Space-time Forecast of Infection Count, Death Count and Risk Analysis
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:
- Professor Lily Wang, Iowa State 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
If you have any suggestions or feedback, please feel free to email us. We appreciate your time and support.