Non-household environments make a major contribution to dengue transmission: Implications for vector control.

ABSTRACT

Aedes-borne pathogens have been increasing in incidence in recent decades despite vector control activities implemented in endemic settings. Vector control for Aedes-transmitted arboviruses typically focuses on households because vectors breed in household containers and bite indoors. Yet, our recent work shows a high abundance of Aedes spp. vectors in public spaces. To investigate the impact of non-household environments on dengue transmission and control, we used field data on the number of water containers and abundance of Aedes mosquitoes in Household (HH) and Non-Household (NH) environments in two Kenyan cities, Kisumu and Ukunda, from 2019-2022. Incorporating information on human activity space, we developed an agent-based model to simulate city-wide conditions considering HH and five types of NH environments in which people move and interact with other humans and vectors during peak biting times. We additionally evaluated the outcome of vector control activities implemented in different environments in preventive (before an epidemic) and reactive (after an epidemic commences) scenarios. We estimated that over half of infections take place in NH environments, where the main spaces for transmission are workplaces, markets, and recreational locations. Accordingly, results highlight the important role of vector control activities at NH locations to reduce dengue. A greater reduction of cases is expected as control activities are implemented earlier, at higher levels of coverage, with greater effectiveness when targeting only NH as opposed to when targeting only HH. Further, local ecological factors such as the differential abundance of water containers within cities are also influential factors to consider for control. This work provides insight into the importance of vector control in both household and non-household environments in endemic settings. It highlights a specific approach to inform evidence-based decision making to target limited vector control resources for optimal control.