A tutorial introduction to Bayesian inference for stochastic epidemic models using Approximate Bayesian Computation
Likelihood-based inference for disease outbreak data can be very challenging due to the inherent dependence of the data and the fact that they are usually incomplete. In this paper we review recent Approximate Bayesian Computation (ABC) methods for the analysis of such data by fitting to them stocha...
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| Format: | Article |
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Elsevier
2017
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| Online Access: | https://eprints.nottingham.ac.uk/44015/ |
| _version_ | 1848796817357012992 |
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| author | Kypraios, Theodore Neal, Peter Prangle, Dennis |
| author_facet | Kypraios, Theodore Neal, Peter Prangle, Dennis |
| author_sort | Kypraios, Theodore |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | Likelihood-based inference for disease outbreak data can be very challenging due to the inherent dependence of the data and the fact that they are usually incomplete. In this paper we review recent Approximate Bayesian Computation (ABC) methods for the analysis of such data by fitting to them stochastic epidemic models without having to calculate the likelihood of the observed data. We consider both non-temporal and temporal-data and illustrate the methods with a number of examples featuring different models and datasets. In addition, we present extensions to existing algorithms which are easy to implement and provide an improvement to the existing methodology. Finally, R code to implement the algorithms presented in the paper is available on https://github.com/kypraios/epiABC. |
| first_indexed | 2025-11-14T19:54:00Z |
| format | Article |
| id | nottingham-44015 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T19:54:00Z |
| publishDate | 2017 |
| publisher | Elsevier |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-440152020-05-04T18:48:31Z https://eprints.nottingham.ac.uk/44015/ A tutorial introduction to Bayesian inference for stochastic epidemic models using Approximate Bayesian Computation Kypraios, Theodore Neal, Peter Prangle, Dennis Likelihood-based inference for disease outbreak data can be very challenging due to the inherent dependence of the data and the fact that they are usually incomplete. In this paper we review recent Approximate Bayesian Computation (ABC) methods for the analysis of such data by fitting to them stochastic epidemic models without having to calculate the likelihood of the observed data. We consider both non-temporal and temporal-data and illustrate the methods with a number of examples featuring different models and datasets. In addition, we present extensions to existing algorithms which are easy to implement and provide an improvement to the existing methodology. Finally, R code to implement the algorithms presented in the paper is available on https://github.com/kypraios/epiABC. Elsevier 2017-05-31 Article PeerReviewed Kypraios, Theodore, Neal, Peter and Prangle, Dennis (2017) A tutorial introduction to Bayesian inference for stochastic epidemic models using Approximate Bayesian Computation. Mathematical Biosciences, 287 . pp. 42-53. ISSN 1879-3134 Bayesian inference; Epidemics; Stochastic epidemic models; Approximate Bayesian Computation; Population Monte Carlo https://doi.org/10.1016/j.mbs.2016.07.001 doi:10.1016/j.mbs.2016.07.001 doi:10.1016/j.mbs.2016.07.001 |
| spellingShingle | Bayesian inference; Epidemics; Stochastic epidemic models; Approximate Bayesian Computation; Population Monte Carlo Kypraios, Theodore Neal, Peter Prangle, Dennis A tutorial introduction to Bayesian inference for stochastic epidemic models using Approximate Bayesian Computation |
| title | A tutorial introduction to Bayesian inference for stochastic epidemic models using Approximate Bayesian Computation |
| title_full | A tutorial introduction to Bayesian inference for stochastic epidemic models using Approximate Bayesian Computation |
| title_fullStr | A tutorial introduction to Bayesian inference for stochastic epidemic models using Approximate Bayesian Computation |
| title_full_unstemmed | A tutorial introduction to Bayesian inference for stochastic epidemic models using Approximate Bayesian Computation |
| title_short | A tutorial introduction to Bayesian inference for stochastic epidemic models using Approximate Bayesian Computation |
| title_sort | tutorial introduction to bayesian inference for stochastic epidemic models using approximate bayesian computation |
| topic | Bayesian inference; Epidemics; Stochastic epidemic models; Approximate Bayesian Computation; Population Monte Carlo |
| url | https://eprints.nottingham.ac.uk/44015/ https://eprints.nottingham.ac.uk/44015/ https://eprints.nottingham.ac.uk/44015/ |