Abstract
Vector control is an effective strategy for reducing vector‐borne disease transmission, but requires knowledge of vector habitat use and dispersal patterns. Our goal was to improve this knowledge for the tsetse species Glossina pallidipes, a vector of human and animal African trypanosomiasis, which are diseases that pose serious health and socioeconomic burdens across sub‐Saharan Africa. We used random forest regression to (i) build and integrate models of G. pallidipes habitat suitability and genetic connectivity across Kenya and northern Tanzania and (ii) provide novel vector control recommendations. Inputs for the models included field survey records from 349 trap locations, genetic data from 11 microsatellite loci from 659 flies and 29 sampling sites, and remotely sensed environmental data. The suitability and connectivity models explained approximately 80% and 67% of the variance in the occurrence and genetic data and exhibited high accuracy based on cross‐validation. The bivariate map showed that suitability and connectivity vary independently across the landscape and was used to inform our vector control recommendations. Post hoc analyses show spatial variation in the correlations between the most important environmental predictors from our models and each response variable (e.g., suitability and connectivity) as well as heterogeneity in expected future climatic change of these predictors. The bivariate map suggests that vector control is most likely to be successful in the Lake Victoria Basin and supports the previous recommendation that G. pallidipes from most of eastern Kenya should be managed as a single unit. We further recommend that future monitoring efforts should focus on tracking potential changes in vector presence and dispersal around the Serengeti and the Lake Victoria Basin based on projected local climatic shifts. The strong performance of the spatial models suggests potential for our integrative methodology to be used to understand future impacts of climate change in this and other vector systems.
Highlights
Worldwide, vector-borne diseases account for more than 17% of all infectious diseases in humans and represent a significant socioeconomic burden through decreases in livestock milk production, birth rates, weight gain, and survival (Chanie et al, 2013; Narladkar, 2018; Rohr et al, 2019)
There have only been a few cases of HAT reported recently in the study area (Franco et al, 2014; World Health Organization, 2020), both Kenya and Tanzania remain classified by the World Health Organization (WHO) as regions of HAT public health concern because of lack of control and surveillance activities (Franco et al, 2020)
We identified regions that may host resilient tsetse fly populations, potential routes of recolonization, and candidate isolated locations for local eradication and/or development of novel vector control strategies
Summary
Vector-borne diseases account for more than 17% of all infectious diseases in humans and represent a significant socioeconomic burden through decreases in livestock milk production, birth rates, weight gain, and survival (Chanie et al, 2013; Narladkar, 2018; Rohr et al, 2019). Variation in vector survival and dispersal are two components that most strongly influence long-term disease transmission. Both survival and dispersal can be modeled spatially as estimates of habitat suitability and genetic connectivity (Bouyer et al, 2015; Dicko et al, 2014; Hirzel & Lay, 2008), which can improve our ability to plan and implement disease control interventions. Tsetse flies (genus Glossina) are obligate vectors of animal and human African trypanosomiasis (AAT and HAT, respectively). These diseases pose serious socioeconomic and health burdens to sub- Saharan Africa. Previous empirical studies and mathematical modeling have indicated that G. pallidipes populations could be reduced to levels that minimize AAT transmission through vector control strategies such as bush clearance, ground spraying using insecticides, odor-baited traps, and insecticide-impregnated targets (Bourn et al, 2001; Davis et al, 2011; Gilbert et al, 2016; Medlock et al, 2013; Ndeffo-Mbah et al, 2019; Pandey et al, 2015)
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