The US is the world’s second largest pork producer and second largest meat exporter (The North American Meat Institute, 2019). The multi-site swine production system (ie pigs separation by pig type and age) allows for specialized housing and feed. However, this multi-site system requires frequent movement of animals between sites and leads to high pig density areas, which increase the risk of disease spread. Porcine Reproductive and Respiratory Syndrome (PRRS) is currently the most challenging and costly viral infectious disease in the US swine industry [2]. The PRRS virus has high variability due to virus mutation which challenges vaccine development and implementation [3]. The high cost of testing and vaccination as well as the financial damage after an outbreak highlights the need to develop predictive models that can help to identify farms at high risk of infection to support risk-based, more cost-effective, target interventions. Such models will allow for more efficient testing, vaccination and outbreak prevention. Currently, control of PRRS relies on a combination of biosecurity, surveillance (ie, testing), and vaccination. Testing in swine farms is conducted using serological and molecular tests that evaluate blood or oral fluids in live pigs or tissues in dead animals. Based on those testing activities the shedding (using PCR) and exposure (using ELISA) status of the herd can be determined and the herd can be classified in different categories [4]. For breeding herds (sow and nursery farms) there are four categories:(I) positive unstable,(II) positive stable,(III) provisional negative and (IV) negative. For growing herds (finishing herds) there are only positive or negative status. The challenge is that the untested farms have uncertain status and cannot easily be categorized into positive or negative. Some of the farm managers may decide to accept the risk of an outbreak rather than continuously test or implement strict biosecurity and vaccination protocols in their farms.

The aim of this study was to examine different machine learning models and explore diverse features to more effectively predict and promptly detect PRRS outbreaks. We do this based on location, pig movements, pig production parameters, weather information, and testing/diagnostic data of the farm. This prediction can support more efficient testing and mitigation strategies to reduce PRRS impact in the swine industry Among the three major farm types (sow, nursery, and finishing). We focus here in finishing farms, which have the lowest frequency of testing and lowest standards of immunization and biosecurity and could highly benefit of a system that helps to predict outbreaks and, more importantly, will contribute to reducing the burden for disease transmission through airborne or other pathways to breeding herds. Thus, this work examines multiple machine learning models for outbreak prediction and early detection in finishing farms using a combination of diagnostics, production, and pig trade data.