| 来源类型 | Working Paper |
| 规范类型 | 报告 |
| DOI | 10.3386/w28120 |
| 来源ID | Working Paper 28120 |
| Public Mobility Data Enables COVID-19 Forecasting and Management at Local and Global Scales | |
| Cornelia Ilin; Sébastien E. Annan-Phan; Xiao Hui Tai; Shikhar Mehra; Solomon M. Hsiang; Joshua E. Blumenstock | |
| 发表日期 | 2020-11-23 |
| 出版年 | 2020 |
| 语种 | 英语 |
| 摘要 | Policymakers everywhere are working to determine the set of restrictions that will effectively contain the spread of COVID-19 without excessively stifling economic activity. We show that publicly available data on human mobility — collected by Google, Facebook, and other providers — can be used to evaluate the effectiveness of non-pharmaceutical interventions and forecast the spread of COVID-19. This approach relies on simple and transparent statistical models, and involves minimal assumptions about disease dynamics. We demonstrate the effectiveness of this approach using local and regional data from China, France, Italy, South Korea, and the United States, as well as national data from 80 countries around the world. |
| 主题 | Econometrics ; Estimation Methods ; Data Collection ; Public Economics ; Subnational Fiscal Issues ; Health, Education, and Welfare ; Health ; Development and Growth ; Regional and Urban Economics ; COVID-19 |
| URL | https://www.nber.org/papers/w28120 |
| 来源智库 | National Bureau of Economic Research (United States) |
| 引用统计 | |
| 资源类型 | 智库出版物 |
| 条目标识符 | http://119.78.100.153/handle/2XGU8XDN/585794 |
| 推荐引用方式 GB/T 7714 | Cornelia Ilin,Sébastien E. Annan-Phan,Xiao Hui Tai,et al. Public Mobility Data Enables COVID-19 Forecasting and Management at Local and Global Scales. 2020. |
| 条目包含的文件 | ||||||
| 文件名称/大小 | 资源类型 | 版本类型 | 开放类型 | 使用许可 | ||
| w28120.pdf（4302KB） | 智库出版物 | 限制开放 | CC BY-NC-SA | 浏览 | ||
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