The COVID-19 has thrilled the whole country for over three months. While the trend is still increasing, the universities are getting ready to reopen their campus. University campuses are locations where students gather around and people tend to have a close social distance, especially when classes are in session. This poses a severe concerns to not only college students but also the public.
In this study, we propose a practical, statistics-based approach to predict the CVIOD-19 transmission trend around the campus. The model (equation) provided is able to tell stories from different scenarios.
The method takes the current COVID-19 infected number of person on a campus, and model the trend based on the campus population data and a series of various transmission rate models (scenarios).
As a case study, we take UCLA campus as an example, and applied our method to demonstrate the difference among scenarios. Simulation results show that the TR models are the major contributing factor in terms of the trend of changes. Suggestion were given for better decision making based on the modeling results.
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