Modeling and inference for infectious disease dynamics: a likelihood-based approach.
Modeling and inference for infectious disease dynamics: a likelihood-based approach.
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DOI：
10.1214/17-sts636
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发表时间：
2018-03
期刊：
影响因子：
--
通讯作者：
Bretó C
中科院分区：
文献类型：
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作者：
Bretó C
大多数涉及随机建模的科学领域都考虑了基于可能性的统计推断。这包括传染病动力学，其中科学理解可以帮助捕获所谓的机械模型及其似然函数中的生物过程。然而，当这种机械模型的可能性缺乏封闭式表达时，计算负担很大。在这种背景下，算法的进步促进了可能性最大化，促进了过去十年中新型数据驱动的机械模型的研究。回顾这些模型是本文的重点。我们特别强调了这些模型的统计方面，例如过度分散，这是非线性传染病建模和数据分析之间接口的关键。我们还指出了进一步模型探索的潜在方向。
Likelihood-based statistical inference has been considered in most scientific fields involving stochastic modeling. This includes infectious disease dynamics, where scientific understanding can help capture biological processes in so-called mechanistic models and their likelihood functions. However, when the likelihood of such mechanistic models lacks a closed-form expression, computational burdens are substantial. In this context, algorithmic advances have facilitated likelihood maximization, promoting the study of novel data-motivated mechanistic models over the last decade. Reviewing these models is the focus of this paper. In particular, we highlight statistical aspects of these models like overdispersion, which is key in the interface between nonlinear infectious disease modeling and data analysis. We also point out potential directions for further model exploration.