Abstract
Despite COVID-19 vaccination programs, the threat of new SARS-CoV-2 strains and continuing pockets of transmission persists. While many U.S. universities replaced their traditional nine-day spring 2021 break with multiple breaks of shorter duration, the effects these schedules have on reducing COVID-19 incidence remains unclear. The main objective of this study is to quantify the impact of alternative break schedules on cumulative COVID-19 incidence on university campuses. Using student mobility data and Monte Carlo simulations of returning infectious student size, we developed a compartmental susceptible-exposed-infectious-asymptomatic-recovered (SEIAR) model to simulate transmission dynamics among university students. As a case study, four alternative spring break schedules were derived from a sample of universities and evaluated. Across alternative multi-break schedules, the median percent reduction of total semester COVID-19 incidence, relative to a traditional nine-day break, ranged from 2 to 4% (for 2% travel destination prevalence) and 8–16% (for 10% travel destination prevalence). The maximum percent reduction from an alternate break schedule was estimated to be 37.6%. Simulation results show that adjusting academic calendars to limit student travel can reduce disease burden. Insights gleaned from our simulations could inform policies regarding appropriate planning of schedules for upcoming semesters upon returning to in-person teaching modalities.
Highlights
Despite COVID-19 vaccination programs, the threat of new SARS-CoV-2 strains and continuing pockets of transmission persists
Modeling SARS-CoV-2 transmission influenced by student travel and disease prevalence at travel destinations can provide insights into the impact different break schedules have on COVID-19 incidence among on-campus university s tudents[4]
Our analysis led to the following four alternative spring break schedules: (1) the one-break schedule, (2) the two-break schedule, (3) the three-break schedule, and (4) the four-break schedule
Summary
Despite COVID-19 vaccination programs, the threat of new SARS-CoV-2 strains and continuing pockets of transmission persists. Modeling SARS-CoV-2 transmission influenced by student travel and disease prevalence at travel destinations can provide insights into the impact different break schedules have on COVID-19 incidence among on-campus university s tudents[4]. Analysis of mobility data, paired with results from Monte Carlo (MC) simulations, informed the relationship between travel and infections and were used directly in a variant of a SEIAR (susceptible-exposed-infectious-asymptomatic-recovered) type compartmental model to estimate total cases throughout the semester. We utilized both real-time and synthetic data to model the effects of breaks of different duration on college campuses. We performed thorough sensitivity analyses on those parameters that drive the results
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