Data-Driven Models Informed by Spatiotemporal Mobility Patterns for Understanding Infectious Disease Dynamics
文献类型：期刊论文
| 作者 | Zhang, Die; Ge, Yong; Wu, Xilin; Liu, Haiyan; Zhang, Wenbin; Lai, Shengjie |
| 刊名 | ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION
 |
| 出版日期 | 2023-07-01 |
| 卷号 | 12期号:7页码:266 |
| 关键词 | human mobility emerging infectious disease COVID-19 disease containment surveillance |
| ISSN号 | 2220-9964 |
| DOI | 10.3390/ijgi12070266 |
| 产权排序 | 1 |
| 文献子类 | Article |
| 英文摘要 | Data-driven approaches predict infectious disease dynamics by considering various factors that influence severity and transmission rates. However, these factors may not fully capture the dynamic nature of disease transmission, limiting prediction accuracy and consistency. Our proposed data-driven approach integrates spatiotemporal human mobility patterns from detailed point-of-interest clustering and population flow data. These patterns inform the creation of mobility-informed risk indices, which serve as auxiliary factors in data-driven models for detecting outbreaks and predicting prevalence trends. We evaluated our approach using real-world COVID-19 outbreaks in Beijing and Guangzhou, China. Incorporating the risk indices, our models successfully identified 87% (95% Confidence Interval: 83-90%) of affected subdistricts in Beijing and Guangzhou. These findings highlight the effectiveness of our approach in identifying high-risk areas for targeted disease containment. Our approach was also tested with COVID-19 prevalence data in the United States, which showed that including the risk indices reduced the mean absolute error and improved the R-squared value for predicting weekly case increases at the county level. It demonstrates applicability for spatiotemporal forecasting of widespread diseases, contributing to routine transmission surveillance. By leveraging comprehensive mobility data, we provide valuable insights to optimize control strategies for emerging infectious diseases and facilitate proactive measures against long-standing diseases. |
| WOS关键词 | COVID-19 |
| WOS研究方向 | Computer Science ; Physical Geography ; Remote Sensing |
| 语种 | 英语 |
| WOS记录号 | WOS:001038631300001 |
| 出版者 | MDPI |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/194575]  |
| 专题 | 资源与环境信息系统国家重点实验室_外文论文 |
| 作者单位 | 1.Southern Marine Science & Engineering Guangdong Laboratory 2.Fudan University 3.University of Southampton 4.Southern Marine Science & Engineering Guangdong Laboratory (Zhuhai) 5.University of Chinese Academy of Sciences, CAS 6.Institute of Geographic Sciences & Natural Resources Research, CAS 7.Chinese Academy of Sciences |
| 推荐引用方式 GB/T 7714 | Zhang, Die,Ge, Yong,Wu, Xilin,et al. Data-Driven Models Informed by Spatiotemporal Mobility Patterns for Understanding Infectious Disease Dynamics[J]. ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION,2023,12(7):266. |
| APA | Zhang, Die,Ge, Yong,Wu, Xilin,Liu, Haiyan,Zhang, Wenbin,&Lai, Shengjie.(2023).Data-Driven Models Informed by Spatiotemporal Mobility Patterns for Understanding Infectious Disease Dynamics.ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION,12(7),266. |
| MLA | Zhang, Die,et al."Data-Driven Models Informed by Spatiotemporal Mobility Patterns for Understanding Infectious Disease Dynamics".ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION 12.7(2023):266. |
入库方式： OAI收割
来源：地理科学与资源研究所
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