Inference in Epidemic Models without Likelihoods

IF 1.2 4区数学

International Journal of Biostatistics Pub Date : 2009-07-20 DOI:10.2202/1557-4679.1171

T. McKinley, A. Cook, R. Deardon

引用次数: 188

Abstract

Likelihood-based inference for epidemic models can be challenging, in part due to difficulties in evaluating the likelihood. The problem is particularly acute in models of large-scale outbreaks, and unobserved or partially observed data further complicates this process. Here we investigate the performance of Markov Chain Monte Carlo and Sequential Monte Carlo algorithms for parameter inference, where the routines are based on approximate likelihoods generated from model simulations. We compare our results to a gold-standard data-augmented MCMC for both complete and incomplete data. We illustrate our techniques using simulated epidemics as well as data from a recent outbreak of Ebola Haemorrhagic Fever in the Democratic Republic of Congo and discuss situations in which we think simulation-based inference may be preferable to likelihood-based inference.

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无似然的流行病模型中的推论

基于可能性的流行病模型推断可能具有挑战性，部分原因是难以评估可能性。这一问题在大规模疫情模型中尤为严重，而未观察到或部分观察到的数据使这一过程进一步复杂化。在这里，我们研究了马尔可夫链蒙特卡罗和顺序蒙特卡罗算法在参数推理方面的性能，其中例程是基于模型模拟产生的近似似然。对于完整和不完整的数据，我们将结果与金标准的数据增强MCMC进行比较。我们使用模拟流行病以及刚果民主共和国最近爆发的埃博拉出血热的数据来说明我们的技术，并讨论了我们认为基于模拟的推断可能比基于可能性的推断更可取的情况。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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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.