A new real-time, county- and farm-type stratified spatial disease surveillance system for swine pathogens has been developed to detect diseases at a regional level. The study, funded by the Swine Health Information Center, sought to evaluate a surveillance system that integrates diagnostic and animal movement data to track pathogen activity and spread at the site and regional levels. Led by Dr. Gustavo Silva at Iowa State University and his PhD Candidate Swaminathan Jayaraman, a comprehensive, data driven approach to emerging disease management was developed integrating data from 3,084 sites across 18 US states representing 10 major production systems. The newly developed system detects emerging diseases and provides weekly infection risk forecasts to support targeted disease control efforts. Producers and veterinarians who are interested in joining this surveillance system as participants should contact Dr. Silva at gustavos@iastate.edu.
Find the industry summary for Swine Health Information Center project #24-029 here.
By integrating diagnostic data, animal movement information, and site location data across >3,000 sites, the analysis revealed critical insights into disease transmission dynamics. Results determined that farm type is the primary determinant of porcine reproductive and respiratory syndrome virus transmission risk, accounting for 81% of the total variance. Two complementary Bayesian spatial surveillance models were used to characterize PRRSV transmission dynamics at the county and sites levels. Models incorporated farm-type stratification to account for different breeding herds, growing herds and other herds in addition to spatial proximity within a 25 km radius.
Baseline infection probabilities for PRRSV were 73% for growing herds, 70% for breeding herds, and 58% for other herds. Movement networks were found to be the second most significant factor at 16%, with geographic proximity explaining only 3%. This finding challenges the conventional emphasis on geographic clustering and highlights the importance of production phase in disease spread.
Overall, the system’s forecasting capabilities achieved accuracy of over 83% for county level models and over 84% for site level models for a one-week horizon. Although predictive accuracy decreased for longer forecasting periods, the system’s ability to provide timely, actionable data remains a powerful tool for veterinarians and producers.
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