Chen Xingru, Fu Feng
School of Sciences, Beijing University of Posts and Telecommunications, Beijing 100876, China.
Department of Mathematics, Dartmouth College, Hanover, NH 03755, USA.
ArXiv. 2022 Jan 7:arXiv:2201.02353v1.
The COVID-19, the disease caused by the novel coronavirus 2019 (SARS-CoV-2), has caused graving woes across the globe since first reported in the epicenter Wuhan, Hubei, China, December 2019. The spread of COVID-19 in China has been successfully curtailed by massive travel restrictions that put more than 900 million people housebound for more than two months since the lockdown of Wuhan on 23 January 2020 when other provinces in China followed suit. Here, we assess the impact of China's massive lockdowns and travel restrictions reflected by the changes in mobility patterns before and during the lockdown period. We quantify the synchrony of mobility patterns across provinces and within provinces. Using these mobility data, we calibrate movement flow between provinces in combination with an epidemiological compartment model to quantify the effectiveness of lockdowns and reductions in disease transmission. Our analysis demonstrates that the onset and phase of local community transmission in other provinces depends on the cumulative population outflow received from the epicenter Hubei. As such, infections can propagate further into other interconnected places both near and far, thereby necessitating synchronous lockdowns. Moreover, our data-driven modeling analysis shows that lockdowns and consequently reduced mobility lag a certain time to elicit an actual impact on slowing down the spreading and ultimately putting the epidemic under check. In spite of the vastly heterogeneous demographics and epidemiological characteristics across China, mobility data shows that massive travel restrictions have been applied consistently via a top-down approach along with high levels of compliance from the bottom up.
新型冠状病毒2019(SARS-CoV-2)引发的COVID-19自2019年12月在中国湖北省武汉市这一疫情中心首次报告以来,已在全球范围内造成严重灾难。自2020年1月23日武汉封城且中国其他省份纷纷效仿以来,大规模的出行限制措施成功遏制了COVID-19在中国的传播,这些措施使9亿多人居家两个多月。在此,我们评估中国大规模封城和出行限制措施的影响,这一影响通过封城前后出行模式的变化得以体现。我们量化了各省之间以及省内出行模式的同步性。利用这些出行数据,我们结合流行病学 compartments 模型校准各省之间的人员流动,以量化封城措施和疾病传播减少的有效性。我们的分析表明,其他省份本地社区传播的起始和阶段取决于从疫情中心湖北省流出的累计人口数量。因此,感染可能会进一步传播到远近相连的其他地方,从而需要同步封城。此外,我们基于数据的建模分析表明,封城以及随之而来的出行减少需要一定时间才能对减缓传播并最终控制疫情产生实际影响。尽管中国各地的人口统计学特征和流行病学特征差异巨大,但出行数据显示,大规模的出行限制措施一直通过自上而下的方式统一实施,并且自下而上的民众遵从度很高。