The Importance of Including Non-Household Environments in Dengue Vector Control Activities

Abstract

Most vector control activities in urban areas are focused on household environments; however, information relating to infection risks in spaces other than households is poor, and the relative risk that these spaces represent has not yet been fully understood. We used data-driven simulations to investigate the importance of household and non-household environments for dengue entomological risk in two Kenyan cities where dengue circulation has been reported. Fieldwork was performed using four strategies that targeted different stages of mosquitoes: ovitraps, larval collections, Prokopack aspiration, and BG-sentinel traps. Data were analyzed separately between household and non-household environments to assess mosquito presence, the number of vectors collected, and the risk factors for vector presence. With these data, we simulated vector and human populations to estimate the parameter m and mosquito-to-human density in both household and non-household environments. Among the analyzed variables, the main difference was found in mosquito abundance, which was consistently higher in non-household environments in Kisumu but was similar in Ukunda. Risk factor analysis suggests that small, clean water-related containers serve as mosquito breeding places in households as opposed to the trash- and rainfall-related containers found in non-household structures. We found that the density of vectors (m) was higher in non-household than household environments in Kisumu and was also similar or slightly lower between both environments in Ukunda. These results suggest that because vectors are abundant, there is a potential risk of transmission in non-household environments; hence, vector control activities should take these spaces into account.

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

“…Recent field work reported a high number of vectors in NH environments in the Kenyan cities of Kisumu and Ukunda, suggesting potentially high risk for Aedes -borne viruses transmission in these environments 23 . However, until now it was unclear how the total burden of transmission varied based on container type and density, human activity space within HH and NH environments, and variation over time, across ages, and between cities.…”

“…Likewise, the addition of other types of buildings beyond the six types we included in this study might provide a more comprehensive perspective of urban locations where transmission can be happening, like those described previously by our team 23 . Our estimates of movement are mainly based on the time people spend in given locations, but other potential sources of variability were not included like intra-urban distances 26,34 , decrease of mobility due to illness 35 , and travel to other urban centers and rural areas.…”

“…Unfortunately, data related to density of mosquitoes in specific NH locations were not available or did not match with information collected from the people movement data survey. Some assessed spaces reported by Peña-García 23 having mosquitoes like “open spaces”, “gardens” or “banana plantation” were not reported by people as places where they spend any time. Equally, NH categories included in the model like “workplaces” can group some of the categories reported by Peña-García.…”

“…Information related to the number of containers in different environments was obtained from fieldwork performed between 2020 and 2022 in both study sites and previously reported in detail 23 . Briefly, 400 m 2 urban areas were sampled with four different strategies targeting different stages of mosquitoes: ovitraps to obtain information on eggs, container surveys to obtain information of larval stages and container availability, Prokopack aspirators to obtain information related to adults, and BG-Sentinel to obtain information from breeding place-searching females.…”

“…To develop the model, we created two synthetic populations representing each of the Kenyan cities. To do this, we considered the number of inhabitants per household for each city according to information provided by reports of 2019 Kenya Population and Housing Census 36 , which is also in accordance with previously reported for both cities 23 , where the mean number of inhabitants per household is 4.6 for Kisumu and 7.3 for Ukunda. The size of synthetic populations was set to be around 20,000 individuals (final size was 20,172 for Ukunda and 20,160 for Kisumu), so the number of households was set accordingly to both size of human population and the mean number of house inhabitants.…”