Frontiers in Veterinary Science (Jun 2017)

A Review of Quantitative Tools Used to Assess the Epidemiology of Porcine Reproductive and Respiratory Syndrome in U.S. Swine Farms Using Dr. Morrison’s Swine Health Monitoring Program Data

  • Carles Vilalta,
  • Andreia G. Arruda,
  • Andreia G. Arruda,
  • Steven J. P. Tousignant,
  • Steven J. P. Tousignant,
  • Pablo Valdes-Donoso,
  • Pablo Valdes-Donoso,
  • Petra Muellner,
  • Ulrich Muellner,
  • Moh A. Alkhamis,
  • Moh A. Alkhamis,
  • Robert B. Morrison,
  • Andres M. Perez

DOI
https://doi.org/10.3389/fvets.2017.00094
Journal volume & issue
Vol. 4

Abstract

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Porcine reproductive and respiratory syndrome (PRRS) causes far-reaching financial losses to infected countries and regions, including the U.S. The Dr. Morrison’s Swine Health Monitoring Program (MSHMP) is a voluntary initiative in which producers and veterinarians share sow farm PRRS status weekly to contribute to the understanding, in quantitative terms, of PRRS epidemiological dynamics and, ultimately, to support its control in the U.S. Here, we offer a review of a variety of analytic tools that were applied to MSHMP data to assess disease dynamics in quantitative terms to support the decision-making process for veterinarians and producers. Use of those methods has helped the U.S. swine industry to quantify the cyclical patterns of PRRS, to describe the impact that emerging pathogens has had on that pattern, to identify the nature and extent at which environmental factors (e.g., precipitation or land cover) influence PRRS risk, to identify PRRS virus emerging strains, and to assess the influence that voluntary reporting has on disease control. Results from the numerous studies reviewed here provide important insights into PRRS epidemiology that help to create the foundations for a near real-time prediction of disease risk, and, ultimately, will contribute to support the prevention and control of, arguably, one of the most devastating diseases affecting the North American swine industry. The review also demonstrates how different approaches to analyze and visualize the data may help to add value to the routine collection of surveillance data and support infectious animal disease control.

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