1. Predicting the effect of confinement on the COVID-19 spread using machine learning enriched with satellite air pollution observations
- Author
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Xu Tang, Jianmin Chen, Tun Lu, Haidong Kan, Philippe Ciais, Renhe Zhang, Ivan A. Janssens, Rong Wang, Jordi Sardans, Lin Wang, Xiaofan Xing, Josep Peñuelas, Xiangrong Wang, Didier A. Hauglustaine, Ruipu Yang, Yves Balkanski, Yijing Wang, Weibing Wang, Lidia Morawska, Guy Brasseur, Junji Cao, Yuankang Xiong, Nico Bauer, Dongsheng Li, Yifei Deng, Olivier Boucher, Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention (LAP3), Fudan University [Shanghai], Microsoft Research Asia, Institute of Atmospheric Physics [Beijing] (IAP), Chinese Academy of Sciences [Beijing] (CAS), CREAF - Centre for Ecological Research and Applied Forestries, Laboratoire des Sciences du Climat et de l'Environnement [Gif-sur-Yvette] (LSCE), Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ), Potsdam Institute for Climate Impact Research (PIK), University of Antwerp (UA), Max Planck Institute for Meteorology (MPI-M), Max-Planck-Gesellschaft, International Laboratory for Air Quality and Health [Brisbane], Queensland University of Technology [Brisbane] (QUT), ANR-15-CE04-0005, European Space Agency, ESA: 725546, ESRIN/4000123002/18/I-NB, National Natural Science Foundation of China, NSFC: 18107, 41877506, Generalitat de Catalunya: AGAUR-2020PANDE00117, ACKNOWLEDGMENTS. We thank H. J. Yu for important comments and J. H. Pan for help in compiling the epidemiological data. This work was supported by the National Natural Science Foundation of China (41877506), the Fudan’s Wangdao Undergraduate Research Opportunities Program (18107), the Chinese Thousand Youth Talents Program, the PolEASIA-ANR project (ANR-15-CE04-0005), and the Australia-China Centre for Air Quality Science and Management. J.P. and J.S. acknowledge financial support from the Catalan Government grant AGAUR-2020PANDE00117. P.C. acknowledges support from the European Space Agency Climate Change Initiative ESA-CCI RECCAP2 project (ESRIN/4000123002/18/I-NB) and the Observation-based system for monitoring and verification of greenhouse gases project (VERIFY, grant 725546)., Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), National Natural Science Foundation of China, Fudan University, Agence Nationale de la Recherche (France), Generalitat de Catalunya, European Space Agency, Xing, Xiaofan [0000-0002-0334-6408], Wang, Rong [0000-0003-1962-0165], Wang, Weibing [0000-0002-4497-5251], Peñuelas, Josep [0000-0002-7215-0150], Ciais, Philippe [0000-0001-8560-4943], Bauer, Nico [0000-0002-0211-4162], Morawska, Lidia [0000-0002-0594-9683], Sardans, Jordi [0000-0003-2478-0219], Wang, Lin [0000-0002-4905-3432], Chen, Jianmin [0000-0001-5859-3070], Zhang, Renhe [0000-0001-7750-8679], Xing, Xiaofan, Wang, Rong, Wang, Weibing, Peñuelas, Josep, Ciais, Philippe, Bauer, Nico, Morawska, Lidia, Sardans, Jordi, Wang, Lin, Chen, Jianmin, and Zhang, Renhe
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China ,2019-20 coronavirus outbreak ,010504 meteorology & atmospheric sciences ,Coronavirus disease 2019 (COVID-19) ,[SDV]Life Sciences [q-bio] ,Air pollution ,Satellite observation ,Pandemic management ,medicine.disease_cause ,7. Clean energy ,01 natural sciences ,Surrogate data ,Troposphere ,03 medical and health sciences ,Machine learning ,medicine ,Humans ,Baseline (configuration management) ,Biology ,030304 developmental biology ,0105 earth and related environmental sciences ,0303 health sciences ,Multidisciplinary ,Scenario based ,COVID-19 ,3. Good health ,Socioeconomic Factors ,13. Climate action ,Climatology ,Physical Sciences ,Environmental science ,Satellite ,Engineering sciences. Technology - 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., This work was supported by the National Natural Science Foundation of China (41877506), the Fudan’s Wangdao Undergraduate Research Opportunities Program (18107), the Chinese Thousand Youth Talents Program, the PolEASIA-ANR project (ANR15-CE04-0005), and the Australia-China Centre for Air Quality Science and Management. J.P. and J.S. acknowledge financial support from the Catalan Government grant AGAUR-2020PANDE00117. P.C. acknowledges support from the European Space Agency Climate Change Initiative ESA-CCI RECCAP2 project (ESRIN/4000123002/18/I-NB) and the Observation-based system for monitoring and verification of greenhouse gases project (VERIFY, grant 725546).
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- 2021