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National Academy of Sciences, Proceedings of the National Academy of Sciences, 33(118), 2021

DOI: 10.1073/pnas.2109098118

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

Journal article published in 2021 by Xiaofan Xing ORCID, Yuankang Xiong, Ruipu Yang, Rong Wang ORCID, Weibing Wang ORCID, Haidong Kan, Tun Lu, Dongsheng Li ORCID, Junji Cao, Josep Peñuelas ORCID, Philippe Ciais ORCID, Nico Bauer ORCID, Olivier Boucher, Yves Balkanski ORCID, Didier Hauglustaine and other authors.
This paper is made freely available by the publisher.
This paper is made freely available by the publisher.

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Abstract

Significance There is a lack of global observational data to monitor the application of nonpharmaceutical interventions (NPI) and evaluate the effect of NPIs for pandemic management in different territories. We find that economic activity reduction inferred from NO 2 is a driver of case deceleration in most of the territories. The effect, however, is not linear but dampens over time, and further reductions are only 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 in different regions relative to a scenario based on the real distribution.