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Title
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Predicting the effect of confinement on the COVID-19 spread using machine learning enriched with satellite air pollution observations
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Author
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Abstract
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| 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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Language
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English
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Source (journal)
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Proceedings of the National Academy of Sciences of the United States of America. - Washington, D.C.
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Publication
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Washington, D.C.
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2021
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ISSN
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0027-8424
[Print]
1091-6490
[Online]
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DOI
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10.1073/PNAS.2109098118
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Volume/pages
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118
:33
(2021)
, 11 p.
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Article Reference
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e2109098118
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ISI
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000689727700005
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Pubmed ID
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34380740
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Medium
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E-only publicatie
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Full text (Publisher's DOI)
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Full text (publisher's version - intranet only)
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