Understanding the early dynamics of the 2014 porcine epidemic diarrhea virus (PEDV) outbreak in Ontario using the incidence decay and exponential adjustment (IDEA) model

Greer, Amy L.; Spence, Kelsey; Gardner, Emma

Mathematical models are important tools for understanding the dynamics of infectious diseases within populations. This understanding contributes to our ability to identify disease prevention and control measures that will have the greatest likelihood of success and also allows for a thorough examination of the uncertainty associated with many epidemic processes. Many disease transmission models are mechanistic in nature and have been built for parsimony in order to present the simplest explanation for the observed data. Models are a mathematical representation of the health states considered to be relevant for the research question and population of interest. Parameter values describe the proposed way in which individuals move between the various health states (e.g. susceptible, infected, recovered). In the case of a mechanistic model, a significant amount of information related to the natural history of the disease, incidence within the population of interest, immune status of the population of interest and contact patterns between individuals and premises is required in order to appropriately parameterize such a model. The Incidence Decay and Exponential Adjustment (IDEA) model was first described in 2013 [20] . The model is not mechanistic in nature but rather is a simple, 2parameter model that has shown significant promise as a descriptive tool capable of projecting epidemic processes within human populations using limited data [21, 22] . Here we apply the IDEA model to a veterinary infectious disease epidemic, specifically the Ontario PEDV outbreak in 2014. Our objectives were to assess the models ability to: 1) capture the documented patterns of PEDV epidemic growth in Ontario during the 2014 outbreak, 2) provide insight into the impact of different PEDV control measures, and 3) "near-cast" the final epidemic size and duration using only data from early in the outbreak.