Cheng, Tao 
ORCID: 0000-0002-5503-9813, Lu, Tianhua, Liu, Yunzhe ORCID: 0000-0002-7189-3323, Gao, Xiaowei ORCID: 0000-0003-3273-7499 and Zhang, Xianghui ORCID: 0000-0002-4173-9990
(2021)
Revealing spatiotemporal transmission patterns and stages of COVID-19 in China using individual patients' trajectory data.
Computational urban science, 1 (1).
9-.
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Abstract
Gauging viral transmission through human mobility in order to contain the COVID-19 pandemic has been a hot topic in academic studies and evidence-based policy-making. Although it is widely accepted that there is a strong positive correlation between the transmission of the coronavirus and the mobility of the general public, there are limitations to existing studies on this topic. For example, using digital proxies of mobile devices/apps may only partially reflect the movement of individuals; using the mobility of the general public and not COVID-19 patients in particular, or only using places where patients were diagnosed to study the spread of the virus may not be accurate; existing studies have focused on either the regional or national spread of COVID-19, and not the spread at the city level; and there are no systematic approaches for understanding the stages of transmission to facilitate the policy-making to contain the spread. To address these issues, we have developed a new methodological framework for COVID-19 transmission analysis based upon individual patients' trajectory data. By using innovative space-time analytics, this framework reveals the spatiotemporal patterns of patients' mobility and the transmission stages of COVID-19 from Wuhan to the rest of China at finer spatial and temporal scales. It can improve our understanding of the interaction of mobility and transmission, identifying the risk of spreading in small and medium-sized cities that have been neglected in existing studies. This demonstrates the effectiveness of the proposed framework and its policy implications to contain the COVID-19 pandemic.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | COVID-19, Patient trajectory, Spatiotemporal data mining, Viral transmission |
| Divisions: | Faculty of Science and Engineering > School of Environmental Sciences |
| Depositing User: | Symplectic Admin |
| Date Deposited: | 17 Aug 2021 07:18 |
| Last Modified: | 18 Jan 2023 21:33 |
| DOI: | 10.1007/s43762-021-00009-8 |
| Related URLs: | |
| URI: | https://livrepository.liverpool.ac.uk/id/eprint/3133763 |
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