# PRRS Outbreak Prediction via Deep Switching Auto-Regressive Factorization Modeling

arXiv: https://arxiv.org/abs/2110.03147 · HTML: https://mlsanity.com/paper/2110.03147/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 21 references that are in the map.
- First posted on arXiv 7 Oct 2021.

## Builds on (most cited first, 8 of 8)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [Variational Inference with Normalizing Flows](https://mlsanity.com/paper/1505.05770/) (2015)
- [Generating Sentences from a Continuous Space](https://mlsanity.com/paper/1511.06349/) (2015)
- [Tree-Structured Recurrent Switching Linear Dynamical Systems for Multi-Scale Modeling](https://mlsanity.com/paper/1811.12386/) (2018)
- [Switching Linear Dynamics for Variational Bayes Filtering](https://mlsanity.com/paper/1905.12434/) (2019)
- [Deep Switching Auto-Regressive Factorization:Application to Time Series Forecasting](https://mlsanity.com/paper/2009.05135/) (2020)
- [Generation of swine movement network and analysis of efficient mitigation strategies for African swine fever virus](https://mlsanity.com/paper/1911.04447/) (2019)

## Built on by (most cited first, 1 of 1)

- [Equivariant Deep Dynamical Model for Motion Prediction](https://mlsanity.com/paper/2111.01892/) (2021)

## Abstract (arXiv)

We propose an epidemic analysis framework for the outbreak prediction in the livestock industry, focusing on the study of the most costly and viral infectious disease in the swine industry -- the PRRS virus. Using this framework, we can predict the PRRS outbreak in all farms of a swine production system by capturing the spatio-temporal dynamics of infection transmission based on the intra-farm pig-level virus transmission dynamics, and inter-farm pig shipment network. We simulate a PRRS infection epidemic based on the shipment network and the SEIR epidemic model using the statistics extracted from real data provided by the swine industry. We develop a hierarchical factorized deep generative model that approximates high dimensional data by a product between time-dependent weights and spatially dependent low dimensional factors to perform per farm time series prediction. The prediction results demonstrate the ability of the model in forecasting the virus spread progression with average error of NRMSE = 2.5%.