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Pathogen detection at state-level may improve disease monitoring

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Monitoring pathogen detection at a state-specific level is insightful to understand the pattern of pathogen detection better.

Infectious diseases can severely limit the health, welfare and productivity of populations of animals. Livestock veterinarians submit thousands of samples daily to veterinary diagnostic laboratories for disease diagnosis, pathogen monitoring and surveillance.

Aggregated historical laboratory data are increasingly valued by clinicians and decision-makers to identify changes in the health status of various animal populations over time and geographical space. The value of this historical information is enhanced by visualization of trends of agent detection, which helps focus time and resources on the most significant pathogens and fosters more effective communication between livestock producers, veterinarians and veterinary diagnostic laboratory professionals.

For years our team has collaborated to develop an inter-VDL data communication and collation project named Swine Disease Reporting System.1, 2 Altogether aggregated submission information, polymerase chain reaction test and testing results data for more than a million submissions submitted for testing at participant VDLs for comprising five porcine endemic pathogens (porcine reproductive and respiratory syndrome virus, porcine epidemic diarrhea virus, porcine deltacoronavirus, M. hyopneumoniae and transmissible gastroenteritis virus), in the United States, were included into the SDRS database.

Recent advances in data visualization tools and monitoring capabilities allow quick, efficient and often real-time scanning and analysis of databases to detect and inform changes in patterns of agent detection to guide animal health interventions.

During June, the SDRS project brought online a new tool to inform significant changes in pathogen detection at the state-specific level. Monthly changes in the percentage of PCR-positive submissions were accessed by using smoothing models to forecast results for the next 12 months. The predicted values of percentage-positive results by PCR (by a pathogen, month and state), were subtracted to the respective observed values, generating residue values.

After that, the residues were scanned by an exponential weighted moving average model to monitor and inform the changes from the expected baseline. The baseline and the monitoring were constructed based on each state’s data, and there is no comparison across states. The project included the states where the SDRS participant VDLs are located, and those having a swine inventory equal to or greater than 2 million pigs.3

The EWMA findings are reported as changes in standard deviations from the expected baseline. Changes from baseline are reported as a) no change when results were within 2 STD from baseline; b) changes between 2-3 STD from baseline; c) changes of at least 3 STD above or below the baseline. Information for each state regarding the change from baseline, the number of total, positive submissions and percent of positive results were recovered from the models and transferred to Microsoft Power Business Intelligence for geographic visualization. The updated dashboards can be accessed here

For 2020, the detection of PRRSV was above 3 STD during June for Minnesota, South Dakota and Indiana (Figure 1). PEDV detection was above 3 STD during February and March in Nebraska (Figure 2).

Iowa State University nhf-isu-090120-figure1.jpg

Iowa State University nhf-isu-090120-figure2.jpg

Historical data generate useful information, but the real value lies in the ability to monitor this information in near-real-time and thus inform stakeholders for changes in the patterns of pathogen detection. Monitoring pathogen detection at a state-specific level is insightful to producers, veterinarians and scientists to understand the pattern of pathogen detection better. It can be used for many applications, including warning signals or indicators, to justify reinforcement of biosecurity and biocontainment practices, and to support regional disease control programs.

References

1. Trevisan G; Linhares LCM; Crim B; Dubey P; Schwartz KJ; Burrough ER; Main RG; Sundberg P; Thurn M; Lages PTF; Corzo CA; Torrison J; Henningson J; Herrman E; Hanzlicek GA; Raghavan R; Marthaler D; Greseth J; Clement T; Christopher-Hennings J; Linhares DCL. Macroepidemiological aspects of porcine reproductive and respiratory syndrome virus detection by major United States veterinary diagnostic laboratories over time, age group, and specimen. PLoS One. 2019;14(10):e0223544.

2. Trevisan G; Linhares LCM; Crim B; Dubey P; Schwartz KJ; Burrough ER; Wang C; Main RG; Sundberg P; Thurn M; Lages PTF; Corzo CA; Torrison J; Henningson J; Herrman E; Hanzlicek GA; Raghavan R; Marthaler D; Greseth J; Clement T; Christopher-Hennings J; Muscatello D; Linhares DCL. Prediction of seasonal patterns of porcine reproductive and respiratory syndrome virus RNA detection in the U.S. swine industry. J Vet Diagn Invest. 2020:1040638720912406.

3. NASS Quarterly Hogs and Pigs.

Sources: Giovani Trevisan, Edison Magalhães, Rodger Main, Jerry Torrison, Jane Christopher-Hennings, Jamie Henningson and Daniel C.L. Linhares, who are solely responsible for the information provided, and wholly own the information. Informa Business Media and all its subsidiaries are not responsible for any of the content contained in this information asset.
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