Predicting the effect of confinement on the COVID-19 spread using machine learning enriched with satellite air pollution observations
Xiaofan Xing, Yuankang Xiong, Ruipu Yang, Rong Wang, Weibing Wang, Haidong Kan, Tun Lu, Dongsheng Li, Junji Cao, Josep Peñuelas, Philippe Ciais, Nico Bauer, Oliviér Boucher, Yves Balkanski, Didier Hauglustaine, Guy Brasseur, Lídia Morawska, Ivan A. Janssens, Xiangrong Wang, Jordi Sardans, Yijing Wang, Yi‐Fei Deng, Lin Wang, Jianmin Chen, Xu Tang, Renhe Zhang
Abstract
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.