@article{TEXTUAL,
      recid = {14613},
      author = {Han, Zhenyu and Xu, Fengli and Li, Yong and Jiang, Tao and  Evans, James},
      title = {Model predicted human mobility explains COVID-19  transmission in urban space without behavioral data},
      journal = {Scientific Reports},
      address = {2025-02-21},
      number = {TEXTUAL},
      abstract = {The SARS-CoV-2 virus is primarily transmitted through  in-person interactions, and so its growth in urban space is  a complex function of human mobility behaviors that cannot  be adequately explained by standard epidemiological models.  Recent studies leveraged fine-grained urban mobility data  to accurately model the viral spread, but such data pose  privacy concerns and are often difficult to collect,  especially in low- and middle-income countries (LMICs).  Here, we show that the metapopulation epidemiological model  incorporated with a simple gravity mobility model can be  sufficient to capture most of the complex epidemic dynamics  in urban space, largely reducing the need for empirical  mobility data. Extensive experiments on 30 cities in the  United States, India and Brazil show that our model  consistently reproduces complex, distinctive COVID-19  growth curves with high accuracy. It also provides a  theoretical explanation of the emergence of urban  “superspreading”, where a few high-risk neighborhoods  account for most subsequent infections. Furthermore, with  the aid of the proposed framework, we can inform  mobility-aware travel restrictions to achieve a better  balance between social cost and disease prevention, which  facilitates sustainable epidemic control and supports the  gradual transition to a post-pandemic world.},
      url = {http://knowledge.uchicago.edu/record/14613},
}