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

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Xing,  Xiaofan
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Xiong,  Yuankang
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Yang,  Ruipu
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Wang,  Rong
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Wang,  Weibing
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Kan,  Haidong
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Lu,  Tun
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Li,  Dongsheng
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Cao,  Junji
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Peñuelas,  Josep
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Ciais,  Philippe
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/persons/resource/Nicolas.Bauer

Bauer,  Nicolas
Potsdam Institute for Climate Impact Research;

Boucher,  Olivier
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Balkanski,  Yves
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Hauglustaine,  Didier
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Brasseur,  Guy
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Morawska,  Lidia
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Janssens,  Ivan A.
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Wang,  Xiangrong
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Sardans,  Jordi
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Wang,  Yijing
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Deng,  Yifei
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Wang,  Lin
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Chen,  Jianmin
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Tang,  Xu
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Zhang,  Renhe
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Xing, X., Xiong, Y., Yang, R., Wang, R., Wang, W., Kan, H., Lu, T., Li, D., Cao, J., Peñuelas, J., Ciais, P., Bauer, N., Boucher, O., Balkanski, Y., Hauglustaine, D., Brasseur, G., Morawska, L., Janssens, I. A., Wang, X., Sardans, J., Wang, Y., Deng, Y., Wang, L., Chen, J., Tang, X., Zhang, R. (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 of the United States of America (PNAS), 118, 33, e2109098118.
https://doi.org/10.1073/pnas.2109098118


???ViewItemOverview_lblCiteAs???: https://publications.pik-potsdam.de/pubman/item/item_26725
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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.