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.

Mobility and non-household environments: understanding dengue transmission patterns in urban contexts

“…To achieve the aims of this study, we modified the agent-based model previously used to describe the importance of HH and NH in transmission [10]. The model was developed to quantify the relative contribution of five different types of NH (workplaces, markets or shops, recreational, religious, and schools) and HH to dengue burden.…”

“…The model was developed to quantify the relative contribution of five different types of NH (workplaces, markets or shops, recreational, religious, and schools) and HH to dengue burden. The model development and calibration were based on data from the two Kenyan cities of Kisumu and Ukunda [10]. This paper’s results focus on parameters calibrated to Kisumu, although additional results including dynamics from Ukunda are found in supporting information.…”

“…The number of people visiting NH was simulated at three levels. First, we included the same levels previously described in the model, categorized as 100% mobility [10]. This treatment includes all students and workers attending their respective school and workplace, and random-attendance locations (religious, markets/shops, and recreational) with number of people uniformly distributed with parameters minimum = 10 and maximum = 70.…”

“…However, some studies have suggested that locations other than households might have an important role because of a significant presence of mosquitoes [5–8], and infected vectors [9]. Recently, a study using agent-based modeling to quantify the number of infections in different types of urban spaces estimated that over half of infections are happening in non-household (NH) environments, where the main high-risk spaces are workplaces and markets/shops [10].…”

“…In particular, what role does spatial configuration of NH spaces play, along with the extent to which people and mosquitoes move between spaces, in determining dengue dynamics? To address this, we modified a previously reported agent-based model [10] to make it spatially explicit by assigning coordinates that mimic different urban conformations and evaluated different scenarios of movement of people and vectors. We then assessed how these variables affect the burden of dengue and the spatial patterns to understand urban-level transmission dynamics.…”