An agent-based model of tsetse fly response to seasonal climatic drivers: Assessing the impact on sleeping sickness transmission rates

Background: This paper presents the development of an agent-based model (ABM) to incorporate climatic drivers which affect tsetse fly (G. m. morsitans) population dynamics, and ultimately disease transmission. The model was used to gain a greater understanding of how tsetse populations fluctuate seasonally, and investigate any response observed in Trypanosoma brucei rhodesiense human African trypanosomiasis (rHAT) disease transmission, with a view to gaining a greater understanding of disease dynamics. Such an understanding is essential for the development of appropriate, well-targeted mitigation strategies in the future. Methods: The ABM was developed to model rHAT incidence at a fine spatial scale along a 75 km transect in the Luangwa Valley, Zambia. The model incorporates climatic factors that affect pupal mortality, pupal development, birth rate, and death rate. In combination with fine scale demographic data such as ethnicity, age and gender for the human population in the region, as well as an animal census and a sample of daily routines, we create a detailed, plausible simulation model to explore tsetse population and disease transmission dynamics. Results: The seasonally-driven model suggests that the number of infections reported annually in the simulation is likely to be a reasonable representation of reality, taking into account the high levels of under-detection observed. Similar infection rates were observed in human (0.355 per 1000 person-years (SE = 0.013)), and cattle (0.281 per 1000 cattle-years (SE = 0.025)) populations, likely due to the sparsity of cattle close to the tsetse interface. The model suggests that immigrant tribes and school children are at greatest risk of infection, a result that derives from the bottom-up nature of the ABM and conditioning on multiple constraints. This result could not be inferred using alternative population-level modelling approaches. Conclusions: In producing a model which models the tsetse population at a very fine resolution, we were able to analyse and evaluate specific elements of the output, such as pupal development and the progression of the teneral population, allowing the development of our understanding of the tsetse population as a whole. This is an important step in the production of a more accurate transmission model for rHAT which can, in turn, help us to gain a greater understanding of the transmission system as a whole. Author summary: African trypanosomiasis is a parasitic disease which affects humans and other animals in 36 sub-Saharan African countries. The disease is transmitted by the tsetse fly, and the human form of the diseases is known as sleeping sickness. In an attempt to improve our understanding of the mechanisms which contribute to sleeping sickness transmission, a detailed, seasonally driven model of the tsetse fly has been produced, with the theory that a greater understanding of the disease vector’s life cycle will allow developments in our knowledge of disease transmission. The model incorporates previously developed spatial data for the Luangwa Valley case study, along with demographic data for its inhabitants. Tsetse and potential human and animal hosts are modelled at the individual level, allowing each contact and infection to be recorded through time. Through modelling at a fine-scale, we can incorporate detailed mechanisms for tsetse birth, feeding, reproduction and death, while considering what demographics, and which locations, have a heightened risk of disease.