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Safe University Instruction During COVID-19: Simulation, Statistics, and Uncertainty Quantification
Safe University Instruction During COVID-19: Simulation, Statistics, and Uncertainty Quantification
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20250211151333
- ISBN
- 9798382841649
- DDC
- 519
- 저자명
- Zhang, Yujia.
- 서명/저자
- Safe University Instruction During COVID-19: Simulation, Statistics, and Uncertainty Quantification
- 발행사항
- [Sl] : Cornell University, 2024
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- 형태사항
- 223 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 85-12, Section: B.
- 주기사항
- Advisor: Frazier, Peter.
- 학위논문주기
- Thesis (Ph.D.)--Cornell University, 2024.
- 초록/해제
- 요약The COVID-19 pandemic has inflicted significant losses and disruptions on the society since its emergence in 2020. During this difficult time, colleges and universities faced numerous operational decisions that needed to balance safety, educational quality, and cost. This dissertation focuses on a few projects that partly supported safe in-person instruction at Cornell University during COVID-19 and hold great promise for broader applications.First, we study the risk of returning to pre-pandemic level in-person instruction through mathematical modeling and agent-based simulation. We estimate the risk associated with different policies and recommend that fully masked in-person classrooms would be safe without needing to assign seats or update the rooms for better ventilation. This result supported the university's decision to return to regular in-person instruction in Fall 2021.Second, we conduct survival analysis to evaluate the risk of infection associated with attending classes in person. Using data on surveillance testing, class schedules, and class enrollments in Fall 2021 and Spring 2022, we construct a novel feature to quantify the amount of exposure a student has in the classroom. Using extended Cox regression and logistic regression, we find that attending classes was associated with minimal increase in the risk of infection.Third, we investigate group testing under the presence of correlation among samples. In large-scale screenings, correlation between samples in the same pool is naturally induced through human behavior and the process of sample collection. By realistically modeling network contagion, viral load progression, and the dilution effect in pooled testing, we show that such correlation improves the sensitivity and resource efficiency of population-wide testing. Thus, policy-makers envisioning using group testing for large-scale screening should take correlation into account and intentionally maximize it when possible.Fourth, we present an approach for uncertainty quantification of simulation models with a large number of parameters. Using a linear approximation, we quantify the sensitivity of simulation output to each parameter. Furthermore, we adapt ideas from robust optimization and identify a one-dimensional family of parameter configurations associated with different pessimism levels. This method provides insight into the uncertainty of the compartmental simulation developed by the Cornell COVID-19 modeling team, and can be broadly used for sensitivity analysis and scenario analysis in an interpretable way for various simulation models.
- 일반주제명
- Applied mathematics
- 일반주제명
- Epidemiology
- 일반주제명
- Public health
- 일반주제명
- Statistics
- 키워드
- COVID-19
- 키워드
- Group testing
- 기타저자
- Cornell University Applied Mathematics
- 기본자료저록
- Dissertations Abstracts International. 85-12B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■020 ▼a9798382841649
■035 ▼a(MiAaPQ)AAI31241498
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a519
■1001 ▼aZhang, Yujia.▼0(orcid)0000-0001-7991-6385
■24510▼aSafe University Instruction During COVID-19: Simulation, Statistics, and Uncertainty Quantification
■260 ▼a[Sl]▼bCornell University▼c2024
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2024
■300 ▼a223 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 85-12, Section: B.
■500 ▼aAdvisor: Frazier, Peter.
■5021 ▼aThesis (Ph.D.)--Cornell University, 2024.
■520 ▼aThe COVID-19 pandemic has inflicted significant losses and disruptions on the society since its emergence in 2020. During this difficult time, colleges and universities faced numerous operational decisions that needed to balance safety, educational quality, and cost. This dissertation focuses on a few projects that partly supported safe in-person instruction at Cornell University during COVID-19 and hold great promise for broader applications.First, we study the risk of returning to pre-pandemic level in-person instruction through mathematical modeling and agent-based simulation. We estimate the risk associated with different policies and recommend that fully masked in-person classrooms would be safe without needing to assign seats or update the rooms for better ventilation. This result supported the university's decision to return to regular in-person instruction in Fall 2021.Second, we conduct survival analysis to evaluate the risk of infection associated with attending classes in person. Using data on surveillance testing, class schedules, and class enrollments in Fall 2021 and Spring 2022, we construct a novel feature to quantify the amount of exposure a student has in the classroom. Using extended Cox regression and logistic regression, we find that attending classes was associated with minimal increase in the risk of infection.Third, we investigate group testing under the presence of correlation among samples. In large-scale screenings, correlation between samples in the same pool is naturally induced through human behavior and the process of sample collection. By realistically modeling network contagion, viral load progression, and the dilution effect in pooled testing, we show that such correlation improves the sensitivity and resource efficiency of population-wide testing. Thus, policy-makers envisioning using group testing for large-scale screening should take correlation into account and intentionally maximize it when possible.Fourth, we present an approach for uncertainty quantification of simulation models with a large number of parameters. Using a linear approximation, we quantify the sensitivity of simulation output to each parameter. Furthermore, we adapt ideas from robust optimization and identify a one-dimensional family of parameter configurations associated with different pessimism levels. This method provides insight into the uncertainty of the compartmental simulation developed by the Cornell COVID-19 modeling team, and can be broadly used for sensitivity analysis and scenario analysis in an interpretable way for various simulation models.
■590 ▼aSchool code: 0058.
■650 4▼aApplied mathematics
■650 4▼aEpidemiology
■650 4▼aPublic health
■650 4▼aStatistics
■653 ▼aCOVID-19
■653 ▼aGroup testing
■653 ▼aLogistic regression
■653 ▼aSimulation models
■653 ▼aOperational decisions
■653 ▼aUncertainty quantification
■690 ▼a0364
■690 ▼a0796
■690 ▼a0766
■690 ▼a0463
■690 ▼a0573
■71020▼aCornell University▼bApplied Mathematics.
■7730 ▼tDissertations Abstracts International▼g85-12B.
■790 ▼a0058
■791 ▼aPh.D.
■792 ▼a2024
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17161275▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
