An agent-based model for simulating COVID-19 transmissions on university campus and its implications on mitigation interventions: a case study

Purpose - Universities across the USA are facing challenging decision-making problems amid the COVID-19 pandemic. The purpose of this study is to facilitate universities in planning disease mitigation interventions as they respond to the pandemic. Design/methodology/approach - An agent-based model is developed to mimic the virus transmission dynamics on campus. Scenario-based experiments are conducted to evaluate the effectiveness of various interventions including course modality shift (from face-to-face to online), social distancing, mask use and vaccination. A case study is performed for a typical US university. Findings - With 10%, 30%, 50%, 70% and 90% course modality shift, the number of total cases can be reduced to 3.9%, 20.9%, 35.6%, 60.9% and 96.8%, respectively, comparing against the baseline scenario (no interventions). More than 99.9% of the total infections can be prevented when combined social distancing and mask use are implemented even without course modality shift. If vaccination is implemented without other interventions, the reductions are 57.1%, 90.6% and 99.6% with 80%, 85% and 90% vaccine efficacies, respectively. In contrast, more than 99% reductions are found with all three vaccine efficacies if mask use is combined. Practical implications - This study provides useful implications for supporting universities in mitigating transmissions on campus and planning operations for the upcoming semesters. Originality/value - An agent-based model is developed to investigate COVID-19 transmissions on campus and evaluate the effectiveness of various mitigation interventions.