A Stochastic Model of University Virus Transmission

The spread of airborne viruses is a concern on campuses that has been heightened by the current COVID-19 global pandemic, leading to many universities taking various safety precautions to minimize outbreaks on campus. Smaller universities may benefit from using a stochastic compartmental model to help determine the impact of specific policies and scenarios on the spread of COVID-19 on their campuses. The model we developed creates a network of students with randomly generated class schedules and, starting with a randomly chosen infected student, calculates the number of infections on campus as the semester progresses. From this model, we determined the relationship between probability of infection, the peak number of infections, and the impact of various social distancing policies on the spread of the virus on campus. As the infectivity of the virus increases, our results show that the peak of the campus outbreak occurred earlier in the semester, while for lower infectivity rates, it was more likely for the virus to entirely disappear from the community. We also observed naturally occurring “superspreader” events that worked to prolong outbreaks on campus. Finally, we expanded our model to capture more complex and realistic student interactions on and off campus.