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A probabilistic Poisson-based model to detect PRRSV recirculation using sow production records.

L Fraile1, N Fernández2, R N Pena1

  • 1Department of Animal Science, University of Lleida - Agrotecnio Center, Lleida, Spain.

Preventive Veterinary Medicine
|March 16, 2020
PubMed
Summary
This summary is machine-generated.

A new method using a conditional Poisson model can quickly detect Porcine Reproductive and Respiratory Syndrome virus (PRRSV) recirculation in sow herds. This tool analyzes farrowing data to identify potential outbreaks early, improving herd management.

Keywords:
Conditional Poisson distributionDetectionPRRSV circulation

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Area of Science:

  • Veterinary Epidemiology
  • Swine Production Medicine
  • Disease Surveillance

Background:

  • Porcine Reproductive and Respiratory Syndrome (PRRS) significantly impacts swine production, causing reduced number of born alive piglets (NBA) and increased number of lost piglets (NLP).
  • Early detection of PRRS virus (PRRSV) recirculation in sow herds is crucial for effective disease management and economic viability.
  • Current diagnostic methods may not always provide timely identification of recirculation events under field conditions.

Purpose of the Study:

  • To develop and validate a novel statistical method for the early detection of PRRSV recirculation in commercial sow production farms.
  • To utilize routine sow reproductive performance data for identifying potential PRRSV circulation periods.
  • To provide a practical tool for swine veterinarians and producers to monitor herd health status regarding PRRSV.

Main Methods:

  • A conditional Poisson model was developed to analyze the relationship between NBA and NLP per farrowing.
  • A three-step procedure involving maximum-likelihood estimation, p-value calculation for deviations, and chi-square-inverse weighting of recent farrowing data was implemented.
  • The method was initially set up using data from one farm (farm T) and validated on ten additional farms (farms V1-V10) with known PRRSV recirculation episodes.

Main Results:

  • The method successfully identified two PRRSV circulating periods in the initial farm (farm T), which were confirmed by laboratory diagnostics.
  • Validation across ten farms accurately detected ten previously diagnosed PRRSV recirculation episodes.
  • The model demonstrated high sensitivity, with only minor discrepancies (one false negative, one false positive) over a relatively small number of farrowings.

Conclusions:

  • A conditional Poisson-based model analyzing NLP in relation to NBA is an effective and potentially routine tool for detecting PRRSV recirculation in sow herds.
  • This statistical approach offers a timely and data-driven method for early warning of PRRSV circulation.
  • The developed method can aid in prompt intervention strategies, mitigating the economic and animal welfare impacts of PRRSV.