Escherichia coli-associated diseases continue to be a significant concern in swine health. Disease may occur when E. coli strains harbor specific virulence factors. The combination of virulence factors detected in a single isolate forms a virotype, which can be grouped into pathotypes based on mechanisms of pathogenicity. This study compiled E. coli polymerase chain reaction (PCR) sample-level data from six major U.S. veterinary diagnostic laboratories (VDLs) to evaluate trends in E. coli virulence factors, virotypes, and pathotypes from porcine cases. The dataset contained diagnostic results for E. coli virulence factors detected by PCR, including attachment genes (fimbriae and adhesins) and toxin genes (heat-labile, heat-stable, and Shiga-like toxins). Between 2008 and 2025, major shifts were observed in the detection of key E. coli virulence factors. Temporal trend analysis using the Mann-Kendall test showed increasing detection of the virulence factors F18, F41, STa, Stx2, and Stx2e, while F4, AIDA, Paa, and EAST1 decreased over time. Similar patterns were observed among isolates classified as potentially pathogenic, with increases in F18, F41, STa, Stx2, and Stx2e and decreases in F4, AIDA, Paa, and EAST1 detection. Among pathotypes, Hybrid enterotoxigenic E. coli (ETEC)/STEC had an increase in detection over time, whereas ETEC, STEC, and isolates classified as not potentially pathogenic decreased. This large-scale diagnostic dataset reveals a notable shift in the E. coli pathogenic profile in swine over the past decade, characterized by an increasing detection of the hybrid ETEC/STEC pathotype and evolving virotype compositions.

Influenza A virus (IAV) in swine is a major respiratory pathogen with global significance. This study aimed to characterize the macroepidemiological patterns of IAV detection using reverse transcription real-time polymerase chain reaction (RT-rtPCR) assays, including subtype identification, in samples submitted between January 2004 and December 2024 to veterinary diagnostic laboratories (VDLs) participating in the Swine Disease Reporting System (SDRS). A secondary objective was establishing an IAV monitoring capability to inform stakeholders of weekly changes in IAV detection patterns. Of the 372,659 samples submitted, 31% tested positive for IAV RNA via RT-rtPCR. The most frequent sample types were oral fluids (44.1%) and lung tissue (38.7%). Submissions from the wean-to-market category had a higher positivity rate (34.4%) than those from the adult/sow farm category (26.9%). IAV detection followed a seasonal pattern, with peaks in spring and fall and lower positivity rates in summer. Of the total of 118,490 samples tested for IAV subtyping using RT-rtPCR, the most frequently detected subtypes were H1N1 (33.1%), H3N2 (25.5%), H1N2 (24.3%), H3N1 (0.2%), mixed subtypes (5.4%), and partial subtype detection (11.5%). Mixed IAV subtypes were detected in individual samples—including lung tissue, nasal swabs, and bronchoalveolar lavage—indicating co-infection with two or more IAV strains. For IAV forecasting, a combined model using dynamic regression and a neural network outperformed individual models in 2023, achieving the lowest root mean square error (RMSE) and an improved overall skill score. This study highlights the importance of using laboratory submission data for IAV surveillance and macroepidemiological analysis. The findings provide valuable insights into IAV dynamics and highlight the need for standardized monitoring systems in VDLs to enhance understanding of IAV in swine populations across the United States.

Porcine reproductive and respiratory syndrome virus (PRRSV) is the most important swine pathogen affecting the United States of America (USA), leading to significant economic losses. Despite advances in diagnostic testing, there remains a gap in understanding the genetic evolution of PRRSV, especially in tracking the emergence of novel sequences and their spread across different regions and production stages. This research addresses this gap by developing a systematic methodology for directly collecting and analyzing PRRSV ORF5 sequences from veterinary diagnostic laboratories. The study aimed to identify trends among collected sequences and emerging PRRSV sequences by integrating nucleotide sequence data with metadata, providing critical insights into their geographic distribution, collected specimens, swine age groups, lineages, variants, and restriction fragment length polymorphism (RFLP) patterns. As of December 2024, the database housed 115,643 PRRSV ORF5 sequences. Sublineages 1B, 1A, 1H, and 1C.5 were the major wild-type PRRSV sequences detected over time, whereas vaccine-like strains comprised mostly of sublineages 5A and 8A. A novel sequence detection system was implemented, categorizing sequences based on similarity thresholds, ambiguities, and length criteria, identifying 167 novel sequences for the period between 2010 and 2024, whereas only three had continued detection in the field over time, forming clusters of detection. The analysis of these novel sequences highlighted significant trends, including the dominance of grow-finish animals in sequence origin and the high number of detections of sublineage 5A. Production sites located in states with the largest swine inventory have contributed to the most frequent detection of new PRRSV strains. Additionally, the development of a web-based tool provides end users with the capability to search sequences similar to their query sequence, providing macroepidemiological information and genetic sequence features to support PRRSV management and control. Real-time PRRSV sequencing data analysis informs producers and veterinarians of any upcoming novel sequences and trends of detection. The findings are intended to enhance current surveillance efforts and support more effective strategies for managing PRRSV outbreaks, ultimately safeguarding animal health, economic sustainability in the swine industry, and ultimately contributing to national food production sovereignty through pork-derived products.

