Macroepidemiological trends of Influenza A virus detection through reverse transcription real-time polymerase chain reaction (RT-rtPCR) in porcine samples in the United States over the last 20 years
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 sampl...
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Frontiers Media S.A.
2025-04-01
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| Series: | Frontiers in Veterinary Science |
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| author | Daniel C. A. Moraes Guilherme A. Cezar Edison S. Magalhães Rafael R. Nicolino Kinath Rupasinghe Srijita Chandra Gustavo S. Silva Marcelo N. Almeida Bret Crim Eric R. Burrough Phillip C. Gauger Darin Madson Joseph Thomas Michael A. Zeller Rodger Main Mary Thurn Paulo Lages Cezar A. Corzo Mattew Sturos Hemant Naikare Rob McGaughey Franco Matias Ferreyra Jamie Retallick Jordan Gebhardt Sara McReynolds Jon Greseth Darren Kersey Travis Clement Angela Pillatzki Jane Christopher-Hennings Beth S. Thompson Melanie Prarat Dennis Summers Craig Bowen Joseph Boyle Kenitra Hendrix James Lyons Kelli Werling Andreia G. Arruda Mark Schwartz Mark Schwartz Paul Yeske Deborah Murray Brigitte Mason Peter Schneider Samuel Copeland Luc Dufresne Daniel Boykin Corrine Fruge William Hollis Rebecca C. Robbins Thomas Petznick Kurt Kuecker Lauren Glowzenski Megan Niederwerder Daniel C. L. Linhares Giovani Trevisan |
| author_facet | Daniel C. A. Moraes Guilherme A. Cezar Edison S. Magalhães Rafael R. Nicolino Kinath Rupasinghe Srijita Chandra Gustavo S. Silva Marcelo N. Almeida Bret Crim Eric R. Burrough Phillip C. Gauger Darin Madson Joseph Thomas Michael A. Zeller Rodger Main Mary Thurn Paulo Lages Cezar A. Corzo Mattew Sturos Hemant Naikare Rob McGaughey Franco Matias Ferreyra Jamie Retallick Jordan Gebhardt Sara McReynolds Jon Greseth Darren Kersey Travis Clement Angela Pillatzki Jane Christopher-Hennings Beth S. Thompson Melanie Prarat Dennis Summers Craig Bowen Joseph Boyle Kenitra Hendrix James Lyons Kelli Werling Andreia G. Arruda Mark Schwartz Mark Schwartz Paul Yeske Deborah Murray Brigitte Mason Peter Schneider Samuel Copeland Luc Dufresne Daniel Boykin Corrine Fruge William Hollis Rebecca C. Robbins Thomas Petznick Kurt Kuecker Lauren Glowzenski Megan Niederwerder Daniel C. L. Linhares Giovani Trevisan |
| author_sort | Daniel C. A. Moraes |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-e1203a2e7d37436bbb8cf1d7b1d7ed62 |
| institution | DOAJ |
| issn | 2297-1769 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Veterinary Science |
| spelling | doaj-art-e1203a2e7d37436bbb8cf1d7b1d7ed622025-08-20T03:14:12ZengFrontiers Media S.A.Frontiers in Veterinary Science2297-17692025-04-011210.3389/fvets.2025.15722371572237Macroepidemiological trends of Influenza A virus detection through reverse transcription real-time polymerase chain reaction (RT-rtPCR) in porcine samples in the United States over the last 20 yearsDaniel C. A. Moraes0Guilherme A. Cezar1Edison S. Magalhães2Rafael R. Nicolino3Kinath Rupasinghe4Srijita Chandra5Gustavo S. Silva6Marcelo N. Almeida7Bret Crim8Eric R. Burrough9Phillip C. Gauger10Darin Madson11Joseph Thomas12Michael A. Zeller13Rodger Main14Mary Thurn15Paulo Lages16Cezar A. Corzo17Mattew Sturos18Hemant Naikare19Rob McGaughey20Franco Matias Ferreyra21Jamie Retallick22Jordan Gebhardt23Sara McReynolds24Jon Greseth25Darren Kersey26Travis Clement27Angela Pillatzki28Jane Christopher-Hennings29Beth S. Thompson30Melanie Prarat31Dennis Summers32Craig Bowen33Joseph Boyle34Kenitra Hendrix35James Lyons36Kelli Werling37Andreia G. Arruda38Mark Schwartz39Mark Schwartz40Paul Yeske41Deborah Murray42Brigitte Mason43Peter Schneider44Samuel Copeland45Luc Dufresne46Daniel Boykin47Corrine Fruge48William Hollis49Rebecca C. Robbins50Thomas Petznick51Kurt Kuecker52Lauren Glowzenski53Megan Niederwerder54Daniel