title: Understanding the early dynamics of the 2014 porcine epidemic diarrhea virus (PEDV) outbreak in Ontario using the incidence decay and exponential adjustment (IDEA) model.
abstract: BACKGROUND
  The United States swine industry was first confronted with porcine epidemic diarrhea virus (PEDV) in 2013. In young pigs, the virus is highly pathogenic and the associated morbidity and mortality has a significant negative impact on the swine industry. We have applied the IDEA model to better understand the 2014 PEDV outbreak in Ontario, Canada. Using our simple, 2-parameter IDEA model, we have evaluated the early epidemic dynamics of PEDV on Ontario swine farms.
  
  
  RESULTS
  We estimated the best-fit R0and control parameter (d) for the between farm transmission component of the outbreak by fitting the model to publically available cumulative incidence data. We used maximum likelihood to compare model fit estimates for different combinations of the R0and d parameters. Using our initial findings from the iterative fitting procedure, we projected the time course of the epidemic using only a subset of the early epidemic data. The IDEA model projections showed excellent agreement with the observed data based on a 7-day generation time estimate. The best-fit estimate for R0was 1.87 (95% CI: 1.52 - 2.34) and for the control parameter (d) was 0.059 (95% CI: 0.022 - 0.117). Using data from the first three generations of the outbreak, our iterative fitting procedure suggests that R0and d had stabilized sufficiently to project the time course of the outbreak with reasonable accuracy.
  
  
  CONCLUSIONS
  The emergence and spread of PEDV represents an important agricultural emergency. The virus presents a significant ongoing threat to the Canadian swine industry. Developing an understanding of the important epidemiological characteristics and disease transmission dynamics of a novel pathogen such as PEDV is critical for helping to guide the implementation of effective, efficient, and economically feasible disease control and prevention strategies that are able to help decrease the impact of an outbreak.
authors.affiliation: Department of Population Medicine, Ontario Veterinary College, University of Guelph, Guelph, ON, N1G 2W1, Canada. agreer@uoguelph.ca.
authors.forename: Amy L
authors.initials: AL
authors.lastname: Greer
authors.affiliation: Department of Population Medicine, Ontario Veterinary College, University of Guelph, Guelph, ON, N1G 2W1, Canada.
authors.forename: Kelsey
authors.initials: K
authors.lastname: Spence
authors.affiliation: Department of Population Medicine, Ontario Veterinary College, University of Guelph, Guelph, ON, N1G 2W1, Canada.
authors.forename: Emma
authors.initials: E
authors.lastname: Gardner
mesh_terms: D000818:Animals; D018352:Coronavirus Infections / Q000662:veterinary* / Q000821:virology; D004196:Disease Outbreaks / Q000662:veterinary*; D009864:Ontario / Q000453:epidemiology; D053485:Porcine epidemic diarrhea virus; D013552:Swine; D013553:Swine Diseases / Q000453:epidemiology / Q000821:virology*; D013997:Time Factors
keywords: Disease dynamics; Epidemic; Forecasting; Modeling; PEDV; Pigs; Porcine epidemic diarrhea virus; Swine