Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1073/pnas.2109098118 |
Predicting the effect of confinement on the COVID-19 spread using machine learning enriched with satellite air pollution observations | |
Xiaofan Xing; Yuankang Xiong; Ruipu Yang; Rong Wang; Weibing Wang; Haidong Kan; Tun Lu; Dongsheng Li; Junji Cao; Josep Peñuelas; Philippe Ciais; Nico Bauer; Olivier Boucher; Yves Balkanski; Didier Hauglustaine; Guy Brasseur; Lidia Morawska; Ivan A. Janssens; Xiangrong Wang; Jordi Sardans; Yijing Wang; Yifei Deng; Lin Wang; Jianmin Chen; Xu Tang; Renhe Zhang | |
2021-08-17 | |
发表期刊 | Proceedings of the National Academy of Sciences |
出版年 | 2021 |
英文摘要 | The real-time monitoring of reductions of economic activity by containment measures and its effect on the transmission of the coronavirus (COVID-19) is a critical unanswered question. We inferred 5,642 weekly activity anomalies from the meteorology-adjusted differences in spaceborne tropospheric NO2 column concentrations after the 2020 COVID-19 outbreak relative to the baseline from 2016 to 2019. Two satellite observations reveal reincreasing economic activity associated with lifting control measures that comes together with accelerating COVID-19 cases before the winter of 2020/2021. Application of the near-real-time satellite NO2 observations produces a much better prediction of the deceleration of COVID-19 cases than applying the Oxford Government Response Tracker, the Public Health and Social Measures, or human mobility data as alternative predictors. A convergent cross-mapping suggests that economic activity reduction inferred from NO2 is a driver of case deceleration in most of the territories. This effect, however, is not linear, while further activity reductions were associated with weaker deceleration. Over the winter of 2020/2021, nearly 1 million daily COVID-19 cases could have been avoided by optimizing the timing and strength of activity reduction relative to a scenario based on the real distribution. Our study shows how satellite observations can provide surrogate data for activity reduction during the COVID-19 pandemic and monitor the effectiveness of containment to the pandemic before vaccines become widely available. |
领域 | 资源环境 |
URL | 查看原文 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/335824 |
专题 | 资源环境科学 |
推荐引用方式 GB/T 7714 | Xiaofan Xing,Yuankang Xiong,Ruipu Yang,et al. Predicting the effect of confinement on the COVID-19 spread using machine learning enriched with satellite air pollution observations[J]. Proceedings of the National Academy of Sciences,2021. |
APA | Xiaofan Xing.,Yuankang Xiong.,Ruipu Yang.,Rong Wang.,Weibing Wang.,...&Renhe Zhang.(2021).Predicting the effect of confinement on the COVID-19 spread using machine learning enriched with satellite air pollution observations.Proceedings of the National Academy of Sciences. |
MLA | Xiaofan Xing,et al."Predicting the effect of confinement on the COVID-19 spread using machine learning enriched with satellite air pollution observations".Proceedings of the National Academy of Sciences (2021). |
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