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.

Porcine circoviruses (PCVs), including porcine circovirus 2 (PCV2) and porcine circovirus 3 (PCV3), have been associated with clinical syndromes in swine, resulting in significant economic losses. To better understand the epidemiology and clinical relevance of PCV2 and PCV3, this study analyzed a dataset comprising diagnostic data from six veterinary diagnostic laboratories (VDLs) in the United States of America. The data comprised of polymerase chain reaction (PCR) test results, sample type, and age group for PCV2 and PCV3 submissions from 2002-2023. Findings indicated a decrease in the percentage of PCV2-positive submissions after introducing a commercial PCV2 vaccine in 2006 and a resurgence in positivity after 2018, particularly in breeding herds, associated with an increased number of submissions using processing fluid samples. After its first report in the U.S. in 2016, PCV3 detection had an upward trend in the percentage of positive cases, peaking in spring 2023. PCV3 detection was more frequent in adult/sow farms, while PCV2 was more frequently detected in the wean-to-market category. An additional analysis used results from tissue diagnostic data from 2019-2023 from one VDL to associate PCR cycle threshold (Ct) values with the probability of confirming a PCV2 or PCV3 disease diagnosis confirmation. An interpretative PCR Ct cutoff for PCV2 and PCV3 diagnoses was assessed based on the logistic regression model associating Ct values with the presence of tissue lesions. The analysis considered only cases tested for PCV2 and PCV3 by PCR with tissue evaluations by diagnosticians. An interpretative Ct cutoff of 22.4 for PCV2 was associated with a high probability of confirming a diagnosis of PCV2 clinical disease through histopathology. For PCV3, the interpretative cutoff with the highest performance was 26.7. These findings contribute to the ongoing efforts to monitor and understand the clinical relevance of PCV2 and PCV3 PCR results, identifying potential disease challenges.

Every day, thousands of samples from diverse populations of animals are submitted to veterinary diagnostic laboratories (VDLs) for testing. Each VDL has its own laboratory information management system (LIMS), with processes and procedures to capture submission information, perform laboratory tests, define the boundaries of test results (i.e., positive or negative), and report results, in addition to internal business and accounting applications. Enormous quantities of data are accumulated and stored within VDL LIMSs. There is a need for platforms that allow VDLs to exchange and share portions of laboratory data using standardized, reliable, and sustainable information technology processes. Here we report concepts and applications for standardization and aggregation of data from swine submissions to multiple VDLs to detect and monitor porcine enteric coronaviruses by RT-PCR. Oral fluids, feces, and fecal swabs were the specimens submitted most frequently for enteric coronavirus testing. Statistical algorithms were used successfully to scan and monitor the overall and state-specific percentage of positive submissions. Major findings revealed a consistently recurrent seasonal pattern, with the highest percentage of positive submissions detected during December-February for porcine epidemic diarrhea virus, porcine deltacoronavirus, and transmissible gastroenteritis virus (TGEV). After 2014, very few submissions tested positive for TGEV. Monitoring VDL data proactively has the potential to signal and alert stakeholders early of significant changes from expected detection. We demonstrate the importance of, and applications for, data organized and aggregated by using LOINC and SNOMED CTs, as well as the use of customized messaging to allow inter-VDL exchange of information.

What is the Swine Disease Reporting System (SDRS)?SDRS includes multiple projects that aggregate data from participating veterinary diagnostic laboratories (VDLs) in the United States of America (USA), and reports the major findings to the swine industry.Our goal is to share information on endemic and emerging diseases affecting the swine population in the USA, assisting veterinarians and producers in making informed decisions on disease prevention, detection, and management.After aggregating information from participating VDLs and summarizing the data, we ask the input of our advisory group, which consists of veterinarians and producers across the USA swine industry.The intent is to provide an interpretation of the observed data, and summarize the implications to the industry.Major findings are also discussed in monthly podcasts.All SDRS reports and podcasts are available at www.fieldepi.org/SDRS.The SDRS projects are: Swine Health Information Center (SHIC)-funded Domestic Swine Disease Surveillance Program: collaborative project among multiple VDLs, with the goal to aggregate swine diagnostic data and report in an intuitive format (web dashboards and monthly PDF report), describing dynamics of pathogen detection by PCRbased assays over time, specimen, age group, and geographical area.

