Minisymposium Presentation

African Swine Fever (ASF) is a highly contagious and deadly viral disease infecting domestic and feral swine populations in Africa and Asia and more recently in the Europe, South America, and Caribbean. ASF has devastating impacts on swine industries in the affected countries. This study proposes to develop a ASF farmer vulnerability risk index for rural swine farms that integrates an array of potential environmental factors (e.g., domestic and feral swine population densities, precipitation, temperature, vegetation), geographic and social factors and seasonality to assess and predict regions where outbreaks are more likely to occur.   The approach is data agnostic, leveraging a broad range of available data with the goal of identifying discriminating features in a data-driven manner.   A feature indexing approach is used to construct labeled training data from historical outbreaks to train a machine learning model to produce a spatial risk index.   Sparse optimization tools are employed to identify the salient features most useful for predictive modeling. The resultant risk index can guide surveillance and preventive strategies, while also outlining limitations related to data granularity and model generalizability. This study incorporates integrating diverse data set to identify areas of risk to potentially inform mitigating the spread of ASF.