论文标题
Stan的贝叶斯工作流程用于疾病传播建模
Bayesian workflow for disease transmission modeling in Stan
论文作者
论文摘要
该教程显示了如何在Stan中建立，适应和批评疾病传播模型，对于有兴趣建模贝叶斯框架中SARS-COV-2大流行和其他传染病的研究人员应该很有用。贝叶斯建模提供了一种量化不确定性并将数据和先验知识纳入模型估计的原则方法。 Stan是一种表达的概率编程语言，它可以提取推理并允许用户专注于建模。结果，Stan代码是可读且易于扩展的，这使建模者的工作更加透明。此外，Stan的主要推理引擎Hamiltonian Monte Carlo采样是对诊断的友善，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，我们演示了如何制定，拟合和诊断Stan中的隔室传输模型，首先采用简单的易感性感染（SIR）模型，然后使用SARS-COV-2大流行期间使用的更精细的传输模型。我们还介绍了高级主题，这些主题可以进一步帮助从业者适合复杂的模型；值得注意的是，如何使用模拟探测模型和先验，以及基于普通微分方程的扩展模型的计算技术。
This tutorial shows how to build, fit, and criticize disease transmission models in Stan, and should be useful to researchers interested in modeling the 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 (SIR) 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.
