@article{TEXTUAL,
      recid = {5632},
      author = {Yang, Xuan and Yang, Yang and Tan, Chenhao and Lin, Yinghe  and Fu, Zhengzhe and Wu, Fei and Zhuang, Yueting},
      title = {Unfolding and modeling the recovery process after COVID  lockdowns},
      journal = {Scientific Reports},
      address = {2023-03-13},
      number = {TEXTUAL},
      abstract = {Lockdown is a common policy used to deter the spread of  COVID-19. However, the question of how our society comes  back to life after a lockdown remains an open one.  Understanding how cities bounce back from lockdown is  critical for promoting the global economy and preparing for  future pandemics. Here, we propose a novel computational  method based on electricity data to study the recovery  process, and conduct a case study on the city of Hangzhou.  With the designed Recovery Index, we find a variety of  recovery patterns in main sectors. One of the main reasons  for this difference is policy; therefore, we aim to answer  the question of how policies can best facilitate the  recovery of society. We first analyze how policy affects  sectors and employ a change-point detection algorithm to  provide a non-subjective approach to policy assessment.  Furthermore, we design a model that can predict future  recovery, allowing policies to be adjusted accordingly in  advance. Specifically, we develop a deep neural network,  TPG, to model recovery trends, which utilizes the graph  structure learning to perceive influences between sectors.  Simulation experiments using our model offer insights for  policy-making: the government should prioritize supporting  sectors that have greater influence on others and are  influential on the whole economy.},
      url = {http://knowledge.uchicago.edu/record/5632},
}