Last Update:
This site was launched on March 27, 2020. We will update the data and our 7-day forecast by 3:00 p.m. daily based on the most recently available information. The long-term projection app will be updated every Tuesday.
We aim to provide a user-friendly tool to visualize, track and predict real-time infected/death cases of COVID-19 in the U.S., based on our collected data and proposed methods, and thus further illustrate the spatiotemporal dynamics of the disease spread and guide evidence-based decision making.
Contributors
- Dr. Lily Wang, Professor in Statistics, Iowa State University
- Dr. GuanNan Wang, Assistant Professor in Mathematics, College of William & Mary
- Dr. Lei Gao, Assistant Professor in Finance, Iowa State University
- Dr. Xinyi Li, Assistant Professor in Statistics, Clemson University
- Shan Yu, Assistant Professor in Statistics, The University of Virginia
- Myungjin Kim, PhD Student in Statistics, Iowa State University
- Yueying Wang, PhD Student in Statistics, Iowa State University
- Zhiling Gu, PhD Student in Statistics, Iowa State University
- Lin Quan, PhD Student in Statistics, Iowa State University
Undergraduate researchers
- Haley Humphries, Senior Undergraduate Student in Statistics, Iowa State University
- Jiankun Wang, Junior Undergraduate Student in Mathematics and Economics, College of William & Mary
- Yiling Zhang, Junior Undergraduate Student in Mathematics and Psychology, College of William & Mary
Suggested Citation
- Wang, L., Wang, G., Gao, L., Li, X., Yu, S., Kim, M. and Wang, Y. (2020). An R shiny app to visualize, track, and predict a 7-day infected and death cases of COVID-19 in the United States. https://covid19.stat.iastate.edu
- Wang, L., Wang, G., Gao, L., Li, X., Yu, S., Kim, M., Wang, Y. and Gu, Z. (2020). A Shiny App to predict infected and death cases of COVID-19 in the U.S. for the next four months. https://covid19.stat.iastate.edu/longtermproj.html
- Wang, L., Wang, G., Gao, L., Li, X., Yu, S. Kim, M., Wang, Y. and Gu, Z. (2020). Spatiotemporal dynamics, nowcasting and forecasting of COVID-19 in the United States. [arXiv: 2004.14103]
- Wang, G., Gu, Z., Li, X., Yu, S. Kim, M., Wang, Y., Gao, L. and Wang, L. (2020). Comparing and integrating US COVID-19 data from multiple sources with anomaly detection and repairing [arXiv: 2006.01333]
Disclaimer
This disclaimer informs readers that the analysis, views, thoughts, and opinions expressed in the site belong solely to our research group, and not necessarily to the contributors' employers, organization, government agency, committee, or other group or individual.
Note: Our research and data will be continuously improved as the pandemic progresses. Thank you very much for your time and support.