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Journal Article

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

Authors

Xing,  Xiaofan
External Organizations;

Xiong,  Yuankang
External Organizations;

Yang,  Ruipu
External Organizations;

Wang,  Rong
External Organizations;

Wang,  Weibing
External Organizations;

Kan,  Haidong
External Organizations;

Lu,  Tun
External Organizations;

Li,  Dongsheng
External Organizations;

Cao,  Junji
External Organizations;

Peñuelas,  Josep
External Organizations;

Ciais,  Philippe
External Organizations;

/persons/resource/Nicolas.Bauer

Bauer,  Nicolas
Potsdam Institute for Climate Impact Research;

Boucher,  Olivier
External Organizations;

Balkanski,  Yves
External Organizations;

Hauglustaine,  Didier
External Organizations;

Brasseur,  Guy
External Organizations;

Morawska,  Lidia
External Organizations;

Janssens,  Ivan A.
External Organizations;

Wang,  Xiangrong
External Organizations;

Sardans,  Jordi
External Organizations;

Wang,  Yijing
External Organizations;

Deng,  Yifei
External Organizations;

Wang,  Lin
External Organizations;

Chen,  Jianmin
External Organizations;

Tang,  Xu
External Organizations;

Zhang,  Renhe
External Organizations;

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Citation

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


Cite as: https://publications.pik-potsdam.de/pubman/item/item_26725
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.