We developed a model to predict the cyclic pattern of porcine reproductive and respiratory syndrome virus (PRRSV) RNA detection by reverse-transcription real-time PCR (RT-rtPCR) from 4 major swine-centric veterinary diagnostic laboratories (VDLs) in the United States and to use historical data to forecast the upcoming year’s weekly percentage of positive submissions and issue outbreak signals when the pattern of detection was not as expected. Standardized submission data and test results were used. Historical data (2015–2017) composed of the weekly percentage of PCR-positive submissions were used to fit a cyclic robust regression model. The findings were used to forecast the expected weekly percentage of PCR-positive submissions, with a 95% confidence interval (CI), for 2018. During 2018, the proportion of PRRSV-positive submissions crossed 95% CI boundaries at week 2, 14–25, and 48. The relatively higher detection on week 2 and 48 were mostly from submissions containing samples from wean-to-market pigs, and for week 14–25 originated mostly from samples from adult/sow farms. There was a recurring yearly pattern of detection, wherein an increased proportion of PRRSV RNA detection in submissions originating from wean-to-finish farms was followed by increased detection in samples from adult/sow farms. Results from the model described herein confirm the seasonal cyclic pattern of PRRSV detection using test results consolidated from 4 VDLs. Wave crests occurred consistently during winter, and wave troughs occurred consistently during the summer months. Our model was able to correctly identify statistically significant outbreak signals in PRRSV RNA detection at 3 instances during 2018.

What is the Swine Disease Reporting System (SDRS)? SDRS includes multiple projects that aggregate data from participating veterinary diagnostic laboratories (VDLs) in the United States of America (USA), and reports the major findings to the swine industry. Our goal is to share information on endemic and emerging diseases affecting the swine population in the USA, assisting veterinarians and producers in making informed decisions on disease prevention, detection, and management. After aggregating information from participating VDLs and summarizing the data

What is the Swine Disease Reporting System (SDRS)?SDRS includes multiple projects that aggregate data from participating veterinary diagnostic laboratories (VDLs) in the United States of America (USA), and reports the major findings to the swine industry.Our goal is to share information on endemic and emerging diseases affecting the swine population in the USA, assisting veterinarians and producers in making informed decisions on disease prevention, detection, and management.After aggregating information from participating VDLs and summarizing the data, we ask the input of our advisory group, which consists of veterinarians and producers across the USA swine industry.The intent is to provide an interpretation of the observed data, and summarize the implications to the industry.Major findings are also discussed in monthly podcasts.All SDRS reports and podcasts are available at www.fieldepi.org/SDRS.The SDRS projects are: Swine Health Information Center (SHIC)-funded Domestic Swine Disease Surveillance Program: collaborative project among multiple VDLs, with the goal to aggregate swine diagnostic data and report in an intuitive format (web dashboards and monthly PDF report), describing dynamics of pathogen detection by PCRbased assays over time, specimen, age group, and geographical area.

Recommended Citation Trevisan, Giovani; Linhares, Daniel; Linhares, Leticia; Crim, Bret; Dubey, Poonam; Schwartz, Kent; Burrough, Eric; Main, Rodger; Thurn, Mary; Lages, Paulo; Vanderwaal, Kimberly; Perez, Andres; Torrison, Jerry; Henningson, Jamie; Herrman, Eric; Hanzlicek, Gregg; Raghavan, Ram; Marthaler, Douglas; Greseth, Jon; Clement, Travis; and Hennings, Jane C., "Swine Disease Reporting: Report #7" (2018). Swine Disease Reporting System. 7. https://lib.dr.iastate.edu/swinedisease_reports/7

Recommended Citation Trevisan, Giovani; Linhares, Daniel; Magalhaes, Edison; Linhares, Leticia; Crim, Bret; Dubey, Poonam; Schwartz, Kent; Burrough, Eric; Gauger, Philip; Main, Rodger; Thurn, Mary; Lages, Paulo; Corzo, Cesar; Torrison, Jerry; McGaughey, Rob; Herrman, Eric; Hanzlicek, Gregg; Henningson, Jamie; Greseth, Jon; Clement, Travis; and Hennings, Jane C., "Swine Disease Reporting: Report #22" (2019). Swine Disease Reporting System. 24. https://lib.dr.iastate.edu/swinedisease_reports/24

Recommended Citation Trevisan, Giovani; Magalhaes, Edison; Linhares, Leticia; Crim, Bret; Dubey, Poonam; Schwartz, Kent; Burrough, Eric; Gauger, Philip; Main, Rodger; Linhares, Daniel; Thurn, Mary; Lages, Paulo; Corzo, Cesar; Torrison, Jerry; McGaughey, Rob; Herrman, Eric; Hanzlicek, Gregg; Henningson, Jamie; Greseth, Jon; Clement, Travis; and Hennings, Jane C., "Swine Disease Reporting: Report #21" (2019). Swine Disease Reporting System. 25. https://lib.dr.iastate.edu/swinedisease_reports/25