Space-time Forecast of Infectious/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 established a new spatiotemporal epidemic modeling (STEM) framework for space-time infected/death count data to study the dynamic 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 paper on arXiv]
Shiny Apps: Currently, we offer two main and multiple small R shiny apps in our dashboard.
The first app is targeted to serve the local communities, and we provide a real-time 7-day forecast of the infection/death count up to the county level. This app also gives you a way to assess your community's risk level compared to others. At a glance, you can check how severe the spread of COVID-19 is (or will be) in your community and guide your decision.
The second app offers a four-month ahead prediction of the death count based on the most recent data, and it is updated weekly. This app is useful for policymakers and public health leaders to understand how this outbreak may unfold through time and space in the future. For example, it can give hospitals an idea of how quickly they need to expand their capacity and by how much.
We also included multiple small apps below to share our findings and insights with the general public.
