Reopen Schools Safely: Simulating COVID-19 Transmission on Campus With a Contact Network Agent-based Model

Background: As phases of COVID-19 vaccination are quickly rolling out, how to evaluate the vaccination effects and then make safe reopening plans has become a prime concern for local governments and school officials. Methods: : We develop a contact network agent-based model (CN-ABM) to simulate on-campus disease transmission scenarios at the micro-scale. The CN-ABM establishes a contact network for each agent based on their daily activity pattern, evaluates the agent's health status change in different activity environments, and then simulates the epidemic curve on campus. Based on the developed model, we identify how different community risk levels, teaching modalities, and vaccination rates would shape the epidemic curve. Results: : The results show that in scenarios where vaccination is not available, restricting on-campus students to under 50% can largely flatten the epi curve (peak value < 2%); and the best result (peak value < 1%) can be achieved by limiting on-campus students to less than 25%. In scenarios where vaccination is available, it is suggested to maintain a maximum of 75% on-campus students and a vaccination rate of at least 45% to suppress the curve (peak value < 2%); and the best result (peak value < 1%) can be achieved at a vaccination rate of 65%. The study also derives the transmission chain of infectious agents, which can be used to identify high-risk activity environments. Conclusions: : The developed CN-ABM model can be employed to evaluate the health outcome of COVID-19 outbreaks on campus based on different disease transmission scenarios. It can assist local government and school officials with developing proactive intervention strategies to safely reopen schools.