## Abstract

University students and faculty members need an effective strategy to evaluate and reduce the probability that an individual will become infected with COVID-19 as a result of classroom interactions. Models are developed here that consider the probability an individual will become infected as a function of: prevalence of the disease in the university community, number of students in class, number of class meetings, and transmission rate in the classroom given the presence of an infected individual. Absolute probabilities that an individual will become infected in a classroom environment cannot be calculated because some of these factors have unknown values. Nevertheless, models suggest that some strategies for minimizing probability of infection are more effective than others. Given that COVID-19 might be present among faculty and students in the university community, limiting class meetings and class size are not likely effective strategies unless these numbers are dramatically reduced. That is, it is likely that infected individuals will be present in classrooms at some point during the term due to the large number of interactions among university faculty and students. The probability of infection of an individual in a classroom setting appears to be most sensitive to the effectiveness of transmission in the classroom, given the presence of an infected individual, especially if the likelihood of transmission itself is a function of class size. If on-campus instruction takes place, efforts should focus on reducing the probability of transmission through physical modifications and upgrades to classrooms and by social distancing measures.

Original language | American English |
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Journal | Default journal |

DOIs | |

State | Published - 1800 |

## Keywords

- COVID-19
- classroom
- class size
- university
- model
- quantitative literacy
- numeracy
- pandemic

## Disciplines

- Influenza Humans
- Interprofessional Education
- Occupational Health and Industrial Hygiene
- Scholarship of Teaching and Learning
- Virus Diseases