COVID-19 – GeoDSLab@UW-Madison

NSF Funded Project: Geospatial Modeling of COVID-19 Spread and Risk Communication by Integrating Human Mobility and Social Media Big Data

Tracking Mobility Changes Interactive Web Portal: https://geods.geography.wisc.edu/covid19/physical-distancing/

Multiscale Dynamic Human Mobility O-D Flow Dataset in the US https://github.com/GeoDS/COVID19USFlows

Intra-County COVID-19 Infection Modeling (PNAS) https://github.com/GeoDS/IntraCounty-Mobility-SEIR

State-Specific COVID-19 Infection Projections in the US (Sci. Rep.) https://github.com/GeoDS/Travel-Network-SEIR

Inter-Country COVID-19 Infection and Importation Modeling (PNAS) https://github.com/GeoDS/COVID-SDPD

References:

Gao, S., Rao, J., Kang, Y., Liang, Y., Kruse, J, Dopfer, D., Sethi, A., Reyes, F., Yandell, B., & Patz J. (2020). Association of Mobile Phone Location Data Indications of Travel and Stay-at-Home Mandates With COVID-19 Infection Rates in the US. JAMA Network Open. 2020;3(9):e2020485. doi:10.1001/jamanetworkopen.2020.20485

Gao, S., Rao, J., Kang, Y., Liang, Y., & Kruse, J. (2020). Mapping county-level mobility pattern changes in the United States in response to COVID-19. SIGSPATIAL Special. 12(1), 16-26. DOI: 10.1145/3404820.3404824

Kang, Y., Gao, S., Liang, Y., Li, M., Rao, J., & Kruse, J. (2020). Multiscale Dynamic Human Mobility Flow Dataset in the US during the COVID-19 Epidemic. Scientific Data, 7(390), 1-13, https://www.nature.com/articles/s41597-020-00734-5.

Chen, S., Li, Q., Gao, S., Kang, Y.,& Shi, X.(2020). State-specific Projection of COVID-19 Infection in the United States and Evaluation of Three Major Control Measures. Scientific Reports,10(22429), 1-9, www.nature.com/articles/s41598-020-80044-3.

Huang, X., Lu, J., Gao, S., Wang, S., Liu, Z., & Wei, H. (2021). Staying at home is a privilege: evidence from fine-grained mobile phone location data in the U.S. during the COVID-19 pandemic. Annals of the American Association of Geographers. 1-18, DOI:10.1080/24694452.2021.1904819.

Hou, X., Gao, S., Li, Q., Kang, Y., Chen, N., Chen, K., Rao, J., Ellenberg, J., & Patz, J. A. (2021). Intra-county modeling of COVID-19 infection with human mobility: assessing spatial heterogeneity with business traffic, age and race. Proceedings of the National Academy of Sciences(PNAS), June 15, 2021, 118 (24), e2020524118

Han, X., Xu, Y., Fan, L., Huang, Y., Xu, M., & Gao, S. (2021). Quantifying COVID-19 importation risk in a dynamic network of domestic cities and international countries. Proceedings of the National Academy of Sciences(PNAS), June 15, 2021, 118 (31), e2100201118

Liang, D., Shi, L., Zhao, J., Liu, P., Sarnat, J. A., Gao, S., Schwartz, J., Liu, Y., Ebelt, S., Scovronick, N., & Chang, H. H. (2021). Urban air pollution may enhance COVID-19 case-fatality and mortality rates in the United States. The Innovation , 1(3), 100047, Cell Press.

Liang, Y., McNair, K. W., Gao, S., & Göçmen, A. (2021). Exploring Store Visit Changes During the COVID-19 Pandemic Using Mobile Phone Location Data. In Shih-Lung Shaw and Daniel Sui (Eds): Mapping COVID-19 in Space and Time: Understanding the Spatial and Temporal Dynamics of a Global Pandemic (Chapter 13) , pp. 253-275, Springer.

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