University Operations During a Pandemic: A Flexible Decision Analysis Toolkit

Abstract

Modeling infection spread during pandemics is not new, with models using past data to tune simulation parameters for predictions. These help in understanding of the healthcare burden posed by a pandemic and responding accordingly. However, the problem of how college/university campuses should function during a pandemic is new for the following reasons: (i) social contact in colleges are structured and can be engineered for chosen objectives; (ii) the last pandemic to cause such societal disruption was more than 100 years ago, when higher education was not a critical part of society; (iii) not much was known about causes of pandemics, and hence effective ways of safe operations were not known; and (iv) today with distance learning, remote operation of an academic institution is possible. As one of the first to address this problem, our approach is unique in presenting a flexible simulation system, containing a suite of model libraries, one for each major component. The system integrates agent-based modeling and the stochastic network approach, and models the interactions among individual entities (e.g., students, instructors, classrooms, residences) in great detail. For each decision to be made, the system can be used to predict the impact of various choices, and thus enables the administrator to make informed decisions. Although current approaches are good for infection modeling, they lack accuracy in social contact modeling. Our agent-based modeling approach, combined with ideas from Network Science, presents a novel approach to contact modeling. A detailed case study of the University of Minnesota’s Sunrise Plan is presented. For each decision made, its impact was assessed, and results were used to get a measure of confidence. We believe that this flexible tool can be a valuable asset for various kinds of organizations to assess their infection risks in pandemic-time operations, including middle and high schools, factories, warehouses, and small/medium-sized businesses.

Using simulation modelling and systems science to help contain COVID‐19: A systematic review

“…Fatehi et al ( 2021 ) evaluated the effectiveness of two forms of therapies, that is, remdesivir and convalescent plasma (CP) therapy. Forty‐five papers evaluated the impacts of different NPIs on COVID‐19 containment in specific organizations, including elementary or secondary schools (Asgary et al, 2021 ; Morrison et al, 2021 ; Zafarnejad & Griffin, 2021 ), colleges and universities (Bahl et al, 2021 ; Brennan et al, 2021 ; Goyal et al, 2021 ; Kharkwal et al, 2021 ; Lv et al, 2021 ), hospital (Campos et al, 2022 ; Huang et al, 2021 ; Mukherjee et al, 2021 ), army training post (Espana et al, 2021 ), refugee camp (Gilman et al, 2020 ), nursing and care home (Holmdahl et al, 2021 , 2022 ; Kahn et al, 2022 ; Lasser et al, 2021 ; Nguyen et al, 2021 ; Stevenson et al, 2021 ), long‐term care facility (Vilches et al, 2021 ), church (Rothrock et al, 2021 ), supermarket (Tong et al, 2021 ) and construction site (Alzu’bi et al, 2021 ). Three of them explored the effects of NPIs on special events, including two rituals of the Hajj (Al‐Shaery et al, 2021 ), wedding ceremony (Alzu’bi et al, 2021 ) and indoor gathering (Farthing & Lanzas, 2021b ).…”

“…Fatehi et al (2021) evaluated the effectiveness of two forms of therapies, that is, remdesivir and convalescent plasma (CP) therapy. Forty-five papers evaluated the impacts of different NPIs on COVID-19 containment in specific organizations, including elementary or secondary schools (Asgary et al, 2021;Morrison et al, 2021;Zafarnejad & Griffin, 2021), colleges and universities (Bahl et al, 2021;Brennan et al, 2021;Goyal et al, 2021;Kharkwal et al, 2021;Lv et al, 2021), hospital (Campos et al, 2022;Huang et al, 2021;Mukherjee et al, 2021), army training post (Espana et al, 2021), refugee camp (Gilman et al, 2020), nursing and care home (Holmdahl et al, 2021(Holmdahl et al, , 2022Kahn et al, 2022;Lasser et al, 2021;Nguyen et al, 2021;Stevenson et al, 2021), long-term care facility (Vilches et al, 2021), church (Rothrock et al, 2021), supermarket (Tong et al, 2021) There were 158 papers related to interventions evaluation at the national and regional levels (refer to Table 1). The six major categories of NPIs used are as follows: (1) mobility restrictions used to prevent seeding during the early outbreak period, including public transport and travel restrictions; (2) identification mechanisms, including screening, testing, diagnosing and reporting; (3) isolation and quarantine measures, including forced isolation, self-quarantine, community isolation and contact tracing of people who were suspected or confirmed to have the disease or who were exposed to the infected; (4) social distancing or contact restrictions implemented to reduce the risk of exposure at the community level, including lockdown, curfew, staying at home and workplace and school closures; (5) personal preventive measures including personal protective equipment (PPE; e.g., facemasks) and frequent handwashing; and (6) healthcare capacity or hospital capacity, including isolation or quarantine beds and ICU beds.…”

SARS-CoV-2 Transmission in University Classes

“…Many individual level models have a compartmental structure, such as SIR (susceptible, infectious, removed) or SEIR (susceptible, exposed, infectious, removed) (Brauer, 2008;Deardon et al, 2010), where individuals are assigned to a category based on their disease status, and the researcher models how individuals move between categories. While much work has been done on modelling community transmission (see, e.g., BCCDC, 2021; Chang et al, 2020;Rȃdulescu et al, 2020;Tuite et al, 2020), some authors have instead directed their efforts toward understanding outbreaks on university campuses (Gressman and Peck, 2020; Kharkwal et al, 2020;Frazier, 2020;Bahl et al, 2020;Borowiak et al, 2020;Ambatipudi et al, 2021;Weeden and Cornwell, 2020). Gressman and Peck (2020) simulated social dynamics within a university and the corresponding infection rates.…”

“…In a seminar presentation, Frazier (2020) discussed both individual-level and differential equation models for disease spread at a large campus; paying particular attention to universal testing schemes and strategies for contact tracing. Kharkwal et al (2020) developed a detailed framework for simulating infections, which integrates models of various phenomena related to the disease into a single simulation. Ambatipudi et al (2021) developed a framework for assessing risk of infection over the course of a semester based on room crowding and air circulation.…”