Incorporating Contact Network Uncertainty in Individual Level Models of Infectious Disease using Approximate Bayesian Computation

IF 1.2 4区数学

International Journal of Biostatistics Pub Date : 2019-12-10 DOI:10.1515/ijb-2017-0092

Waleed Almutiry, R. Deardon

引用次数: 7

Abstract

Abstract Infectious disease transmission between individuals in a heterogeneous population is often best modelled through a contact network. However, such contact network data are often unobserved. Such missing data can be accounted for in a Bayesian data augmented framework using Markov chain Monte Carlo (MCMC). Unfortunately, fitting models in such a framework can be highly computationally intensive. We investigate the fitting of network-based infectious disease models with completely unknown contact networks using approximate Bayesian computation population Monte Carlo (ABC-PMC) methods. This is done in the context of both simulated data, and data from the UK 2001 foot-and-mouth disease epidemic. We show that ABC-PMC is able to obtain reasonable approximations of the underlying infectious disease model with huge savings in computation time when compared to a full Bayesian MCMC analysis.

查看原文本刊更多论文

用近似贝叶斯计算纳入传染病个体水平模型中的接触网络不确定性

摘要异质人群中个体之间的传染病传播通常最好通过接触网络进行建模。然而，这样的联系网络数据往往是不被注意到的。这种缺失的数据可以在使用马尔可夫链蒙特卡罗（MCMC）的贝叶斯数据增强框架中解释。不幸的是，在这样的框架中拟合模型可能是高度计算密集型的。我们使用近似贝叶斯计算群体蒙特卡罗（ABC-PCC）方法研究了具有完全未知接触网络的基于网络的传染病模型的拟合。这是在模拟数据和英国2001年口蹄疫疫情数据的背景下进行的。我们表明，与完整的贝叶斯MCMC分析相比，ABC-PC能够获得潜在传染病模型的合理近似值，并节省了大量的计算时间。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

International Journal of Biostatistics Mathematics-Statistics and Probability

CiteScore

2.30

自引率

8.30%

发文量

期刊介绍： The International Journal of Biostatistics (IJB) seeks to publish new biostatistical models and methods, new statistical theory, as well as original applications of statistical methods, for important practical problems arising from the biological, medical, public health, and agricultural sciences with an emphasis on semiparametric methods. Given many alternatives to publish exist within biostatistics, IJB offers a place to publish for research in biostatistics focusing on modern methods, often based on machine-learning and other data-adaptive methodologies, as well as providing a unique reading experience that compels the author to be explicit about the statistical inference problem addressed by the paper. IJB is intended that the journal cover the entire range of biostatistics, from theoretical advances to relevant and sensible translations of a practical problem into a statistical framework. Electronic publication also allows for data and software code to be appended, and opens the door for reproducible research allowing readers to easily replicate analyses described in a paper. Both original research and review articles will be warmly received, as will articles applying sound statistical methods to practical problems.