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

Proc Natl Acad Sci U S A. 2021 Aug 17;118(33):e2109098118. doi: 10.1073/pnas.2109098118.


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.

Keywords: COVID-19; air pollution; machine learning; pandemic management; satellite observation.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Air Pollution / adverse effects*
  • COVID-19 / epidemiology*
  • COVID-19 / etiology
  • China / epidemiology
  • Humans
  • Machine Learning*
  • Socioeconomic Factors