Epidemic dynamics are modelled using a variant of the SIR (susceptible-infectious-recovered) model. We investigate scenarios in which a single (dominant) locus affects animal susceptibility, infectivity and recovery rates. In particular, we find that genetic differences in susceptibility and recovery can be readily inferred using data from a single epidemic, but that infectivity requires information from multiple or replicated epidemics. Inference from partially observed epidemics was conducted within a Bayesian framework using Markov chain Monte Carlo (MCMC). The method is tested using simulated data generated by applying the Doob-Gillespie algorithm to a suitable epidemic model. Limits in our ability to carry out inference were explored in the case when complete epidemic data is known, and theoretical expressions are presented for expectations of parameter accuracy. The practical utility of the approach is subsequently demonstrated using data for which infection times are uncertain or completely unknown.
Proceedings of the World Congress on Genetics Applied to Livestock Production, Volume Methods and Tools: Statistical methods - linear and nonlinear models, , 221, 2014
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