Modelling and Bayesian analysis of the Abakaliki smallpox data
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 asses...
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| Format: | Article |
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Elsevier
2017
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| Online Access: | https://eprints.nottingham.ac.uk/39887/ |
| _version_ | 1848795937990770688 |
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| author | Stockdale, Jessica E. Kypraios, Theodore O’Neill, Philip D. |
| author_facet | Stockdale, Jessica E. Kypraios, Theodore O’Neill, Philip D. |
| author_sort | Stockdale, Jessica E. |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T19:40:02Z |
| format | Article |
| id | nottingham-39887 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T19:40:02Z |
| publishDate | 2017 |
| publisher | Elsevier |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-398872020-05-04T18:50:12Z https://eprints.nottingham.ac.uk/39887/ Modelling and Bayesian analysis of the Abakaliki smallpox data Stockdale, Jessica E. Kypraios, Theodore O’Neill, Philip D. 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. Elsevier 2017-06-15 Article PeerReviewed Stockdale, Jessica E., Kypraios, Theodore and O’Neill, Philip D. (2017) Modelling and Bayesian analysis of the Abakaliki smallpox data. Epidemics, 19 . pp. 13-23. ISSN 1878-0067 Smallpox; Bayesian inference; Markov chain Monte Carlo; Stochastic epidemic model; Abakaliki http://www.sciencedirect.com/science/article/pii/S1755436516300500?via%3Dihub doi:10.1016/j.epidem.2016.11.005 doi:10.1016/j.epidem.2016.11.005 |
| spellingShingle | Smallpox; Bayesian inference; Markov chain Monte Carlo; Stochastic epidemic model; Abakaliki Stockdale, Jessica E. Kypraios, Theodore O’Neill, Philip D. Modelling and Bayesian analysis of the Abakaliki smallpox data |
| title | Modelling and Bayesian analysis of the Abakaliki smallpox data |
| title_full | Modelling and Bayesian analysis of the Abakaliki smallpox data |
| title_fullStr | Modelling and Bayesian analysis of the Abakaliki smallpox data |
| title_full_unstemmed | Modelling and Bayesian analysis of the Abakaliki smallpox data |
| title_short | Modelling and Bayesian analysis of the Abakaliki smallpox data |
| title_sort | modelling and bayesian analysis of the abakaliki smallpox data |
| topic | Smallpox; Bayesian inference; Markov chain Monte Carlo; Stochastic epidemic model; Abakaliki |
| url | https://eprints.nottingham.ac.uk/39887/ https://eprints.nottingham.ac.uk/39887/ https://eprints.nottingham.ac.uk/39887/ |