Predicting the time to detect moderately virulent African swine fever virus in finisher swine herds using a stochastic disease transmission model

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2 Scopus citations


Background: African swine fever (ASF) is a highly contagious and devastating pig disease that has caused extensive global economic losses. Understanding ASF virus (ASFV) transmission dynamics within a herd is necessary in order to prepare for and respond to an outbreak in the United States. Although the transmission parameters for the highly virulent ASF strains have been estimated in several articles, there are relatively few studies focused on moderately virulent strains. Using an approximate Bayesian computation algorithm in conjunction with Monte Carlo simulation, we have estimated the adequate contact rate for moderately virulent ASFV strains and determined the statistical distributions for the durations of mild and severe clinical signs using individual, pig-level data. A discrete individual based disease transmission model was then used to estimate the time to detect ASF infection based on increased mild clinical signs, severe clinical signs, or daily mortality. Results: Our results indicate that it may take two weeks or longer to detect ASF in a finisher swine herd via mild clinical signs or increased mortality beyond levels expected in routine production. A key factor contributing to the extended time to detect ASF in a herd is the fairly long latently infected period for an individual pig (mean 4.5, 95% P.I., 2.4 - 7.2 days). Conclusion: These transmission model parameter estimates and estimated time to detection via clinical signs provide valuable information that can be used not only to support emergency preparedness but also to inform other simulation models of evaluating regional disease spread.

Original languageEnglish (US)
Article number84
JournalBMC Veterinary Research
Issue number1
StatePublished - Dec 2022

Bibliographical note

Funding Information:
The authors thank Willie Loeffen and Wageningen Bioveterinary Research, Wageningen University, the Netherlands for kindly providing the experimental transmission data used in this study. The authors acknowledge the Minnesota Supercomputing Institute (MSI) and the University of Minnesota for providing resources that contributed to the research results reported within this paper.

Funding Information:
PJB, SM, AS, MRC, and CJC acknowledge funding of their work by a cooperative agreement between the Center for Epidemiology and Animal Health (CEAH) of the United States Department of Agriculture (USDA), Animal and Plant Health Inspection Service (APHIS) Veterinary Services (VS) and the University of Minnesota (UMN) USDA Award #: AP20VSCEAH00C054| (Quantitative Analysis to Manage Animal Transboundary /Emerging and Domestic Disease Emergencies and Support Risk-based Decision-Making.)

Publisher Copyright:
© 2022, The Author(s).


  • African Swine Fever
  • Clinical signs detection
  • Modeling
  • Moderately virulent strain
  • Mortality triggers
  • Surveillance
  • Swine Diseases/diagnosis
  • African Swine Fever Virus
  • African Swine Fever/diagnosis
  • Animals
  • Swine
  • Bayes Theorem
  • Disease Outbreaks/veterinary

PubMed: MeSH publication types

  • Journal Article


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