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Tackling COVID-19 Challenges at Cornell University: Stochastic Modeling, Simulation, and Statistics
Tackling COVID-19 Challenges at Cornell University: Stochastic Modeling, Simulation, and Statistics
Detailed Information
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20250211151332
- ISBN
- 9798382840185
- DDC
- 519
- 저자명
- Wan, Jiayue.
- 서명/저자
- Tackling COVID-19 Challenges at Cornell University: Stochastic Modeling, Simulation, and Statistics
- 발행사항
- [Sl] : Cornell University, 2024
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- 형태사항
- 252 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 85-12, Section: B.
- 주기사항
- Advisor: Frazier, Peter.
- 학위논문주기
- Thesis (Ph.D.)--Cornell University, 2024.
- 초록/해제
- 요약Amidst the global challenge posed by the COVID-19 pandemic in early 2020, countries implemented containment measures to curb the virus's spread, leading to widespread disruptions. Cornell University faced the dilemma of whether to reopen its campus for in-person instruction in the fall of 2020 among uncertainties about disease transmission. Leveraging operations research methods such as stochastic modeling, simulation, and statistics, the Cornell COVID-19 mathematical modeling team, of which I was a member, arrived at a somewhat surprising conclusion that prompted the university's decision to reopen: In-person instruction would offer greater safety to students than virtual classes.Over the subsequent two years, the team continued to study various aspects of the virus and disease, providing recommendations on travel policies, vaccine mandates, and other measures, in response to emerging variants and evolving social distancing protocols. Building on these efforts, this dissertation presents a series of research projects that deepen our understanding of COVID-19 and advance strategies for combating infectious diseases. These projects include theoretical exploration of group testing, modeling analysis of gateway testing protocols, retrospective calibration of stochastic simulation models, and statistical analysis of booster vaccination effectiveness during Omicron outbreaks. Together, they highlight the critical role of operations research in informing pandemic response and broader public health decision-making.
- 일반주제명
- Applied mathematics
- 일반주제명
- Epidemiology
- 일반주제명
- Public health
- 일반주제명
- Biostatistics
- 키워드
- COVID-19
- 키워드
- Group testing
- 키워드
- Simulation
- 기타저자
- Cornell University Operations Research and Information Engineering
- 기본자료저록
- Dissertations Abstracts International. 85-12B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■020 ▼a9798382840185
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■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a519
■1001 ▼aWan, Jiayue.▼0(orcid)0000-0002-8252-1584
■24510▼aTackling COVID-19 Challenges at Cornell University: Stochastic Modeling, Simulation, and Statistics
■260 ▼a[Sl]▼bCornell University▼c2024
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2024
■300 ▼a252 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 85-12, Section: B.
■500 ▼aAdvisor: Frazier, Peter.
■5021 ▼aThesis (Ph.D.)--Cornell University, 2024.
■520 ▼aAmidst the global challenge posed by the COVID-19 pandemic in early 2020, countries implemented containment measures to curb the virus's spread, leading to widespread disruptions. Cornell University faced the dilemma of whether to reopen its campus for in-person instruction in the fall of 2020 among uncertainties about disease transmission. Leveraging operations research methods such as stochastic modeling, simulation, and statistics, the Cornell COVID-19 mathematical modeling team, of which I was a member, arrived at a somewhat surprising conclusion that prompted the university's decision to reopen: In-person instruction would offer greater safety to students than virtual classes.Over the subsequent two years, the team continued to study various aspects of the virus and disease, providing recommendations on travel policies, vaccine mandates, and other measures, in response to emerging variants and evolving social distancing protocols. Building on these efforts, this dissertation presents a series of research projects that deepen our understanding of COVID-19 and advance strategies for combating infectious diseases. These projects include theoretical exploration of group testing, modeling analysis of gateway testing protocols, retrospective calibration of stochastic simulation models, and statistical analysis of booster vaccination effectiveness during Omicron outbreaks. Together, they highlight the critical role of operations research in informing pandemic response and broader public health decision-making.
■590 ▼aSchool code: 0058.
■650 4▼aApplied mathematics
■650 4▼aEpidemiology
■650 4▼aPublic health
■650 4▼aBiostatistics
■653 ▼aCOVID-19
■653 ▼aEpidemiological modeling
■653 ▼aGroup testing
■653 ▼aSimulation
■653 ▼aStochastic modeling
■690 ▼a0796
■690 ▼a0364
■690 ▼a0766
■690 ▼a0308
■690 ▼a0573
■71020▼aCornell University▼bOperations Research and Information Engineering.
■7730 ▼tDissertations Abstracts International▼g85-12B.
■790 ▼a0058
■791 ▼aPh.D.
■792 ▼a2024
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17161265▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
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