English
 
Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
  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

Item is

Files

show Files
hide Files
:
e2109098118.full.pdf (Publisher version), 4MB
 
File Permalink:
-
Name:
e2109098118.full.pdf
Description:
-
Visibility:
Private
MIME-Type / Checksum:
application/pdf
Technical Metadata:
Copyright Date:
-
Copyright Info:
-
License:
-

Locators

show

Creators

show
hide
 Creators:
Xing, Xiaofan1, Author
Xiong, Yuankang1, Author
Yang, Ruipu1, Author
Wang, Rong1, Author
Wang, Weibing1, Author
Kan, Haidong1, Author
Lu, Tun1, Author
Li, Dongsheng1, Author
Cao, Junji1, Author
Peñuelas, Josep1, Author
Ciais, Philippe1, Author
Bauer, Nicolas2, Author              
Boucher, Olivier1, Author
Balkanski, Yves1, Author
Hauglustaine, Didier1, Author
Brasseur, Guy1, Author
Morawska, Lidia1, Author
Janssens, Ivan A.1, Author
Wang, Xiangrong1, Author
Sardans, Jordi1, Author
Wang, Yijing1, AuthorDeng, Yifei1, AuthorWang, Lin1, AuthorChen, Jianmin1, AuthorTang, Xu1, AuthorZhang, Renhe1, Author more..
Affiliations:
1External Organizations, ou_persistent22              
2Potsdam Institute for Climate Impact Research, ou_persistent13              

Content

show
hide
Free keywords: -
 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.

Details

show
hide
Language(s):
 Dates: 2021-08-112021-08-17
 Publication Status: Finally published
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1073/pnas.2109098118
PIKDOMAIN: RD3 - Transformation Pathways
Organisational keyword: RD3 - Transformation Pathways
MDB-ID: No data to archive
 Degree: -

Event

show

Legal Case

show

Project information

show

Source 1

show
hide
Title: Proceedings of the National Academy of Sciences of the United States of America (PNAS)
Source Genre: Journal, SCI, Scopus, p3
 Creator(s):
Affiliations:
Publ. Info: -
Pages: - Volume / Issue: 118 (33) Sequence Number: e2109098118 Start / End Page: - Identifier: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/journals410
Publisher: National Academy of Sciences (NAS)