Simulation-based what-if analysis for controlling the spread of Covid-19 in universities

Abstract

A simulation model is developed to analyze the spread of covid-19 in universities. The model can be used to conduct a what-if analysis and estimate infection cases under different policies. For proof-of-concept, the model is simulated for a hypothetical university of 25,000 students and 3,000 faculty/staff in a U.S. college town. Simulation results show that early outbreaks are very likely, and there is no silver bullet to avoid them. Instead, a combination of policies should be carefully implemented. The results suggest (almost) full remote university operations from the beginning of the semester. In a less-preferred alternative, if universities decide to have students attend in person, they should encourage remote operations for high-risk individuals, conduct frequent rapid tests, enforce mask use, communicate with students and employees about the risks, and promote social distancing. Universities should be willing to move to remote operations if cases rise. Under this scenario, and considering implementation challenges, many universities are still likely to experience an early outbreak, and the likelihood of having a case of death is worrisome. In the long run, students and faculty react to the risks, and even if universities decide to continue operations, classes are likely to have very low in-person attendance. Overall, our analysis depicts several sources of system complexities, negative unintended consequences of relying on a single policy, non-linear incremental effects, and positive synergies of implementing multiple policies. A simulation platform for a what-if analysis is offered so marginal effectiveness of different policies and different decision-making thresholds for closure can be tested for universities of varying populations.

Designing optimal COVID-19 testing stations locally: A discrete event simulation model applied on a university campus

“…Simulation notably allows many "what if?" scenarios to be tested in an efficient way for decision-making [20]. For instance, simulation-based failure mode analysis can be useful to identify the risks related to the readiness of the healthcare workers and emergency departments for the COVID-19 [21].…”

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

“…While a few modeling studies have provided some insights into COVID-19 spread in college campuses 9 – 14 , none of the previous studies has explored the inter-relation among break schedule, travel behavior, and local COVID-19 surge. In this paper, we used mathematical and computational models to examine the effects of four commonly practiced alternative spring break schedules under five different realistic mean prevalence rates at travel destinations, on SARS-CoV-2 transmission among on-campus students (those not involved in virtual learning who physically go to campus).…”

Association mining based approach to analyze COVID-19 response and case growth in the United States

“…Tatapudi et al presented a study on Miami-Dade County to understand how social-mixing behavior, stay-at-home orders, and contact tracing affect both the case growth and economy 3 . Similar efforts include agent-based simulation developed by Silvia et al 4 , and Ghaffarzadegan 5 . As more case growth data became available, researchers used data-driven methods to find associations between non-pharmaceutical interventions and case growth data.…”