogeneous landscapes: Insights from a data-driven model

Aedes albopictus is one of the most prolific nuisance mosquitoes in the United States and a potential vector of important arboviruses.  The control of Ae. albopictus and other container ovipositing Aedes has often proven difficult, due in part to their heterogeneous distribution across landscapes.  This, coupled with the expansion of Aedes-vectored diseases, has resulted in a renewed interest in developing strategies for effectively and efficiently reducing populations.  Unfortunately, neighborhood- and city-scale field trials are costly and time consuming to perform and are not capable of testing all possible control strategies.  Data-driven models provide an opportunity to evaluate the potential effectiveness of various strategies to inform the direction of future field trials.  Using a model parameterized with data from a field experiment, we evaluated the effectiveness of various strategies for deploying targeted controls across a heterogeneous neighborhood.  We show that a high level of suppression is possible when as few as a quarter of the yards are treated.  In addition, leveraging information about the distribution of Aedes mosquitoes across the neighborhood significantly increases the predicted suppression, even when that information is imperfect.  This suggests that it is possible to attain a sufficient level of suppression across a heterogeneous landscape using targeted controls deployed based on home-owner complaints or minimal surveillance.