Simulation-Based Analysis of COVID-19 Spread Through Classroom Transmission on a University Campus

Hekmati, Arvin; Luhar, Mitul; Krishnamachari, Bhaskar; Matarić, Maja J.

doi:10.1109/iccworkshops50388.2021.9473803

Cited by 5 publications

(6 citation statements)

References 16 publications

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“…Due to a high studentfaculty ratio at universities, for simplicity we only consider the behaviors of students. [1] Each student is enrolled in a class. Students go to classes according to their class schedules.…”

Section: Time-varying and Heterogeneous Contact Network In Residentia...mentioning

confidence: 99%

“…Many research works have proposed models to study COVID-19 spread in such settings. [1][2][3][4][5][6] For instance, Hekmati et al studied the airborne transmission risk associated with holding in-person classes on university campuses. [1] Borowiak et al used mathematical models to evaluate strategies that suppress the spread of the virus, specifically in dorms and in classrooms.…”

Section: Introductionmentioning

confidence: 99%

“…[1][2][3][4][5][6] For instance, Hekmati et al studied the airborne transmission risk associated with holding in-person classes on university campuses. [1] Borowiak et al used mathematical models to evaluate strategies that suppress the spread of the virus, specifically in dorms and in classrooms. [2] When there is an epidemic outbreak, non-pharmaceutical interventions (NPIs) are effective methods to contain infection and delay spread, and include isolation of ill individuals, quarantine of exposed individuals, face mask usage, nucleic acid testing, cancellation of mass gatherings, and school and workplace closures.…”

Section: Introductionmentioning

confidence: 99%

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Epidemic threshold influenced by non-pharmaceutical interventions in residential university environments

Zhao

et al. 2024

Chinese Phys. B

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The control of highly contagious disease spreading in campus is a critical challenge. In residential universities, students attend classes according to curriculum schedule, and mainly pack into classrooms, dining halls and dorms. They move from one place to another. To simulate such environments, we propose an agent-based susceptible-infected-recovered model with time-varying heterogeneous contact networks. In close environments, keeping physical distancing is the mostly recommended and encouraged non-pharmaceutical intervention. It can be easily realized by using larger classroom, adopting staggered dining hours, decreasing the number of students per dorm and so on. Their real-world influence remains uncertain. With numerical simulations, we obtain epidemic thresholds. The effect of such countermeasures on reducing the number of disease cases is also quantitatively evaluated.

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Section: Time-varying and Heterogeneous Contact Network In Residentia...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Epidemic threshold influenced by non-pharmaceutical interventions in residential university environments

Zhao

et al. 2024

Chinese Phys. B

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“…According to Fatonia et al ( 2020 ), advantages, disadvantages, and solutions of online learning for university students were investigated. Hekmati et al ( 2021 ) examined the risk of contagion with the classroom arrangement in the environment where face-to-face lectures are held on university campuses. On the other hand, Fischetti et al ( 2021 ) examined the effect of table, armchair, etc., placement on the risk of infection.…”

Section: Literature Reviewmentioning

confidence: 99%

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

Baskak

Ozbey

Yücesan

et al. 2022

Environ Sci Pollut Res

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The fight against the COVID-19 pandemic, which has affected the whole world in recent years and has had devastating effects on all segments of society, has been one of the most important priorities. The Turkish Standards Institution has determined a checklist to contribute to developing safe and clean environments in higher education institutions in Turkey and to follow-up on infection control measures. However, this study is only a checklist that makes it necessary for decision-makers to make a subjective evaluation during the evaluation process, while the need to develop a more effective, systematic framework that takes into account the importance levels of multiple criteria has emerged. Therefore, this study applies the best-worst method under interval type-2 fuzzy set concept (IT2F-BWM) to determine the importance levels of criteria affecting the “COVID-19 safe campus” evaluation of universities in the context of global pandemic. A three-level hierarchy consisting of three main criteria, 11 sub-criteria, and 58 sub-criteria has been created for this aim. Considering the hierarchy, the most important sub-criterion was determined as periodic disinfection. The high contribution of the interval-valued type-2 fuzzy sets in expressing the uncertainty in the decision-makers’ evaluations and the fact that BWM provides criterion weights with a mathematical optimization model that produces less pairwise comparisons and higher consistency are the main factors in choosing this approach. Simple additive weighting (SAW) has also been injected into the IT2F-BWM to determine the safety level of any university campus regarding COVID-19. Thus, decision-makers will be better prepared for the devastating effects of the pandemic by first improving the factors that are relatively important in the fight against the pandemic. In addition, a threshold value will be determined by considering all criteria, and it will prepare the ground for a road map for campuses. A case study is employed to apply the proposed model, and a comparison study is also presented with the Bayesian BWM to validate the results of the criteria weights. Supplementary Information The online version contains supplementary material available at 10.1007/s11356-022-22796-1.

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“…This paper uses a previously established modeling framework ( 19 ) that studied the impact of different university policies on the transmission of the original strain of SARS-CoV-2 through classroom interactions at universities by considering parameters such as occupancy, mask usage, and initial infection rates for students and instructors. In this paper, we incorporate the impact of vaccinations into the modeling framework and evaluate how vaccination rates and vaccine efficacy impact the spread of the virus on a university campus.…”

mentioning

confidence: 99%

Simulating COVID-19 classroom transmission on a university campus

Hekmati

Luhar

Krishnamachari

et al. 2022

Proc. Natl. Acad. Sci. U.S.A.

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Significance This paper simulates the spread of COVID-19 at universities via airborne transmission in classroom settings. The transmission risk model used for these simulations accounts for student-specific class schedules, classroom sizes and occupancy, and ventilation rates, as well as vaccination rate and efficacy. We show the simulations reproduce trends observed in weekly infection rates at a large US university. We also evaluate the impact of campus operational policies. Model predictions show moving 90% of classes online can reduce new infections by as much as 18 × , and universal mask usage can reduce new infections by up to 3.6 × . For full-time in-person instruction, high vaccination rates are predicted to curb transmission even for more contagious variants of severe acute respiratory syndrome coronavirus 2.

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Simulation-Based Analysis of COVID-19 Spread Through Classroom Transmission on a University Campus

Cited by 5 publications

References 16 publications

Epidemic threshold influenced by non-pharmaceutical interventions in residential university environments

Epidemic threshold influenced by non-pharmaceutical interventions in residential university environments

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

Simulating COVID-19 classroom transmission on a university campus

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