Simulating sleeping sickness : a multi-host agent-based model

Human African Trypanosomiasis, also known as sleeping sickness, is a neglected tropical disease (NTD), a group of the most common conditions which affect the poorest 500 million people living in sub-Saharan Africa. The disease is vector-borne, parasitic, and transmitted through the bite of the tsetse fly. The Rhodesian form of the disease is prevalent in both human and animal hosts, and, in its animal form, is a causal factor in Animal African Trypanosomiasis (AAT), or nagana. Characterised as an epidemiological enigma due to its ability to self-sustain at low background levels between periodic epidemics, the disease is hard to control due to under-reporting, invasive and often ineective diagnosis techniques, and out-dated treatments. With a focus on preventative measures to mitigate the disease, this research presents a novel, detailed agent-based modelling (ABM) approach to aid understanding of rHAT transmission at a ne resolution in a 75 km case study of Eastern Province, Zambia. Within the research, ABM and A* pathfinding techniques were combined to allow the generation of human movement routes between homes and vital resources in a rural setting, given commonly available geospatial datasets on population distribution, land cover and landscape resources. A multi-host ABM was created using these paths, census and activity data for human, cattle, and other domestic animal inhabitants, and a detailed tsetse fly model component. Finally, seasonal fluctuations were incorporated into the model, before subjecting the system to a realistic perturbation in the form of population growth - a factor which could be key in the future transmission landscape of the region as the pattern of host-vector contact varies. The research has shown that, for this case study, statistically significant walk times to resources can be simulated along paths generated largely from remotely sensed information, when compared to real world data. Through the identification of possible spatial, demographic and behavioural characteristics which may have differing implications for rHAT risk in the region, the ABM produced output that could not be produced readily through compartmentalised approaches. The model output identified that immigrant tribes and school-age children were the most at risk groups for acquiring the disease, while also highlighting spatial heterogeneity in the transmission of the disease, and areas of high connectivity between vector and host. Realistic population growth of both human and domestic animals resulted in a dramatic change in the pattern of infections, with adult females and short, high frequency resource trips creating the most vulnerability. Ultimately, ABMs provide an alternative way of thinking about NTDs, providing an attractive solution to the investigation of local-scale questions. The ABM can be used as a tool for scenario testing at an appropriate spatial scale to allow the design of logistically feasible mitigation strategies suggested by model output. This is of particular importance where resources are limited and management strategies are often pushed to the local scale.