The COVID-19 pandemic has spread rapidly across the world, forcing governments to imposing travel bans, quarantine laws, business and school closings, and many other restrictions in efforts to contain the virus and limit the spread. There is an urgent need for scientists around the world to work together to model the virus, study how the virus has changed and will change over time, understand how it spreads, and discover a vaccine.
The COVID-19 Data Archive (COVID-ARC) is a repository for multimodal (i.e., demographic information, clinical outcome reports, imaging scans) and longitudinal data related to COVID-19. This archive provides access to data along with user-friendly tools for researchers to perform analyses to better understand COVID-19 and encourage collaboration on this research. The work from this project can also prepare scientists for future pandemics by putting the infrastructure in place to enable researchers to aggregate data and perform analyses quickly in the event of an emergency.
This work is supported by the National Science Foundation, award number 2027456.
For more information, please visit https://covid-arc.loni.usc.edu/.
Related Publications
A Novel Threshold-Based Segmentation Method for Quantification of COVID-19 Lung Abnormalities. Khan A, Garner R, La Rocca M, Salehi S Duncan D. Signal, Image, and Video Processing. 2023. doi:https://doi.org/10.1007/s11760-022-02183-6
Interdisciplinary K-12 Control Education in Biomedical and Public Health Applications. Duncan D, Garner R, Bennett A, Sinclair M, Ramirez-De La Cruz G, Pasik-Duncan B. Proceedings: 13th Symposium on Advances in Control Education. 2022. doi:https://doi.org/10.1016/j.ifacol.2022.09.286
Molecular and antigen tests, and sample types for diagnosis of COVID-19: a review. Zhang, Y., Garner, R., Salehi, S., Rocca, M. L., & Duncan, D. (2022). Future Virology, 17(9), 675-685. doi.org/10.2217/fvl-2021-0256
Key Radiological Features of COVID-19 Chest CT Scans with a Focus on Special Subgroups: A Literature Review. Nouaili, N., Garner, R., Salehi, S., Rocca, M., & Duncan, D. (2022). Current Medical Imaging. doi.org/10.2174/1573405618666220620125332
Global impact of COVID-19 on healthcare systems. Bruckhaus AA, Eibschutz LS, Bennett A, Sackett C, Dave K, Cherukury S, and III Smith CM. In Ali Gholamrezanezhad and Michael Dube, editors, Coronavirus Disease 2019 (COVID-19): A Clinical Guide. Wiley, 2022. doi:https://doi-org.libproxy1.usc.edu/10.1002/9781119789741.ch29
COVID-19: Risk Stratification. Duncan D, Garner R, Zhang Y. In Ali Gholamrezanezhad and Michael Dube, editors, Coronavirus Disease 2019 (COVID-19): A Clinical Guide. Wiley, 2022. doi:https://doi-org.libproxy1.usc.edu/10.1002/9781119789741.ch8
Efficient and visualizable convolutional neural networks for COVID-19 classification using Chest CT. Garg A, Salehi S, Rocca ML, Garner R, Duncan D. Expert Syst Appl. 2022;195:116540. doi:https://doi.org/10.1016/j.eswa.2022.116540
COVID-19 Vaccination Dynamics in the US: Coverage Velocity and Carrying Capacity Based on Socio-demographic Vulnerability Indices in California. Bruckhaus AA, Abedi A, Salehi S, Pickering TA, Zhang Y, Martinez A, Lai M, Garner R, Duncan D. J Immigrant Minority Health (2021). https://doi.org/10.1007/s10903-021-01308-2
Post-lockdown infection rates of COVID-19 following the reopening of public businesses. Bruckhaus A*, Martinez A*, Garner R, La Rocca M, Duncan D. Journal of Public Health, 2021; fdab325
Association between ABO blood types and coronavirus disease 2019 (COVID-19), genetic associations, and underlying molecular mechanisms: a literature review of 23 studies. Zhang Y, Garner R, Salehi S, La Rocca M, Duncan D. Ann Hematol. 2021 Mar 8:1–10. doi: 10.1007/s00277-021-04489-w. Epub ahead of print. PMID: 33686492; PMCID: PMC7939543.
COVID-19 data sharing and collaboration. Duncan, D. (2021). Communications in Information and Systems, 21(3).