Modeling COVID-19 spread in small colleges

Abstract

We develop an agent-based model on a network meant to capture features unique to COVID-19 spread through a small residential college. We find that a safe reopening requires strong policy from administrators combined with cautious behavior from students. Strong policy includes weekly screening tests with quick turnaround and halving the campus population. Cautious behavior from students means wearing facemasks, socializing less, and showing up for COVID-19 testing. We also find that comprehensive testing and facemasks are the most effective single interventions, building closures can lead to infection spikes in other areas depending on student behavior, and faster return of test results significantly reduces total infections.

Effectiveness of alternative semester break schedules on reducing COVID-19 incidence on college campuses

“…If a university has reason to believe that many of its students will not heed public health warnings and travel for recreation during scheduled break periods, the university may want to consider implementing alternative break schedules, along with other mitigation measures such as vaccination, masking, and surveillance testing, to help reduce the potential impact of prolonged break periods on virus transmission in their local community. Strong policy measures such as wearing facemasks, testing and reduced capacity have shown large reductions in infections in campus models 11 . Our findings corroborate the importance of travel restrictions in limiting COVID-19 disease spread 18 .…”

Estimating data-driven coronavirus disease 2019 mitigation strategies for safe university reopening

“…When an individual has mild symptoms (state Is1), it may get a SARS-CoV-2 test based on a 70% probability [12]. Individuals in the critical or moderate symptomatic state (Is2 or Is3) may get tested with a 95% probability [12]. The delay for returning test results is uniformly distributed between 0 and 2 days.…”

“…Bahl et al . [ 12 ] developed a detailed agent-based susceptible–exposed–infected–recovered (SEIR) model with a university population of 2380, focusing on small, closed-community residential colleges. In their model, each agent randomly moves between nodes in a graph based on a fixed hourly schedule.…”

“…Previous studies have shown that contact networks could significantly impact the accuracy of epidemic predictions and the effectiveness of control strategies [ 20 – 22 ], highlighting the importance of data collection on real-world contact patterns. Similar to the models in [ 10 – 12 ], each individual in our model follows a schedule to move to different locations and can have contacts with other individuals that are in the same location. The main difference between our work and previous studies is that our model is tailored in such a way as to use the social contact survey data we collected about social interactions in a US university as much as possible.…”

SARS-CoV-2 Transmission in University Classes

“…Many individual level models have a compartmental structure, such as SIR (susceptible, infectious, removed) or SEIR (susceptible, exposed, infectious, removed) (Brauer, 2008;Deardon et al, 2010), where individuals are assigned to a category based on their disease status, and the researcher models how individuals move between categories. While much work has been done on modelling community transmission (see, e.g., BCCDC, 2021; Chang et al, 2020;Rȃdulescu et al, 2020;Tuite et al, 2020), some authors have instead directed their efforts toward understanding outbreaks on university campuses (Gressman and Peck, 2020; Kharkwal et al, 2020;Frazier, 2020;Bahl et al, 2020;Borowiak et al, 2020;Ambatipudi et al, 2021;Weeden and Cornwell, 2020). Gressman and Peck (2020) simulated social dynamics within a university and the corresponding infection rates.…”