Statistical Inference for Stochastic Epidemic Models
نویسندگان
چکیده
We consider continuous-time stochastic compartmental models which can be applied in veterinary epidemiology to model the within-herd dynamics of infectious diseases. We focus on an extension of Markovian epidemic models, allowing the infectious period of an individual to follow a Weibull distribution, resulting in more flexible modelling for many diseases. Following a Bayesian approach we show how approximation methods can be applied to design efficient MCMC algorithms with high acceptance ratios for fitting non-Markovian models to partial observations of epidemic processes. A simulation study is conducted to assess the effects of the frequency and accuracy of diagnostic tests on the information yielded on the epidemic process.
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