Kypraios Theodore, Neal Peter, Prangle Dennis
School of Mathematical Sciences, University of Nottingham, UK.
Department of Mathematics and Statistics, Lancaster University, UK.
Math Biosci. 2017 May;287:42-53. doi: 10.1016/j.mbs.2016.07.001. Epub 2016 Jul 18.
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.
由于疾病爆发数据固有的依赖性以及数据通常不完整这一事实，基于似然性的疾病爆发数据推断可能极具挑战性。在本文中，我们回顾了近期用于分析此类数据的近似贝叶斯计算（ABC）方法，这些方法通过拟合随机流行模型来处理数据，而无需计算观测数据的似然性。我们考虑了非时间数据和时间数据，并通过多个具有不同模型和数据集的示例来说明这些方法。此外，我们还介绍了对现有算法的扩展，这些扩展易于实现，并对现有方法有所改进。最后，可在https://github.com/kypraios/epiABC上获取实现本文所提出算法的R代码。
