A general urban spreading pattern of COVID-19 and its underlying mechanism.
A general urban spreading pattern of COVID-19 and its underlying mechanism.
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DOI：
10.1038/s42949-023-00082-4
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发表时间：
2023
期刊：
影响因子：
--
通讯作者：
Chen, Jiming
中科院分区：
文献类型：
--
作者：
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
当前，全球新冠肺炎疫情形势严峻，迫切需要采取有效的控制和预防措施。了解 COVID-19 的传播模式已被广泛认为是实施非药物措施的重要一步。先前的研究解释了城市社会政治措施导致的传染率差异，而细粒度的地理城市传播模式仍然是一个悬而未决的问题。在这里，我们利用中国九个城市的 197,808 名智能手机用户（包括 17,808 名匿名确诊病例）的轨迹数据来填补这一空白。我们发现所有城市都有一个普遍的传播模式：确诊病例的空间分布遵循幂律模型，并且传播质心的人口流动性是时不变的。此外，我们发现，在细粒度地理模型中，平均旅行距离长会导致传播半径的高增长率和确诊病例的广泛空间扩散。有了这样的洞察，我们采用Kendall模型来模拟COVID-19的城市传播，它可以很好地贴合真实的传播过程。我们的研究结果揭示了COVID-19城市时空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绩效并估计COVID-19不断演变的传播情况。
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.
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影响因子：
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DOI：
10.1073/pnas.2103272118
发表时间：
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影响因子：
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