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

Abstract

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.

Monitoring and responding to emerging infectious diseases in a university setting: A case study using COVID-19

“…Finally, like numerous other modeling studies (e.g., (39,41)), we found that surveillance testing with complete sampling can substantially decrease infection rates. Complete sampling requires a vast amount of financial, logistical, and human resources to implement, though, and cannot be launched in Disease X situations until reliable tests are developed.…”

“…Contact tracing was also effective in controlling the transmission rate in simulated results. This intervention has a long and proven track record in public health policy, translates well to novel diseases, and was found to have a comparable effectiveness to surveillance testing with complete sampling in other modeling studies of COVID-19 (41). However, contact tracing requires trained personnel that may not be available at every university.…”

Community- and Data-Driven Services for Multi-Policy Pedestrian Routing

“…A set of studies have been conducted to see how the transmission is carried out and to determine the right measures for prevention and distancing. They regard the dependency between pedestrian dynamics and epidemiology [33], the aerosol transmission [34], the mathematical modeling of disease spreading [35], and the risks of students' exposure to COVID-19 in a university building [36] or in other urban settlements [37].…”

COVID-19 safe campus evaluation for universities by a hybrid interval type-2 fuzzy decision-making model

“…Universities faced many challenges with the decision to offer face-to-face education during the COVID-19 pandemic. When the studies on the creation of a safe campus in the literature are examined, it is seen that studies have focused on effective measures to make face-to-face education safer during the COVID-19 pandemic (Cardonha et al 2022 ; Greenhalgh et al 2021 ; Muller and Muller 2021 ; Gillespie et al 2021 ). Wilson et al ( 2020 ), while studying the reliability of the measures taken at university A in North Carolina and the reasons for the increase in cases, Rafiq et al ( 2021 ), on the other hand, conducted a study to determine the service models, strategies, and role of university libraries during the COVID-19 epidemic.…”