Login / Signup

Managing complexity: Simplifying assumptions of foot-and-mouth disease models for swine.

Amy KinsleyK VanderWaalM E CraftR B MorrisonA M Perez
Published in: Transboundary and emerging diseases (2018)
Compartmental models have often been used to test the effectiveness and efficiency of alternative control strategies to mitigate the spread of infectious animal diseases. A fundamental principle of epidemiological modelling is that models should start as simple as possible and become as complex as needed. The simplest version of a compartmental model assumes that the population is closed, void of births and deaths and that this closed population mixes homogeneously, meaning that each infected individual has an equal probability of coming into contact with each susceptible individual in the population. However, this assumption may oversimplify field conditions, leading to conclusions about disease mitigation strategies that are suboptimal. Here, we assessed the impact of the homogeneous mixing/closed population assumption, which is commonly assumed for within-farm models of highly contagious diseases of swine, such as foot-and-mouth disease (FMD), on predictions about disease spread. Incorporation of farm structure (different barns or rooms for breeding and gestation, farrowing, nursery and finishing) and demography (piglet births and deaths, and animal movement within and off of the farm) resulted in transmission dynamics that differed in the latter portion of an outbreak. Specifically, farm structure and demography, which were included in the farrow to finish and farrow to wean farms, resulted in FMD virus persistence within the population under certain conditions. Results here demonstrate the impact of incorporating farm structure and demography into models of FMD spread in swine populations and will ultimately contribute to the design and evaluation of effective disease control strategies to mitigate the impact of potential incursions.
Keyphrases
  • randomized controlled trial
  • systematic review
  • climate change
  • risk assessment
  • palliative care