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

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

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Xing, Xiaofan1, Autor
Xiong, Yuankang1, Autor
Yang, Ruipu1, Autor
Wang, Rong1, Autor
Wang, Weibing1, Autor
Kan, Haidong1, Autor
Lu, Tun1, Autor
Li, Dongsheng1, Autor
Cao, Junji1, Autor
Peñuelas, Josep1, Autor
Ciais, Philippe1, Autor
Bauer, Nicolas2, Autor              
Boucher, Olivier1, Autor
Balkanski, Yves1, Autor
Hauglustaine, Didier1, Autor
Brasseur, Guy1, Autor
Morawska, Lidia1, Autor
Janssens, Ivan A.1, Autor
Wang, Xiangrong1, Autor
Sardans, Jordi1, Autor
Wang, Yijing1, AutorDeng, Yifei1, AutorWang, Lin1, AutorChen, Jianmin1, AutorTang, Xu1, AutorZhang, Renhe1, Autor mehr..
Affiliations:
1External Organizations, ou_persistent22              
2Potsdam Institute for Climate Impact Research, ou_persistent13              

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 Zusammenfassung: 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.

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 Datum: 2021-08-112021-08-17
 Publikationsstatus: Final veröffentlicht
 Seiten: -
 Ort, Verlag, Ausgabe: -
 Inhaltsverzeichnis: -
 Art der Begutachtung: Expertenbegutachtung
 Identifikatoren: DOI: 10.1073/pnas.2109098118
PIKDOMAIN: RD3 - Transformation Pathways
Organisational keyword: RD3 - Transformation Pathways
MDB-ID: No data to archive
 Art des Abschluß: -

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Titel: Proceedings of the National Academy of Sciences of the United States of America (PNAS)
Genre der Quelle: Zeitschrift, SCI, Scopus, p3
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Seiten: - Band / Heft: 118 (33) Artikelnummer: e2109098118 Start- / Endseite: - Identifikator: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/journals410
Publisher: National Academy of Sciences (NAS)