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 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, code to implement the algorithms presented in the paper is available on url{https://github.com/kypraios/epiABC}.

References in zbMATH (referenced in 10 articles , 1 standard article )

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  1. Buckwar, Evelyn; Tamborrino, Massimiliano; Tubikanec, Irene: Spectral density-based and measure-preserving ABC for partially observed diffusion processes. An illustration on Hamiltonian SDEs (2020)
  2. Manevski, Damjan; Ružić Gorenjec, Nina; Kejžar, Nataša; Blagus, Rok: Modeling COVID-19 pandemic using Bayesian analysis with application to Slovene data (2020)
  3. Touloupou, Panayiota; Finkenstädt, Bärbel; Besser, Thomas E.; French, Nigel P.; Spencer, Simon E. F.: Bayesian inference for multistrain epidemics with application to \textitEscherichiacoli O157:H7 in feedlot cattle (2020)
  4. Ke, Yuqin; Tian, Tianhai: Approximate Bayesian computational methods for the inference of unknown parameters (2019)
  5. Dutta, Ritabrata; Mira, Antonietta; Onnela, Jukka-Pekka: Bayesian inference of spreading processes on networks (2018)
  6. Feng, Tao; Qiu, Zhipeng: Global dynamics of deterministic and stochastic epidemic systems with nonmonotone incidence rate (2018)
  7. Karabatsos, George; Leisen, Fabrizio: An approximate likelihood perspective on ABC methods (2018)
  8. McKinley, Trevelyan J.; Vernon, Ian; Andrianakis, Ioannis; McCreesh, Nicky; Oakley, Jeremy E.; Nsubuga, Rebecca N.; Goldstein, Michael; White, Richard G.: Approximate Bayesian computation and simulation-based inference for complex stochastic epidemic models (2018)
  9. Prangle, Dennis; Everitt, Richard G.; Kypraios, Theodore: A rare event approach to high-dimensional approximate Bayesian computation (2018)
  10. Kypraios, Theodore; Neal, Peter; Prangle, Dennis: A tutorial introduction to Bayesian inference for stochastic epidemic models using approximate Bayesian computation (2017)