Project Description:
Responding and adapting to the impacts of the 2020 Covid-19 outbreak will be a defining feature of much future research. One key aspect will be better understanding the ecological and sociological processes that drive pathogen spill-overs from animals to people, and better defining this interface will help create less risky future landscapes. Despite their public health relevance, many important zoonotic diseases have not been systematically studied from a quantitative perspective. There is therefore an urgent need for a more interdisciplinary approach integrating computational modelling, ecology and socio-economic factors in more a holistic understanding of disease dynamics:
Year 1: The student will use geospatial data, alongside literature resources, to capture spatial variation in three critical zoonotic risk pathways (hunting, agricultural practices and livestock farming) and combine these elements to reveal the relative impacts of different land-use types on disease outbreak risk, for a set of zoonoses.
Year 2: The student will then explore counterfactual scenarios, undertaking cutting-edge statistical analyses to determine how following different socioeconomic development pathways, with contrasting human demography and land-use, might change the relative risk of spill-overs.
Year 3: Finally, the student will collate data on different strands of vulnerability (e.g. access to hospitals, governance, housing quality) at fine spatial scales. They will then determine how these vulnerabilities are changing over time to identify current and future hotspots of outbreak risk. Finally, using all the data, they will run simple spatial, compartment models to determine the chance of infected people contacting large urban settlements, across all possible scenarios.
Policy Impact of Research:
The overall aim of this work is to better define the zoonotic spill-over risk interface to create next-generation models of global outbreak risk. These models will be run for wide set of zoonotic diseases which will be able to inform future high-level policy decisions at a national level.
