气候变化领域集成服务门户(CCPortal): Predicting the effect of confinement on the COVID-19 spread using machine learning enriched with satellite air pollution observations

CCPortal

DOI	10.1073/pnas.2109098118
	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.
发表日期	2021
ISSN	0027-8424
卷号	118 期号:33
英文摘要	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. © 2021 National Academy of Sciences. All rights reserved.
英文关键词	Air pollution; COVID-19; Machine learning; Pandemic management; Satellite observation
语种	英语
scopus关键词	nitrogen dioxide; air pollution; Article; China; coronavirus disease 2019; Europe; geographic distribution; home quarantine; human; India; leave one out cross validation; machine learning; pandemic; prediction; satellite imagery; socioeconomics; troposphere; United States; virus transmission; weather; adverse event; air pollution; epidemiology; etiology; Air Pollution; China; COVID-19; Humans; Machine Learning; Socioeconomic Factors
来源期刊	Proceedings of the National Academy of Sciences of the United States of America
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/238428
作者单位	Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention, Department of Environmental Science and Engineering, Fudan University, Shanghai, 200438, China; Integrated Research on Disaster Risk International Center of Excellence on Risk Interconnectivity and Governance on Weather, Climate Extremes Impact and Public Health, Fudan University, Shanghai, 200438, China; Department of Atmospheric and Oceanic Sciences, Institute of Atmospheric Sciences, Fudan University, Shanghai, 200438, China; Center for Urban Eco-Planning and Design, Fudan University, Shanghai, 200438, China; Big Data Institute for Carbon Emission and Environmental Pollution, Fudan University, Shanghai, 200438, China; Shanghai Institute of Pollution Control and Ecological Security, Shanghai, 200092, China; Key Laboratory of Public Health Safety, Ministry of Education and National Health Commission, Key Laboratory of Health Technology Assessment, School of Public Health, Fudan University, Shanghai, 200438, China; Shanghai Key Labo...
推荐引用方式 GB/T 7714	Xing X.,Xiong Y.,Yang R.,et al. Predicting the effect of confinement on the COVID-19 spread using machine learning enriched with satellite air pollution observations[J],2021,118(33).
APA	Xing X..,Xiong Y..,Yang R..,Wang R..,Wang W..,...&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,118(33).
MLA	Xing X.,et al."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 118.33(2021).