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The incidence ofAedes-borne pathogens has been increasing despite vector control efforts. Control strategies typically target households (HH), whereAedesmosquitoes breed in HH containers and bite indoors. However, our study in Kenyan cities of Kisumu and Ukunda (2019–2022) revealed highAedesabundance in public spaces, prompting the question: How important are non-household (NH) environments for dengue transmission and control? Using field data and human activity patterns, we developed an agent-based model simulating transmission across HH and five types of NH environments, which was then used to evaluate preventive (before an epidemic) and reactive (after an epidemic commences) vector control scenarios. Our findings estimate over half of infections occurring in NH settings, particularly workplaces, markets and recreational sites. Container removal was more effective in NH than in HH areas, contrasting with the global focus on HH-based management. Greater reductions in dengue cases occurred with early, high-coverage interventions, especially in NH locations. Additionally, local ecological factors, such as uneven water container distribution, influence control outcomes. This study underscores the importance of vector control in both HH and NH environments in endemic settings. It highlights a specific approach to inform evidence-based decision-making to target limited vector control resources for optimal control. 

Abstract Despite the increasing burden of dengue in Kenya and Africa, the introduction and expansion of the virus in the region remain poorly understood. The objective of this study is to examine the genetic diversity and evolutionary histories of dengue virus (DENV) serotypes 1 and 3 in Kenya and contextualize their circulation within circulation dynamics in the broader African region. Viral RNA was extracted from samples collected from a cohort of febrile patients recruited at clinical sites in Kenya from 2013 to 2022. Samples were tested by polymerase chain reaction (PCR) for DENV presence. Five DENV-positive samples were serotyped, and complete viral genomes for phylogenetic inference were obtained via sequencing on Illumina platforms. Sequences generated in our study were combined with global datasets of sequences, and Bayesian and maximum likelihood methods were used to infer phylogenetic trees and geographic patterns of spread with a focus on Kenya and Africa as a whole. Four new DENV-1 and one new DENV-3 genomes were successfully sequenced and combined with 328 DENV-1 and 395 DENV-3 genomes from elsewhere for phylogenetic analyses. The DENV-1 sequences from our study formed a monophyletic cluster with an inferred common ancestor in 2019 (most recent common ancestor 2019 and 95% high posterior density 2018–19), which was closely related to sequences from Tanzania. The single DENV-3 sequence clustered with sequences from Tanzania and Kenya, was collected between 2017 and 2019 and was related to recent outbreaks in the region. Phylogenetic trees resolved multiple clades of DENV-1 and DENV-3 concurrently circulating in Africa, introduced in the early-to mid-2000s. Three DENV-1 and four DENV-3 clades are highlighted, introduced between 2000 and 2015. Phylogeographic models suggest frequent, independent importations of DENV lineages into Kenya and Africa from East and South-East Asia via distinct geographic pathways. DENV-1 and DENV-3 evolutionary dynamics in Africa are characterized by the cocirculation of multiple recently introduced lineages. Circulating lineages are introduced via distinct geographic pathways that may be centered around regional nexus locations. Increased surveillance is required to identify key regional locations that drive spread, and dengue interventions should focus on interrupting spread at these locations. 

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. 

Abstract Climate drives population dynamics through multiple mechanisms, which can lead to seemingly context-dependent effects of climate on natural populations. For climate-sensitive diseases, such as dengue, chikungunya, and Zika, climate appears to have opposing effects in different contexts. Here we show that a model, parameterized with laboratory measured climate-driven mosquito physiology, captures three key epidemic characteristics across ecologically and culturally distinct settings in Ecuador and Kenya: the number, timing, and duration of outbreaks. The model generates a range of disease dynamics consistent with observed Aedes aegypti abundances and laboratory-confirmed arboviral incidence with variable accuracy (28–85% for vectors, 44–88% for incidence). The model predicted vector dynamics better in sites with a smaller proportion of young children in the population, lower mean temperature, and homes with piped water and made of cement. Models with limited calibration that robustly capture climate-virus relationships can help guide intervention efforts and climate change disease projections.