C. L. Linhares55Giovani Trevisan56Veterinary Diagnostic and Production Animal Medicine, Iowa State University, Ames, IA, United StatesVeterinary Diagnostic and Production Animal Medicine, Iowa State University, Ames, IA, United StatesVeterinary Diagnostic and Production Animal Medicine, Iowa State University, Ames, IA, United StatesVeterinary Diagnostic and Production Animal Medicine, Iowa State University, Ames, IA, United StatesVeterinary Diagnostic and Production Animal Medicine, Iowa State University, Ames, IA, United StatesVeterinary Diagnostic and Production Animal Medicine, Iowa State University, Ames, IA, United StatesVeterinary Diagnostic and Production Animal Medicine, Iowa State University, Ames, IA, United StatesVeterinary Diagnostic and Production Animal Medicine, Iowa State University, Ames, IA, United StatesVeterinary Diagnostic and Production Animal Medicine, Iowa State University, Ames, IA, United StatesVeterinary Diagnostic and Production Animal Medicine, Iowa State University, Ames, IA, United StatesVeterinary Diagnostic and Production Animal Medicine, Iowa State University, Ames, IA, United StatesVeterinary Diagnostic and Production Animal Medicine, Iowa State University, Ames, IA, United StatesVeterinary Diagnostic and Production Animal Medicine, Iowa State University, Ames, IA, United StatesVeterinary Diagnostic and Production Animal Medicine, Iowa State University, Ames, IA, United StatesVeterinary Diagnostic and Production Animal Medicine, Iowa State University, Ames, IA, United StatesVeterinary Population Medicine, University of Minnesota, Saint Paul, MN, United StatesVeterinary Population Medicine, University of Minnesota, Saint Paul, MN, United StatesVeterinary Population Medicine, University of Minnesota, Saint Paul, MN, United StatesVeterinary Population Medicine, University of Minnesota, Saint Paul, MN, United StatesVeterinary Population Medicine, University of Minnesota, Saint Paul, MN, United StatesKansas State Veterinary Diagnostic Laboratory, Kansas State University, Manhattan, KS, United StatesKansas State Veterinary Diagnostic Laboratory, Kansas State University, Manhattan, KS, United StatesKansas State Veterinary Diagnostic Laboratory, Kansas State University, Manhattan, KS, United StatesKansas State Veterinary Diagnostic Laboratory, Kansas State University, Manhattan, KS, United StatesKansas State Veterinary Diagnostic Laboratory, Kansas State University, Manhattan, KS, United StatesVeterinary and Biomedical Sciences Department, South Dakota State University, Brookings, SD, United StatesVeterinary and Biomedical Sciences Department, South Dakota State University, Brookings, SD, United StatesVeterinary and Biomedical Sciences Department, South Dakota State University, Brookings, SD, United StatesVeterinary and Biomedical Sciences Department, South Dakota State University, Brookings, SD, United StatesVeterinary and Biomedical Sciences Department, South Dakota State University, Brookings, SD, United StatesSouth Dakota Animal Industry Board, Pierre, SD, United StatesOhio Animal Disease and Diagnostic Laboratory, Reynoldsburg, OH, United StatesOhio Animal Disease and Diagnostic Laboratory, Reynoldsburg, OH, United StatesCollege of Veterinary Medicine, Purdue University, West Lafayette, IN, United StatesCollege of Veterinary Medicine, Purdue University, West Lafayette, IN, United StatesCollege of Veterinary Medicine, Purdue University, West Lafayette, IN, United StatesCollege of Veterinary Medicine, Purdue University, West Lafayette, IN, United StatesIndiana State Board of Animal Health, Indianapolis, IN, United States0Department of Veterinary Preventive Medicine, College of Veterinary Medicine, The Ohio State University, Columbus, OH, United StatesVeterinary Population Medicine, University of Minnesota, Saint Paul, MN, United States1Schwartz Farms