Department of Econometrics and Business Statistics, Monash University, Caulfield, Victoria, Australia.
PLoS One. 2021 Jul 19;16(7):e0254403. doi: 10.1371/journal.pone.0254403. eCollection 2021.
COVID-19 poses a severe threat worldwide. This study analyzes its propagation and evaluates statistically the effect of mobility restriction policies on the spread of the disease.
We apply a variation of the stochastic Susceptible-Infectious-Recovered model to describe the temporal-spatial evolution of the disease across 33 provincial regions in China, where the disease was first identified. We employ Bayesian Markov Chain Monte-Carlo methods to estimate the model and to characterize a dynamic transmission network, which enables us to evaluate the effectiveness of various local and national policies.
The spread of the disease in China was predominantly driven by community transmission within regions, which dropped substantially after local governments imposed various lockdown policies. Further, 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. The transmission from these epicenters substantially declined following the introduction of mobility restrictions across regions.
The spatial transmission network is able to differentiate the effect of the local lockdown policies and the cross-region mobility restrictions. We conclude that both are important policy tools for curbing the disease transmission. The coordination between central and local governments is important in suppressing the spread of infectious diseases.
COVID-19 在全球范围内构成严重威胁。本研究分析了其传播情况，并从统计学角度评估了流动性限制政策对疾病传播的影响。
我们应用随机传染病恢复模型的变体来描述疾病在中国 33 个省级行政区的时空演变，疾病最初就是在这些地区被发现的。我们采用贝叶斯马尔可夫链蒙特卡罗方法来估计模型，并对动态传播网络进行特征描述，这使我们能够评估各种地方和国家政策的有效性。
疾病在中国的传播主要是由区域内的社区传播驱动的，地方政府实施各种封锁政策后，这种传播大幅下降。此外，湖北只是疫情早期的震中。到 2020 年 1 月底，北京和广东等二级震中已经出现。在实施跨区域流动性限制后，来自这些震中的传播大幅减少。
空间传播网络能够区分地方封锁政策和跨区域流动性限制的效果。我们的结论是，两者都是抑制疾病传播的重要政策工具。中央和地方政府之间的协调对于抑制传染病的传播至关重要。
