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Predicting the effect of confinement on the COVID-19 spread using machine learning enriched with satellite air pollution observations.

ABSTRACT: 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.

SUBMITTER: Xing X

PROVIDER: S-EPMC8379976 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature

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Predicting the effect of confinement on the COVID-19 spread using machine learning enriched with satellite air pollution observations.

Xing Xiaofan X Xiong Yuankang Y Yang Ruipu R Wang Rong R Wang Weibing W Kan Haidong H Lu Tun T Li Dongsheng D Cao Junji J Peñuelas Josep J Ciais Philippe P Bauer Nico N Boucher Olivier O Balkanski Yves Y Hauglustaine Didier D Brasseur Guy G Morawska Lidia L Janssens Ivan A IA Wang Xiangrong X Sardans Jordi J Wang Yijing Y Deng Yifei Y Wang Lin L Chen Jianmin J Tang Xu X Zhang Renhe R

Proceedings of the National Academy of Sciences of the United States of America 20210801 33

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 NO<sub>2</sub> 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 ...[more]

PMID: 34380740

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Predicting the effect of confinement on the COVID-19 spread using machine learning enriched with satellite air pollution observations.

Publications

Predicting the effect of confinement on the COVID-19 spread using machine learning enriched with satellite air pollution observations.

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