title: Estimation of under-reporting in epidemics using approximations.
abstract: Under-reporting in epidemics, when it is ignored, leads to under-estimation of the infection rate and therefore of the reproduction number. In the case of stochastic models with temporal data, a usual approach for dealing with such issues is to apply data augmentation techniques through Bayesian methodology. Departing from earlier literature approaches implemented using reversible jump Markov chain Monte Carlo (RJMCMC) techniques, we make use of approximations to obtain faster estimation with simple MCMC. Comparisons among the methods developed here, and with the RJMCMC approach, are carried out and highlight that approximation-based methodology offers useful alternative inference tools for large epidemics, with a good trade-off between time cost and accuracy.
authors: Biomathematics and Statistics Scotland, Edinburgh, UK. Kokouvi.Gamado@bioss.ac.uk.
authors: Kokouvi
authors: K
authors: Gamado
authors: Maxwell Institute for Mathematical Sciences, Heriot-Watt University, Edinburgh, UK.
authors: George
authors: G
authors: Streftaris
authors: Maxwell Institute for Mathematical Sciences, Heriot-Watt University, Edinburgh, UK.
authors: Stan
authors: S
authors: Zachary
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D058872:Epidemics / Q000706:statistics & numerical data*; D006801:Humans; D008390:Markov Chains; D008962:Models, Theoretical; D009010:Monte Carlo Method
keywords: Approximations; Final size distribution; Markov chain Monte Carlo; Reversible jump; Stochastic SIR model; Under-reporting