SHIC Funded Study Utilizes Endemic Disease Data for Detecting Emerging Diseases

A Swine Health Information Center-funded study has investigated if an increase in negative test results for endemic pathogens could be utilized as an early warning signal for emerging diseases. Led by Dr. Giovani Trevisan at Iowa State University, a team of scientists from six veterinary diagnostic labs evaluated different surveillance models using endemic enteric coronavirus PCR-negative test results to predict novel enteric coronavirus emergence. As an alternative approach to detect a new animal health threat causing similar clinical signs, the researchers determined that the TGEV negative-based monitoring system functioned well for the 2013 PEDV epidemic. Specifically, results demonstrated that emerging disease alarms could be identified four weeks earlier than the first official diagnosis of PEDV in the US.

Unexpected increases in negative test results can serve as a warning system to alert veterinarians and producers of an emerging swine disease. Read the full report, published by PLOS ONE and posted on the SHIC website, here.

Routine monitoring of laboratory submissions for shifts in test results can reveal trends in pathogen activity, seasonality, and provide evidence of pathogen emergence. Pathogen monitoring and surveillance systems are routine measures for veterinary medicine and recognized as tools for efficient disease control and prevention in populations. Monitoring and surveillance systems’ primary goal is the timely and accurate identification of emerging and re-emerging pathogens with few or no false alarms. Systems include general passive surveillance, routine laboratory submissions, animal movement inspections, livestock markets, and other secondary data sources.

In this SHIC-funded study, a monitoring system was proposed using negative results from enteric coronavirus PCR testing in the US, where the primary goal was the early identification of a sustained increase in negative submissions that indicated a novel pathogen had emerged. Data used in this study was retrieved from the Swine Disease Reporting System, which is an ongoing monitoring project that aggregates producer anonymized diagnostic test results from six participating US VDLs.

 

Real diagnostic data on TGEV PCR-negative results between 2010 and 2013 were used for a negative results-based monitoring system for enteric coronaviruses during the time of PEDV emergence in 2013. TGEV and PEDV PCR-negative results between 2009 and 2014 were used to monitor the PDCoV emergence in 2014. The same methodology was thereafter applied to monitor enteric coronavirus negative results from 2023. The observation unit in the study was a porcine diagnostic submission shared with the SDRS database, which was searched for submissions between January 2009 and October 2023. The total number of negative and positive PCR submissions were calculated weekly, using the date received at the VDL as the aggregate factor. This step was repeated for TGEV, PEDV, and PDCoV PCR-negative and positive results.

 

Seasonal Autoregressive-Integrated Moving-Average (SARIMA) algorithms were employed to smooth the time series of negative submissions. The purpose of the smoothing process was to control for outliers, abrupt changes, trends, and seasonality to prevent false alarms from being triggered by anomalies that are not indicative of a true emerging disease. The SARIMA’s fitted and residual values were subjected to four anomaly detection algorithms, EARS, CUSUM, EWMA and Farrington Flexible. These algorithms are statistical control charts that can be used to detect sustained increases and decreases for the negative results monitoring while controlling for seasonality and time trends.

 

In the study, all three best-performing algorithms (CUSUM, EWMA, Farrington flexible) resulted in alarms four weeks earlier than the first disease diagnosis of PEDV in the US. These alarms were considered true early alarms of PEDV emergence given that epidemiological investigations reported the first PCR-confirmed PEDV infection on April 15, 2013, but that the virus had likely been circulating in the US for a few weeks prior. These results showed that the use of negative monitoring accurately identified the sustained increase in TGEV negative submissions aligned with the emergence of PEDV in 2013. Although PDCoV emergence was lower in magnitude than PEDV, alarms were identified due to increases in TGEV and PEDV PCR-negative test results. The monitoring system revealed no alarms for 2023 negative PCR enteric data.

 

Ongoing monitoring of animal health parameters and routine monitoring of laboratory submissions provides value in revealing trends and providing early warnings. Early detection of novel pathogen emergence, even without immediate identification of the specific pathogen, will provide stakeholders with opportunities for proactive responses, biocontainment, resource allocation, diagnostics, and awareness.

The Swine Health Information Center, launched in 2015 with Pork Checkoff funding, protects and enhances the health of the US swine herd by minimizing the impact of emerging disease threats through preparedness, coordinated communications, global disease monitoring, analysis of swine health data, and targeted research investments. As a conduit of information and research, SHIC encourages sharing of its publications and research. Forward, reprint, and quote SHIC material freely. For more information, visit http://www.swinehealth.org or contact Dr. Megan Niederwerder at [email protected] or Dr. Lisa Becton at [email protected].