Modelling and Bayesian analysis of the Abakaliki smallpox data

Stockdale, Jessica E.; Kypraios, Theodore; O�Neill, Philip D.

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.

Citation

Stockdale, J. E., Kypraios, T., & O’Neill, P. D. (2017). Modelling and Bayesian analysis of the Abakaliki smallpox data. Epidemics, 19, https://doi.org/10.1016/j.epidem.2016.11.005

• Journal Article Type: Article
• Acceptance Date: Nov 7, 2016
• Online Publication Date: Dec 9, 2016
• Publication Date: Jun 15, 2017
• Deposit Date: Jan 17, 2017
• Publicly Available Date: Jan 17, 2017
• Journal: Epidemics
• Print ISSN: 1755-4365
• Electronic ISSN: 1878-0067
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 19
• DOI: https://doi.org/10.1016/j.epidem.2016.11.005
• Keywords: Smallpox; Bayesian inference; Markov chain Monte Carlo; Stochastic epidemic model; Abakaliki
• Public URL: https://nottingham-repository.worktribe.com/output/866137
• Publisher URL: http://www.sciencedirect.com/science/article/pii/S1755436516300500?via%3Dihub
• Contract Date: Jan 17, 2017