Policy and newly confirmed cases universally shape the human mobility during COVID-19
Understanding how human mobility pattern changes during the COVID-19 is of great importance in controlling the transmission of the pandemic. This pattern seems unpredictable due to the complex social contexts, individual behaviors, and limited data. We analyze the human mobility data of over 10 mill...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Science Press
2022-05-01
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| Series: | National Science Open |
| Subjects: | |
| Online Access: | https://www.sciengine.com/doi/10.1360/nso/20220003 |
| _version_ | 1826550639059009536 |
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| author | Li Kehan Li Chao Xiang Yinfeng He Fengxiang He Shibo Chen Jiming Fang Yi Sun Youxian |
| author_facet | Li Kehan Li Chao Xiang Yinfeng He Fengxiang He Shibo Chen Jiming Fang Yi Sun Youxian |
| author_sort | Li Kehan |
| collection | DOAJ |
| description | Understanding how human mobility pattern changes during the COVID-19 is of great importance in controlling the transmission of the pandemic. This pattern seems unpredictable due to the complex social contexts, individual behaviors, and limited data. We analyze the human mobility data of over 10 million smart devices in three major cities in China from January 2020 to March 2021. We find that the human mobility across multi-waves of epidemics presents a surprisingly similar pattern in these three cities, despite their significant gaps in geographic environments and epidemic intensities. In particular, we reveal that the COVID-19 policies and statistics (i.e., confirmed cases) dominate human mobility during the pandemic. Thus, we propose a universal conditional generative adversarial network based framework to estimate human mobility, integrating COVID-19 statistics and policies via a gating fusion module. Extensive numerical experiments demonstrate that our model is generalizable for estimating human mobility dynamics accurately across three cities with multi-waves of COVID-19. Beyond, our model also allows policymakers to better evaluate the potential influences of various policies on human mobility and mitigate the unprecedented and disruptive pandemic. |
| first_indexed | 2024-03-12T22:53:40Z |
| format | Article |
| id | doaj.art-17f45e970fab43719d9846ae95fe4d67 |
| institution | Directory Open Access Journal |
| issn | 2097-1168 |
| language | English |
| last_indexed | 2025-03-14T06:40:32Z |
| publishDate | 2022-05-01 |
| publisher | Science Press |
| record_format | Article |
| series | National Science Open |
| spelling | doaj.art-17f45e970fab43719d9846ae95fe4d672025-03-05T01:27:06ZengScience PressNational Science Open2097-11682022-05-01110.1360/nso/20220003eb33e642Policy and newly confirmed cases universally shape the human mobility during COVID-19Li Kehan0Li Chao1Xiang Yinfeng2He Fengxiang3He Shibo4Chen Jiming5Fang Yi6Sun Youxian7["College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China","JD Explore Academy, JD.com, Beijing 100176, China"]["College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China"]["College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China"]["JD Explore Academy, JD.com, Beijing 100176, China"]["College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China"]["College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China"]["Westlake Institute for Data Intelligence, Hangzhou 310012, China"]["College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China"]Understanding how human mobility pattern changes during the COVID-19 is of great importance in controlling the transmission of the pandemic. This pattern seems unpredictable due to the complex social contexts, individual behaviors, and limited data. We analyze the human mobility data of over 10 million smart devices in three major cities in China from January 2020 to March 2021. We find that the human mobility across multi-waves of epidemics presents a surprisingly similar pattern in these three cities, despite their significant gaps in geographic environments and epidemic intensities. In particular, we reveal that the COVID-19 policies and statistics (i.e., confirmed cases) dominate human mobility during the pandemic. Thus, we propose a universal conditional generative adversarial network based framework to estimate human mobility, integrating COVID-19 statistics and policies via a gating fusion module. Extensive numerical experiments demonstrate that our model is generalizable for estimating human mobility dynamics accurately across three cities with multi-waves of COVID-19. Beyond, our model also allows policymakers to better evaluate the potential influences of various policies on human mobility and mitigate the unprecedented and disruptive pandemic.https://www.sciengine.com/doi/10.1360/nso/20220003COVID-19human mobilitygenerative adversarial network |
| spellingShingle | Li Kehan Li Chao Xiang Yinfeng He Fengxiang He Shibo Chen Jiming Fang Yi Sun Youxian Policy and newly confirmed cases universally shape the human mobility during COVID-19 National Science Open COVID-19 human mobility generative adversarial network |
| title | Policy and newly confirmed cases universally shape the human mobility during COVID-19 |
| title_full | Policy and newly confirmed cases universally shape the human mobility during COVID-19 |
| title_fullStr | Policy and newly confirmed cases universally shape the human mobility during COVID-19 |
| title_full_unstemmed | Policy and newly confirmed cases universally shape the human mobility during COVID-19 |
| title_short | Policy and newly confirmed cases universally shape the human mobility during COVID-19 |
| title_sort | policy and newly confirmed cases universally shape the human mobility during covid 19 |
| topic | COVID-19 human mobility generative adversarial network |
| url | https://www.sciengine.com/doi/10.1360/nso/20220003 |
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