Estimation of under-reporting in epidemics using approximations

Kokouvi Gamado, George Streftaris, Stanley Zachary

Research output: Contribution to journalArticle

2 Citations (Scopus)
15 Downloads (Pure)

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.
Original languageEnglish
Pages (from-to)1683–1707
Number of pages25
JournalJournal of Mathematical Biology
Volume74
Issue number7
Early online date26 Oct 2016
DOIs
Publication statusPublished - Jun 2017

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