Spatiotemporal infection dynamics: Linking individual movement patterns to infection status
文献类型：期刊论文
| 作者 | Yan, Xiaorui4,5; Song, Ci4,5; Pei, Tao3,4,5; Ge, Erjia2; Liu, Le4,5; Wang, Xi4,5; Jiang, Linfeng1 |
| 刊名 | CITIES
 |
| 出版日期 | 2024-07-01 |
| 卷号 | 150页码:104932 |
| 关键词 | Post-zero-COVID Individual mobility status Infection dynamics Mobile phone signaling data Beijing |
| DOI | 10.1016/j.cities.2024.104932 |
| 产权排序 | 1 |
| 文献子类 | Article |
| 英文摘要 | The swift relaxation of the zero-COVID policy in late 2022 led to an unprecedented surge in Omicron variant infections in many cities of China. Reconstructing the spatiotemporal spread of infections is crucial for effective disease prevention. However, the challenge arose due to limited data from surveys and testing results. As such, we utilized large-scale mobile phone data to estimate daily infections in Beijing from November 2022 to January 2023. Our study demonstrated that an individual's mobility status (staying home or going outside), inferred from long-term mobile phone signaling data, could indicate his or her infection status. Then, the inferred statuses of millions of individuals could be summed to reconstruct the citywide spatiotemporal dynamics of infections. We found that the infection incidence peaked on 21 December, and 80.1 % of population had been infected by 14 January 2023 in Beijing. Furthermore, infection dynamics exhibited significant demographic and spatiotemporal disparities, with urban centers experiencing faster initial increases compared to suburbs. Our work provides a new viewpoint for sensing the epidemic spatiotemporally from residents' mobility patterns in a city when official measures of confirming cases are not available, and our findings facilitate city policymaking in terms of relaxing containment measures. |
| WOS研究方向 | Urban Studies |
| WOS记录号 | WOS:001234801500001 |
| 出版者 | ELSEVIER SCI LTD |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/205297]  |
| 专题 | 资源与环境信息系统国家重点实验室_外文论文 |
| 通讯作者 | Pei, Tao |
| 作者单位 | 1.Lanzhou Univ, Coll Earth & Environm Sci, Lanzhou, Peoples R China 2.Univ Toronto, Dalla Lana Sch Publ Hlth, Toronto, ON, Canada 3.Jiangsu Ctr Collaborat Innovat, Geog Informat Resource Dev & Applicat, Nanjing, Peoples R China 4.Univ Chinese Acad Sci, Beijing, Peoples R China 5.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing, Peoples R China |
| 推荐引用方式 GB/T 7714 | Yan, Xiaorui,Song, Ci,Pei, Tao,et al. Spatiotemporal infection dynamics: Linking individual movement patterns to infection status[J]. CITIES,2024,150:104932. |
| APA | Yan, Xiaorui.,Song, Ci.,Pei, Tao.,Ge, Erjia.,Liu, Le.,...&Jiang, Linfeng.(2024).Spatiotemporal infection dynamics: Linking individual movement patterns to infection status.CITIES,150,104932. |
| MLA | Yan, Xiaorui,et al."Spatiotemporal infection dynamics: Linking individual movement patterns to infection status".CITIES 150(2024):104932. |
入库方式： OAI收割
来源：地理科学与资源研究所
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