Speaker
Description
Schistosomiasis is driven by complex transmission dynamics influenced by human behaviour and environmental factors. While existing individual-based models (IBMs) have advanced our understanding of the disease, they often lack a spatial component, limiting their ability to inform geographically targeted interventions. In this work, we present a spatially explicit IBM to better simulate the transmission dynamics of Schistosoma mansoni in our study area in rural Uganda.
Our approach integrates geospatial data on household and water site locations to construct a bipartite network. This network forms the foundation for our model, explicitly linking humans to water sites. Biomphalaria snail dynamics are also explicitly modelled at the site level. By doing so, our model can simulate how factors like human mobility and the spatial arrangement of water sites and households affect the spread of infection.
This model is a powerful tool for evaluating the efficacy of different control strategies. We use it to explore how targeted interventions, such as snail control at high-risk water sites or removing access to high-risk water sites altogether, compare to untargeted strategies. Ultimately, this work aims to demonstrate the importance of spatial modelling in identifying water sites of higher risk and optimising resource allocation for more effective control programmes.
