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Abstract. Tsetse flies are the primary vector for African trypanosomiasis, a neglected tropical disease that affects both humans and livestock across the continent of Africa. In 1973 tsetse were estimated to inhabit 22% of Kenya; by 1996 that number had risen to roughly 34%. Efforts to control the disease are hampered by a lack of information and costs associated with the identification of infested areas. To aid control efforts we have constructed the Tsetse Ecological Distribution Model (TED Model). The TED Model is a raster based dynamic species distribution model that predicts tsetse distributions at 250 m spatial resolution, based on habitat suitability and fly movement rates, at 16-day intervals. Although the TED Model can be parameterized to any tsetse subgenus/species requirements, for the purpose of this study the TED Model was parameterized to identify suitable habitat for Glossina subgenus Morsitans. Using the TED Model we have identified where and when Glossina subgenus Morsitans populations should be constrained by unfavorable ecological conditions to particular parcels of suitable habitat. It is our hope that by utilizing the predicted locations of tsetse reservoirs and refugia, control efforts will be better able to target tsetse populations when they are spatially constrained, thus maximizing limited available resources.

African trypanosomiasis, otherwise known as sleeping sickness in humans and nagana in animals, is a parasitic protist passed cyclically by the tsetse fly. Despite more than a century of control and eradication efforts, the fly remains widely distributed across Africa and coextensive with other prevalent diseases. Control and planning are hampered by spatially and temporally variant vector distributions, ecologically irrelevant boundaries, and neglect. Tsetse are particularly well suited to move into previously disease-free areas under climate change scenarios, placing unprepared populations at risk. Here we present the modeling framework ATcast, which combines a dynamically downscaled regional climate model with a temporally and spatially dynamic species distribution model to predict tsetse populations over space and time. These modeled results are integrated with Kenyan population data to predict, for the period 2050 to 2059, exposure potential to tsetse and, by association, sleeping sickness and nagana across Kenya.

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Background African trypanosomiasis, also known as “sleeping sickness” in humans and “nagana” in livestock is an important vector-borne disease in Sub-Saharan Africa. Control of trypanosomiasis has focused on eliminating the vector, the tsetse fly (Glossina, spp.). Effective tsetse fly control planning requires models to predict tsetse population and distribution changes over time and space. Traditional planning models have used statistical tools to predict tsetse distributions and have been hindered by limited field survey data. Methodology/Results We developed an Agent-Based Model (ABM) to provide timing and location information for tsetse fly control without presence/absence training data. The model is driven by daily remotely-sensed environment data. The model provides a flexible tool linking environmental changes with individual biology to analyze tsetse control methods such as aerial insecticide spraying, wild animal control, releasing irradiated sterile tsetse males, and land use and cover modification. Significance This is a bottom-up process-based model with freely available data as inputs that can be easily transferred to a new area. The tsetse population simulation more closely approximates real conditions than those using traditional statistical models making it a useful tool in tsetse fly control planning.