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Objective: Slaughterhouse data has recently been used to enhance animal disease surveillance in many countries, however has been largely underused for syndromic surveillance in the United States. We characterize spatiotemporal patterns and system dynamics of whole carcass swine condemnations in the US. We illustrate the value of data mining and machine learning approaches to more cost-effectively identify: emerging trends by condemnation reason, areas and time periods with higher than predicted condemnation rates, and regions or time periods with similar trends. Methods: Swine slaughter and condemnation data from 2005-2016 were obtained for slaughterhouses inspected by the Food Safety and Inspection Service (FSIS). Time series of condemnation rates by condemnation reason, type of pig, state and month were generated. Data time warping (DTW) and hierarchical clustering methods were used to identify states with similar patterns in the rate of condemnation cases by cause and type of pig. Spatiotemporal scan statistics were used to identify states and months with significantly higher number of condemnation cases than expected. Clusters were compared to historic infectious disease outbreaks in the swine industry. Results: Between 2005-2016, 1,109,300 whole swine carcasses were condemned. The top causes for condemnation were abscess/pyemia, septicemia, pneumonia, icterus, and peritonitis, respectively. DTW and cluster analysis revealed clear spatiotemporal patterns in the rate of condemnations, many with a strong seasonal component. Several clusters were detected in timeframes where widespread outbreaks had occurred. Conclusions: Timely evaluation of spatiotemporal patterns in swine condemnations may provide critical information in predicting disease outbreaks. Identification of spatiotemporal hot spots can direct investigation of primary on-farm risk factors contributing to condemnation. Risk mitigation through targeted decision-making and improved management practices can minimize carcass condemnations and animal losses, improving economic efficiency, profitability and sustainability of the US swine industry 

Livestock industry is daily producing large amounts of multi-scale data (pathogen-, animal-, site-, system-, regional- level) from different sources such as diagnostic laboratories, trade and production records, management and environmental monitoring systems; however, all these data are still presented and used separately and are largely infra-utilized to timely (i.e., near real-time) inform livestock health decisions. Recent advances in the automation of data capture, standardization, multi-scale integration and sharing/communication (i.e. The Internet Of Things) as well as in the development of novel data mining analytical and visualization capabilities specifically adapted to the livestock industry are dramatically changing this paradigm. As a result, we expect vertical advances in the way we prevent and manage livestock diseases both locally and globally. Our team at the Center for Animal Disease Modeling and Surveillance (CADMS), in collaboration with researchers at Iowa State University and industry leaders at Boehringer Ingelheim and GlobalVetLINK have been working in an exceptional research-industry partnership to develop key data connections and novel Big Data capabilities within the Disease BioPortal (http://bioportal.ucdavis.edu/). This web-based platform includes automation of diagnostic interpretations and facilitates the combined analysis of health, production and trade data using novel space-time-genomic visualization and data mining tools. Access to confidential databases is individually granted with different levels of secure access, visualization and editing capabilities for participating producers, labs, veterinarians and other stakeholders. Each user can create and share customized dashboards and reports to inform risk-based, more cost-effective, decisions at site, system or regional level. Here we will provide practical examples of applications in the swine, poultry and aquaculture industries. We hope to contribute to the more coordinated and effective prevention and control of infectious diseases locally and globally.