论文标题
用于传输网络的空间随机SIR模型，该模型在中国的COVID-19
A Spatial Stochastic SIR Model for Transmission Networks with Application to COVID-19 Epidemic in China
论文作者
论文摘要
全世界政府已采取预防措施，以抵抗冠状病毒疾病的传播（Covid-19）。在这项研究中，我们考虑了一个多元离散时间马尔可夫模型，以分析中国33个省区的Covid-19的传播。这种方法使我们能够评估流动性限制政策对疾病传播的影响。我们使用跨地区每日人类流动性的数据，并应用贝叶斯框架来估计所提出的模型。结果表明，该疾病在中国的传播主要是由社区传播的地区传播驱动的，地方政府引入的锁定政策遏制了大流行的传播。此外，我们记录了湖北是早期流行病阶段的中心。北京和广东等次要中心已经在2020年1月下旬建立，该疾病分散到互联地区。在跨地区引入人类流动性限制之后，这些震中的传播显着下降。
Governments around the world have implemented preventive measures against the spread of the coronavirus disease (COVID-19). In this study, we consider a multivariate discrete-time Markov model to analyze the propagation of COVID-19 across 33 provincial regions in China. This approach enables us to evaluate the effect of mobility restriction policies on the spread of the disease. We use data on daily human mobility across regions and apply the Bayesian framework to estimate the proposed model. The results show that the spread of the disease in China was predominately driven by community transmission within regions and the lockdown policy introduced by local governments curbed the spread of the pandemic. Further, we document that Hubei was only the epicenter of the early epidemic stage. Secondary epicenters, such as Beijing and Guangdong, had already become established by late January 2020, and the disease spread out to connected regions. The transmission from these epicenters substantially declined following the introduction of human mobility restrictions across regions.
