Zhang Hongshen, Zhang Yongtao, He Shibo, Fang Yi, Cheng Yanggang, Shi Zhiguo, Shao Cunqi, Li Chao, Ying Songmin, Gong Zhenyu, Liu Yu, Dong Lin, Sun Youxian, Jia Jianmin, Stanley H Eugene, Chen Jiming
College of Control Science and Engineering, Zhejiang University, Hangzhou, China.
Westlake Institute for Data Intelligence, Hangzhou, China.
NPJ Urban Sustain. 2023;3(1):3. doi: 10.1038/s42949-023-00082-4. Epub 2023 Jan 28.
Currently, the global situation of COVID-19 is aggravating, pressingly calling for efficient control and prevention measures. Understanding the spreading pattern of COVID-19 has been widely recognized as a vital step for implementing non-pharmaceutical measures. Previous studies explained the differences in contagion rates due to the urban socio-political measures, while fine-grained geographic urban spreading pattern still remains an open issue. Here, we fill this gap by leveraging the trajectory data of 197,808 smartphone users (including 17,808 anonymous confirmed cases) in nine cities in China. We find a general spreading pattern in all cities: the spatial distribution of confirmed cases follows a power-law-like model and the spreading centroid human mobility is time-invariant. Moreover, we reveal that long average traveling distance results in a high growth rate of spreading radius and wide spatial diffusion of confirmed cases in the fine-grained geographic model. With such insight, we adopt the Kendall model to simulate the urban spreading of COVID-19 which can well fit the real spreading process. Our results unveil the underlying mechanism behind the spatial-temporal urban evolution of COVID-19, and can be used to evaluate the performance of mobility restriction policies implemented by many governments and to estimate the evolving spreading situation of COVID-19.
当前，新冠疫情全球形势日益严峻，亟需高效的防控措施。了解新冠病毒的传播模式已被广泛认为是实施非药物措施的关键一步。此前的研究解释了城市社会政治措施导致的传染率差异，而城市细粒度地理传播模式仍是一个悬而未决的问题。在此，我们利用中国九个城市197,808名智能手机用户（包括17,808名匿名确诊病例）的轨迹数据填补了这一空白。我们发现所有城市都存在一种普遍的传播模式：确诊病例的空间分布遵循类似幂律的模型，传播中心的人口流动在时间上是不变的。此外，我们还揭示，在细粒度地理模型中，平均出行距离长会导致传播半径增长率高，确诊病例的空间扩散范围广。基于这一见解，我们采用肯德尔模型来模拟新冠疫情在城市中的传播，该模型能够很好地拟合实际传播过程。我们的研究结果揭示了新冠疫情时空城市演变背后的潜在机制，可用于评估许多政府实施的流动限制政策的效果，并估计新冠疫情不断演变的传播情况。
