Modelling and Bayesian analysis of the Abakaliki smallpox data

Stockdale, Jessica E. and Kypraios, Theodore and O’Neill, Philip D. (2016) Modelling and Bayesian analysis of the Abakaliki smallpox data. Epidemics . ISSN 1878-0067 (In Press)

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Abstract

The celebrated Abakaliki smallpox data have appeared numerous times in the epidemic modelling literature, but in almost all cases only a specific subset of the data is considered. The only previous analysis of the full data set relied on approximation methods to derive a likelihood and did not assess model adequacy. The data themselves continue to be of interest due to concerns about the possible re-emergence of smallpox as a bioterrorism weapon. We present the first full Bayesian statistical analysis using data-augmentation Markov chain Monte Carlo methods which avoid the need for likelihood approximations and which yield a wider range of results than previous analyses. We also carry out model assessment using simulation-based methods. Our findings suggest that the outbreak was largely driven by the interaction structure of the population, and that the introduction of control measures was not the sole reason for the end of the epidemic. We also obtain quantitative estimates of key quantities including reproduction numbers.

Item Type: Article
Keywords: Smallpox; Bayesian inference; Markov chain Monte Carlo; Stochastic epidemic model; Abakaliki
Schools/Departments: University of Nottingham, UK > Faculty of Science > School of Mathematical Sciences
Identification Number: 10.1016/j.epidem.2016.11.005
Depositing User: Eprints, Support
Date Deposited: 17 Jan 2017 08:43
Last Modified: 27 May 2017 06:06
URI: http://eprints.nottingham.ac.uk/id/eprint/39887

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