COVID-19

Goal: 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.

Method: We developed a novel spatiotemporal epidemic modeling (STEM) framework for space-time epidemic data to study the spatial-temporal pattern in the spread of COVID-19. The proposed methodology can be used to dissect the spatial structure and dynamics of spread, as well as to assess how this outbreak may unfold through time and space. [Click here to read our arXiv paper]

Based on our research findings, we established a Dashboard with multiple R shiny apps embedded to provide a realtime 7-day forecast and a four-month forecast of COVID-19 infection and death count at both the county level and state level, as well as the corresponding risk analysis. This dashboard was launched on 03/27/2020 for displaying results of our statistical analyses on publicly available COVID-19 datasets merged from different sources.

Click here to read our interview with Amstat News about the COVID-19 Dashboard:

• Amstat News

• Professor Lily Wang, George Mason University

• Professor GuanNan Wang, College of William and Mary

• Professor Lei Gao, Iowa State University

• Professor Xinyi Li, Clemson University [Former Ph.D. student at Iowa State University]

• Professor Shan Yu, University of Virginia [Former Ph.D. student at Iowa State University]

• Myungjin Kim, Ph.D. student, Iowa State University

• Yueying Wang, Ph.D. student, Iowa State University

• Zhiling Gu, Ph.D. student, Iowa State University

• Lin Quan, Ph.D. student, Iowa State University

• Yu, S.*, Wang, Y.*, Wang, L. and Gao, L. (2021). Spatiotemporal autoregressive partially linear varying coefficient models. Statistica Sinica. Accepted.

• Wang, Y.*, Kim, M.*, Yu, S.*, Li, X.*, Wang, G.* and Wang, L. (2021). Nonparametric estimation and inference for spatiotemporal epidemic models. Journal of Nonparametric Statistics. Accepted.

• Wang, G.*, Gu, Z.*, Li, X.*, Yu, S.*, Kim, M.*, Wang, Y.*, Gao, L. and Wang, L. (2021). Comparing and integrating US COVID-19 data from multiple sources with anomaly detection and repairing. Journal of Applied Statistics. In press. [Read PDF]

• Kim, M.*, Gu, Z.*, Yu, S.*, Wang, G.*, and Wang, L. (2021). Methods, challenges, and practical issues of COVID-19 projection: a data science perspective. Journal of Data Science,19 (2), 219-242. [Read PDF] [Philosophy of Data Science]

• Wang, L., Wang, G.*, Li, X.*, Yu, S.*, Kim, M.*, Wang, Y.*, Gu, Z.* and Gao, L. (2021). Modeling and forecasting COVID-19. AMS: Notices Of The American Mathematical Society, 68, 585-595. [Read PDF]

• Yu, S.*, Wang, G.* and Wang, L. (2021). Discussion of "Evaluate the risk of resumption of business for the states of New York, New Jersey and Connecticut via a pre-symptomatic and asymptomatic transmission model of COVID-19". Journal of Data Science. [Read PDF]