论文标题
传染病时空演化的系统推断：密歇根州COVID-19
System inference for the spatio-temporal evolution of infectious diseases: Michigan in the time of COVID-19
论文作者
论文摘要
我们扩展了传染病的经典SIR模型扩散，以解释参数的时间依赖性，其中还包括扩散性。时间依赖性解释了测试，隔离和治疗方案的特征变化，而扩散率则融合了移动人群。该模型已应用于美国密歇根州Covid-19大流行的演变数据。对于系统推断，我们使用最近的进步；特别是我们的变分系统识别框架（Wang等，Comp。Meth。App。Mech。Eng。，356，44-74，2019; Arxiv：2001.04816 [CS.CE]）以及贝叶斯机器的机器学习方法。
We extend the classical SIR model of infectious disease spread to account for time dependence in the parameters, which also include diffusivities. The temporal dependence accounts for the changing characteristics of testing, quarantine and treatment protocols, while diffusivity incorporates a mobile population. This model has been applied to data on the evolution of the COVID-19 pandemic in the US state of Michigan. For system inference, we use recent advances; specifically our framework for Variational System Identification (Wang et al., Comp. Meth. App. Mech. Eng., 356, 44-74, 2019; arXiv:2001.04816 [cs.CE]) as well as Bayesian machine learning methods.
