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dc.creatorGressman, Philip T.-
dc.creatorPeck, Jennifer R.-
dc.date.accessioned2020-08-31T20:15:47Z-
dc.date.available2020-08-31T20:15:47Z-
dc.date.issued2020-10-
dc.identifier.citationGRESSMAN, P. T.; PECK, J. R. Simulating COVID-19 in a university environment. Mathematical Biosciences, [S.l.], v. 328, Oct. 2020.pt_BR
dc.identifier.urihttps://www.sciencedirect.com/science/article/pii/S0025556420301048pt_BR
dc.identifier.urihttp://repositorio.ufla.br/jspui/handle/1/42781-
dc.description.abstractResidential colleges and universities face unique challenges in providing in-person instruction during the COVID-19 pandemic. Administrators are currently faced with decisions about whether to open during the pandemic and what modifications of their normal operations might be necessary to protect students, faculty and staff. There is little information, however, on what measures are likely to be most effective and whether existing interventions could contain the spread of an outbreak on campus. We develop a full-scale stochastic agent-based model to determine whether in-person instruction could safely continue during the pandemic and evaluate the necessity of various interventions. Simulation results indicate that large scale randomized testing, contact-tracing, and quarantining are important components of a successful strategy for containing campus outbreaks. High test specificity is critical for keeping the size of the quarantine population manageable. Moving the largest classes online is also crucial for controlling both the size of outbreaks and the number of students in quarantine. Increased residential exposure can significantly impact the size of an outbreak, but it is likely more important to control non-residential social exposure among students. Finally, necessarily high quarantine rates even in controlled outbreaks imply significant absenteeism, indicating a need to plan for remote instruction of quarantined students.pt_BR
dc.languageen_USpt_BR
dc.publisherElsevierpt_BR
dc.rightsrestrictAccesspt_BR
dc.sourceMathematical Biosciencespt_BR
dc.subjectCOVID-19pt_BR
dc.subjectCoronaviruspt_BR
dc.subjectSevere Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2)pt_BR
dc.subjectEpidemicspt_BR
dc.subjectComputational epidemiologypt_BR
dc.subjectAgent-based modelingpt_BR
dc.subjectHigher educationpt_BR
dc.subjectPost-secondary educationpt_BR
dc.titleSimulating COVID-19 in a university environmentpt_BR
dc.typeArtigopt_BR
Aparece nas coleções:FCS - Artigos sobre Coronavirus Disease 2019 (COVID-19)

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