A UW-Madison IFDS team (Nan Chen, Jordan Ellenberg, Xiao Hou and Qin Li), together with Song Gao, Yuhao Kang and Jingmeng Rao (UW-Madison, Geography), Kaiping Chen (UW-Madison, Life Sciences Communication) and Jonathan Patz (Global Health Institute), recently studied the COVID-19 spreading pattern, and its correlation with business foot traffic, race and ethnicity and age structure of sub-regions within Dane and Milwaukee county. The results are published on Proceedings of the National Academy of Sciences of the United States of America. (https://www.pnas.org/content/118/24/e2020524118)
A human mobility flow-augmented stochastic SEIR model was developed. When the model was combined with data assimilation and machine learning techniques, the team reconstructed the historical growth trajectories of COVID-19 infection in both counties. The results reveal different types of spatial heterogeneities (e.g., varying peak infection timing in different subregions) even within a county, suggesting a regionalization-based policy (e.g, testing and vaccination resource allocation) is necessary to mitigate the spread of COVID-19, and to prepare for future epidemics.