Universal Urban Spreading Pattern of COVID-19 and Its Underlying Mechanism
Currently, the global situation of COVID-19 is aggravating, pressingly calling for efficient control and prevention measures. Understanding spreading pattern of COVID-19 has been widely recognized as a vital step for implementing non-pharmaceutical measures. Previous studies investigated such an issue in large-scale (e.g., inter-country or inter-state) scenarios while 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 9 cities in China. We find a universal spreading pattern in all cities: the spatial distribution of confirmed cases follows a power-law-like model and the spreading centroid is time-invariant. Moreover, we reveal that human mobility in a city drives the spatialtemporal spreading process: long average travelling distance results in a high growth rate of spreading radius and wide spatial diffusion of confirmed cases. With such insight, we adopt Kendall model to simulate urban spreading of COVID-19 that 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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Preprint| Erscheinungsjahr: |
2020 |
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| Erschienen: |
2020 |
| Enthalten in: |
arXiv.org - (2020) vom: 30. Dez. Zur Gesamtaufnahme - year:2020 |
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| Sprache: |
Englisch |
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| Beteiligte Personen: |
Zhang, Yongtao [VerfasserIn] |
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| Links: |
Volltext [kostenfrei] |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
XAR01964910X |
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| 520 | |a Currently, the global situation of COVID-19 is aggravating, pressingly calling for efficient control and prevention measures. Understanding spreading pattern of COVID-19 has been widely recognized as a vital step for implementing non-pharmaceutical measures. Previous studies investigated such an issue in large-scale (e.g., inter-country or inter-state) scenarios while 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 9 cities in China. We find a universal spreading pattern in all cities: the spatial distribution of confirmed cases follows a power-law-like model and the spreading centroid is time-invariant. Moreover, we reveal that human mobility in a city drives the spatialtemporal spreading process: long average travelling distance results in a high growth rate of spreading radius and wide spatial diffusion of confirmed cases. With such insight, we adopt Kendall model to simulate urban spreading of COVID-19 that 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. | ||
| 700 | 1 | |a Zhang, Hongshen |e verfasserin |4 aut | |
| 700 | 1 | |a Wu, Mincheng |e verfasserin |4 aut | |
| 700 | 1 | |a He, Shibo |e verfasserin |4 aut | |
| 700 | 1 | |a Fang, Yi |e verfasserin |4 aut | |
| 700 | 1 | |a Cheng, Yanggang |e verfasserin |4 aut | |
| 700 | 1 | |a Shi, Zhiguo |e verfasserin |4 aut | |
| 700 | 1 | |a Shao, Cunqi |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Chao |e verfasserin |4 aut | |
| 700 | 1 | |a Ying, Songmin |e verfasserin |4 aut | |
| 700 | 1 | |a Gong, Zhenyu |e verfasserin |4 aut | |
| 700 | 1 | |a Liu, Yu |e verfasserin |4 aut | |
| 700 | 1 | |a Ye, Xinjiang |e verfasserin |4 aut | |
| 700 | 1 | |a Chen, Jinlai |e verfasserin |4 aut | |
| 700 | 1 | |a Sun, Youxian |e verfasserin |4 aut | |
| 700 | 1 | |a Chen, Jiming |e verfasserin |4 aut | |
| 700 | 1 | |a Stanley, H. Eugene |e verfasserin |4 aut | |
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