Bayesian workflow for disease transmission modeling in Stan
Bayesian workflow for disease transmission modeling in Stan
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DOI：
10.1002/sim.9164
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发表时间：
2021-09-08
影响因子：
2
通讯作者：
Riou, Julien
中科院分区：
文献类型：
--
作者：
Grinsztajn, Leo;Semenova, Elizaveta;Riou, Julien
本教程介绍了如何在STAN中构建、匹配和批评疾病传播模型，对于有意在贝叶斯框架中对严重急性呼吸综合征冠状病毒2(SARS-CoV-2)大流行和其他传染病建模的研究人员应该很有用。贝叶斯建模提供了一种原则性的方法来量化不确定性，并将数据和先验知识纳入模型估计。Stan是一种可表达的概率编程语言，它抽象了推理并允许用户将注意力集中在建模上。因此，Stan代码具有可读性和可扩展性，这使得建模师的工作更加透明。此外，斯坦的主要推理引擎哈密顿蒙特卡罗采样易于诊断，这意味着用户可以验证所获得的推理是否可靠。在本教程中，我们将演示如何在STAN中构建、拟合和诊断间隔传播模型，首先使用简单的易感-感染-恢复模型，然后使用在SARS-CoV-2大流行期间使用的更详细的传播模型。我们还介绍了可以进一步帮助实践者拟合复杂模型的高级主题；特别是如何使用模拟来探索模型和先验知识，以及计算技术来放大基于常微分方程式的模型。
This tutorial shows how to build, fit, and criticize disease transmission models in Stan, and should be useful to researchers interested in modeling the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic and other infectious diseases in a Bayesian framework. Bayesian modeling provides a principled way to quantify uncertainty and incorporate both data and prior knowledge into the model estimates. Stan is an expressive probabilistic programming language that abstracts the inference and allows users to focus on the modeling. As a result, Stan code is readable and easily extensible, which makes the modeler's work more transparent. Furthermore, Stan's main inference engine, Hamiltonian Monte Carlo sampling, is amiable to diagnostics, which means the user can verify whether the obtained inference is reliable. In this tutorial, we demonstrate how to formulate, fit, and diagnose a compartmental transmission model in Stan, first with a simple susceptible-infected-recovered model, then with a more elaborate transmission model used during the SARS-CoV-2 pandemic. We also cover advanced topics which can further help practitioners fit sophisticated models; notably, how to use simulations to probe the model and priors, and computational techniques to scale-up models based on ordinary differential equations.