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Capturing Epidemic Dynamics Through Epidemic Surveillance Panels: Approximating Agent-Based Models Using Dynamical Survival Analysis
Capturing Epidemic Dynamics Through Epidemic Surveillance Panels: Approximating Agent-Based Models Using Dynamical Survival Analysis
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20260202105633
- ISBN
- 9798297962033
- DDC
- 312
- 서명/저자
- Capturing Epidemic Dynamics Through Epidemic Surveillance Panels: Approximating Agent-Based Models Using Dynamical Survival Analysis
- 발행사항
- [Sl] : The Ohio State University, 2025
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2025
- 형태사항
- 134 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 87-05, Section: A.
- 주기사항
- Advisor: Rempala, Grzegorz;Kenah, Eben.
- 학위논문주기
- Thesis (Ph.D.)--The Ohio State University, 2025.
- 초록/해제
- 요약Epidemic models have been used to characterize disease dynamics, inform public health interventions, and forecast epidemic spread and its impact on healthcare systems. However, compartmental models are limited in their ability to account for complex dynamics rooted in population heterogeneities. Additionally, these models are often fit using time series of cases detected through clinical testing or other data streams that can produce biased estimates of key epidemiological parameters. This dissertation addresses these limitations by developing a modeling framework that uses compartmental models to locally approximate complex epidemic dynamics. This approach can capture changes in epidemic dynamics due to factors such as changes in human behavior, environment, and pathogen. The first study introduces an agentbased model of the United States and explores three key drivers of racial and ethnic disparities: household composition, occupation, and school setting. The second study extends a modeling framework called dynamical survival analysis (DSA) for estimating epidemic parameters from individual observed infection histories measured in an epidemic surveillance panel, which is a random sample from the population that is followed longitudinally. In the third study, we show how DSA and epidemic surveillance panels could be used to produce more accurate estimates of the effective reproduction number Rt. Collectively, these studies underscore the need for high-resolution, individual-level data streams like those provided by an epidemic surveillance panel.
- 일반주제명
- Demography
- 일반주제명
- Epidemiology
- 일반주제명
- Biostatistics
- 일반주제명
- Public health
- 키워드
- Modeling
- 키워드
- Forecasting
- 키워드
- Surveillance
- 기타저자
- The Ohio State University Biostatistics
- 기본자료저록
- Dissertations Abstracts International. 87-05A.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■020 ▼a9798297962033
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■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a312
■1001 ▼aRichter, Micaela.
■24510▼aCapturing Epidemic Dynamics Through Epidemic Surveillance Panels: Approximating Agent-Based Models Using Dynamical Survival Analysis
■260 ▼a[Sl]▼bThe Ohio State University▼c2025
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2025
■300 ▼a134 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 87-05, Section: A.
■500 ▼aAdvisor: Rempala, Grzegorz;Kenah, Eben.
■5021 ▼aThesis (Ph.D.)--The Ohio State University, 2025.
■520 ▼aEpidemic models have been used to characterize disease dynamics, inform public health interventions, and forecast epidemic spread and its impact on healthcare systems. However, compartmental models are limited in their ability to account for complex dynamics rooted in population heterogeneities. Additionally, these models are often fit using time series of cases detected through clinical testing or other data streams that can produce biased estimates of key epidemiological parameters. This dissertation addresses these limitations by developing a modeling framework that uses compartmental models to locally approximate complex epidemic dynamics. This approach can capture changes in epidemic dynamics due to factors such as changes in human behavior, environment, and pathogen. The first study introduces an agentbased model of the United States and explores three key drivers of racial and ethnic disparities: household composition, occupation, and school setting. The second study extends a modeling framework called dynamical survival analysis (DSA) for estimating epidemic parameters from individual observed infection histories measured in an epidemic surveillance panel, which is a random sample from the population that is followed longitudinally. In the third study, we show how DSA and epidemic surveillance panels could be used to produce more accurate estimates of the effective reproduction number Rt. Collectively, these studies underscore the need for high-resolution, individual-level data streams like those provided by an epidemic surveillance panel.
■590 ▼aSchool code: 0168.
■650 4▼aDemography
■650 4▼aEpidemiology
■650 4▼aBiostatistics
■650 4▼aPublic health
■653 ▼aPopulation heterogeneity
■653 ▼aModeling
■653 ▼aAgent-based models
■653 ▼aSurvival analysis
■653 ▼aForecasting
■653 ▼aSurveillance
■690 ▼a0938
■690 ▼a0766
■690 ▼a0308
■690 ▼a0573
■71020▼aThe Ohio State University▼bBiostatistics.
■7730 ▼tDissertations Abstracts International▼g87-05A.
■790 ▼a0168
■791 ▼aPh.D.
■792 ▼a2025
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17360888▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
