RESUMO
Understanding regional disease risk is critical for swine disease prevention and control. Since 2011, the Morrison Swine Health Monitoring Project (MSHMP) has strengthened partnerships among practitioners and producers to report health events (e.g., porcine reproductive and respiratory syndrome (PRRS) virus outbreaks) at the U.S. national level. Using MSHMP data and PRRS as an example, an early regional occurrence warning tool to provide near-real-time alerts was developed. MSHMP-participating production systems were invited to enroll. An algorithm was developed to calculate the number of PRRSV-positive sites near each enrolled site, determined from site-specific radius. The radius was determined in three steps. First, an initial radius of 25 miles was set for sites in pig-dense states and 50 miles for others. Secondly, four variables were generated to account for the sites within the initial radius: A) Total number of PRRSV-positive sites; B) Number of PRRSV-positive sites from other production systems; C) Total number of sites enrolled, and D) Total number of sites monitored by MSHMP. Subsequently, the reporting radius was automatically increased when confidentiality concerns arose. Results were compiled into system-specific reports and shared weekly with each participant. Reports have been shared since May 9, 2023, representing 178 breeding sites, comprising approximately 565â¯K sows. Examples of how participants use these reports include adjusting biosecurity programs, frequency of supply introduction, and transportation routes. The early occurrence warning tool developed in this study enhances producers' ability to communicate effectively and respond quickly to health threats, mitigating regional disease while preparing for foreign disease introductions.
RESUMO
This study evaluated the use of endemic enteric coronaviruses polymerase chain reaction (PCR)-negative testing results as an alternative approach to detect the emergence of animal health threats with similar clinical diseases presentation. This retrospective study, conducted in the United States, used PCR-negative testing results from porcine samples tested at six veterinary diagnostic laboratories. As a proof of concept, the database was first searched for transmissible gastroenteritis virus (TGEV) negative submissions between January 1st, 2010, through April 29th, 2013, when the first porcine epidemic diarrhea virus (PEDV) case was diagnosed. Secondly, TGEV- and PEDV-negative submissions were used to detect the porcine delta coronavirus (PDCoV) emergence in 2014. Lastly, encountered best detection algorithms were implemented to prospectively monitor the 2023 enteric coronavirus-negative submissions. Time series (weekly TGEV-negative counts) and Seasonal Autoregressive-Integrated Moving-Average (SARIMA) were used to control for outliers, trends, and seasonality. The SARIMA's fitted and residuals were then subjected to anomaly detection algorithms (EARS, EWMA, CUSUM, Farrington) to identify alarms, defined as weeks of higher TGEV-negativity than what was predicted by models preceding the PEDV emergence. The best-performing detection algorithms had the lowest false alarms (number of alarms detected during the baseline) and highest time to detect (number of weeks between the first alarm and PEDV emergence). The best-performing detection algorithms were CUSUM, EWMA, and Farrington flexible using SARIMA fitted values, having a lower false alarm rate and identified alarms 4 to 17 weeks before PEDV and PDCoV emergences. No alarms were identified in the 2023 enteric negative testing results. The negative-based monitoring system functioned in the case of PEDV propagating epidemic and in the presence of a concurrent propagating epidemic with the PDCoV emergence. It demonstrated its applicability as an additional tool for diagnostic data monitoring of emergent pathogens having similar clinical disease as the monitored endemic pathogens.