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We propose an epidemic analysis framework for the outbreak prediction in thelivestock industry, focusing on the study of the most costly and viralinfectious disease in the swine industry -- the PRRS virus. Using thisframework, we can predict the PRRS outbreak in all farms of a swine productionsystem by capturing the spatio-temporal dynamics of infection transmissionbased on the intra-farm pig-level virus transmission dynamics, and inter-farmpig shipment network. We simulate a PRRS infection epidemic based on theshipment network and the SEIR epidemic model using the statistics extractedfrom real data provided by the swine industry. We develop a hierarchicalfactorized deep generative model that approximates high dimensional data by aproduct between time-dependent weights and spatially dependent low dimensionalfactors to perform per farm time series prediction. The prediction resultsdemonstrate the ability of the model in forecasting the virus spreadprogression with average error of NRMSE = 2.5\%.

PRRS Outbreak Prediction via Deep Switching Auto-Regressive Factorization Modeling