Estimating data-driven coronavirus disease 2019 mitigation strategies for safe university reopening

Abstract

After one pandemic year of remote or hybrid instructional modes, universities struggled with plans for an in-person autumn (fall) semester in 2021. To help inform university reopening policies, we collected survey data on social contact patterns and developed an agent-based model to simulate the spread of severe acute respiratory syndrome coronavirus 2 in university settings. Considering a reproduction number of R 0 = 3 and 70% immunization effectiveness, we estimated that at least 80% of the university population immunized through natural infection or vaccination is needed for safe university reopening with relaxed non-pharmaceutical interventions (NPIs). By contrast, at least 60% of the university population immunized through natural infection or vaccination is needed for safe university reopening when NPIs are adopted. Nevertheless, attention needs to be paid to large-gathering events that could lead to infection size spikes. At an immunization coverage of 70%, continuing NPIs, such as wearing masks, could lead to a 78.39% reduction in the maximum cumulative infections and a 67.59% reduction in the median cumulative infections. However, even though this reduction is very beneficial, there is still a possibility of non-negligible size outbreaks because the maximum cumulative infection size is equal to 1.61% of the population, which is substantial.

Incorporating social determinants of health into transmission modeling of COVID-19 vaccine in the US: a scoping review

“…The study setting was the US in 80 studies, and the US combined with other countries in 12 studies. 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 The target population was the general population in 68 studies and specific sub-populations in 24 studies, including university population (n = 11), 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 school population (n = 5), 39 , 40 , 41 , 42 , 43 congregation setting population (n = 5), 44 , 45 , 46 , 47 , 48 children (n = 2), 18 , 49 and wildland firefighting workforce (n = 1). 50 Vaccine intervention strategies were studied interventions in 69 studies, and vaccines combined with other strategies (including non-pharmaceutical interventions (NPIs) and screening) in 23 studies.…”

Public health policy impact evaluation: A potential use case for longitudinal monitoring of viruses in wastewater at small geographic scales

“…Notwithstanding these limitations, we compared our results to those of other studies that also assessed COVID-19 public health policies among a vaccinated university population. Yang et al similarly found that large gatherings are potentially high-risk events on campus [84]. Niu and Scarciotti concluded that mask wearing and social distancing measures were most effective at reducing new infections [33].…”

An agent-based modelling framework for assessing SARS-CoV-2 indoor airborne transmission risk

“…The models with the simplest realistic structure are typified by Cuevas [14] who model agents randomly moving in a 2-dimensional space, with infection risk based on a fixed probability within a radius of infectious individuals. Several models have explored university campuses, with varying levels of detail [15][16][17]. Vecherin et al develop micro-exposure models of workplaces [18].…”

“…Several ABMs consider different types of rooms and buildings [15, 18]. Some build schedules based on realistic features such as day of the week, known course schedule (studying or teaching), and residence on or off campus [15], whilst others use surveys to capture social networks [16]. Some models capture the risk of infection using susceptible-infectious-removed progression approaches [17], some use the transition between states as a lognormal distribution [16], whilst others try to capture the epidemiology and physics of interaction [6], and in particular, how close the contact is and for how long [15, 18].…”

“…Some build schedules based on realistic features such as day of the week, known course schedule (studying or teaching), and residence on or off campus [15], whilst others use surveys to capture social networks [16]. Some models capture the risk of infection using susceptible-infectious-removed progression approaches [17], some use the transition between states as a lognormal distribution [16], whilst others try to capture the epidemiology and physics of interaction [6], and in particular, how close the contact is and for how long [15, 18]. The interventions considered by these ABMs range from contact tracing [17], mobility restrictions [14], changes to infection probabilities [14], and combinations of vaccination and other interventions [18].…”