Inc., Sleepy Eye, MN, United States2Swine Vet Center, St. Peter, MN, United States3New Fashion Pork, Jackson, MN, United States4Country View Family Farms, Middletown, PA, United States5Innovative Agriculture Solutions, LLC, Waterloo, IA, United States6Prestage Farms, Clinton, NC, United States7Swine Veterinary Partners, Québec, QC, Canada8Smithfield Foods, Smithfield, VA, United States9The Maschhoffs LLC, Carlyle, IL, United States0Carthage Veterinary Service LTD, Carthage, IL, United States1Pig Improvement Company, Hendersonville, TN, United States2ArkCare, Omaha, NE, United States3The Hanor Company, Enid, OK, United States4Pipestone Veterinary Services, Pipestone, MN, United States5Swine Health Information Center, Ames, IA, United StatesVeterinary Diagnostic and Production Animal Medicine, Iowa State University, Ames, IA, United StatesVeterinary Diagnostic and Production Animal Medicine, Iowa State University, Ames, IA, United StatesInfluenza 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.https://www.frontiersin.org/articles/10.3389/fvets.2025.1572237/fullzoonotic diseaseIAVmonitoringswineepidemiologydiagnostic |
| spellingShingle | Daniel C. A. Moraes Guilherme A. Cezar Edison S. Magalhães Rafael R. Nicolino Kinath Rupasinghe Srijita Chandra Gustavo S. Silva Marcelo N. Almeida Bret Crim Eric R. Burrough Phillip C. Gauger Darin Madson Joseph Thomas Michael A. Zeller Rodger Main Mary Thurn Paulo Lages Cezar A. Corzo Mattew Sturos Hemant Naikare Rob McGaughey Franco Matias Ferreyra Jamie Retallick Jordan Gebhardt Sara McReynolds Jon Greseth Darren Kersey Travis Clement Angela Pillatzki Jane Christopher-Hennings Beth S. Thompson Melanie Prarat Dennis Summers Craig Bowen Joseph Boyle Kenitra Hendrix James Lyons Kelli Werling Andreia G. Arruda Mark Schwartz Mark Schwartz Paul Yeske Deborah Murray Brigitte Mason Peter Schneider Samuel Copeland Luc Dufresne Daniel Boykin Corrine Fruge William Hollis Rebecca C. Robbins Thomas Petznick Kurt Kuecker Lauren Glowzenski Megan Niederwerder Daniel C. L. Linhares Giovani Trevisan Macroepidemiological trends of Influenza A virus detection through reverse transcription real-time polymerase chain reaction (RT-rtPCR) in porcine samples in the United States over the last 20 years Frontiers in Veterinary Science zoonotic disease IAV monitoring swine epidemiology diagnostic |
| title | Macroepidemiological trends of Influenza A virus detection through reverse transcription real-time polymerase chain reaction (RT-rtPCR) in porcine samples in the United States over the last 20 years |
| title_full | Macroepidemiological trends of Influenza A virus detection through reverse transcription real-time polymerase chain reaction (RT-rtPCR) in porcine samples in the United States over the last 20 years |
| title_fullStr | Macroepidemiological trends of Influenza A virus detection through reverse transcription real-time polymerase chain reaction (RT-rtPCR) in porcine samples in the United States over the last 20 years |
| title_full_unstemmed | Macroepidemiological trends of Influenza A virus detection through reverse transcription real-time polymerase chain reaction (RT-rtPCR) in porcine samples in the United States over the last 20 years |
| title_short | Macroepidemiological trends of Influenza A virus detection through reverse transcription real-time polymerase chain reaction (RT-rtPCR) in porcine samples in the United States over the last 20 years |
| title_sort | macroepidemiological trends of influenza a virus detection through reverse transcription real time polymerase chain reaction rt rtpcr in porcine samples in the united states over the last 20 years |
| topic | zoonotic disease IAV monitoring swine epidemiology diagnostic |
| url | https://www.frontiersin.org/articles/10.3389/fvets.2025.1572237/full |
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