Mathematical modelling of the spread of COVID-19 on a university campus

• Published in: Infectious disease modelling Vol. 6; pp. 1025 - 1045
• Main Authors: Muller, Kaitlyn, Muller, Peter A.
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.01.2021; KeAi Publishing; KeAi Communications Co., Ltd
• Subjects: Contact tracing; Coronavirus; COVID-19; Epidemiological models; Original; SEIR; Surveillance testing 2010 MSC: 92-10; University/college campus; COVID-19; Coronavirus; University/college campus; Contact tracing; Epidemiological models; Surveillance testing 2010 MSC: 92-10; SEIR
• ISSN: 2468-0427; 2468-2152; 2468-0427
• DOI: 10.1016/j.idm.2021.08.004

In this paper we present a deterministic transmission dynamic compartmental model for the spread of the novel coronavirus on a college campus for the purpose of analyzing strategies to mitigate an outbreak. The goal of this project is to determine and compare the utility of certain containment strategies including gateway testing, surveillance testing, and contact tracing as well as individual level control measures such as mask wearing and social distancing. We modify a standard SEIR-type model to reflect what is currently known about COVID-19. We also modify the model to reflect the population present on a college campus, separating it into students and faculty. This is done in order to capture the expected different contact rates between groups as well as the expected difference in outcomes based on age known for COVID-19. We aim to provide insight into which strategies are most effective, rather than predict exact numbers of infections. We analyze effectiveness by looking at relative changes in the total number of cases as well as the effect a measure has on the estimated basic reproductive number. We find that the total number of infections is most sensitive to parameters relating to student behaviors. We also find that contact tracing can be an effective control strategy when surveillance testing is unavailable. Lastly, we validate the model using data from Villanova University's online COVID-19 Dashboard from Fall 2020 and find good agreement between model and data when superspreader events are incorporated in the model as shocks to the number of infected individuals approximately two weeks after each superspreader event.