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Multiscale Infectious Disease Dynamics: Linking Epidemiology and Testing for Outbreak Neutralization
Multiscale Infectious Disease Dynamics: Linking Epidemiology and Testing for Outbreak Neutralization
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20260202103125
- ISBN
- 9798314899380
- DDC
- 614.4
- 서명/저자
- Multiscale Infectious Disease Dynamics: Linking Epidemiology and Testing for Outbreak Neutralization
- 발행사항
- [Sl] : University of Colorado at Boulder, 2025
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2025
- 형태사항
- 152 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 86-11, Section: B.
- 주기사항
- Advisor: Larremore, Daniel.
- 학위논문주기
- Thesis (Ph.D.)--University of Colorado at Boulder, 2025.
- 초록/해제
- 요약Infectious disease dynamics are shaped by interactions across multiple scales. Within a host, pathogens interact with cellular and immune processes, driving fluctuations in pathogen concentrations and immune responses. Between hosts, infectious contacts facilitate pathogen transmission, driving fluctuations in case counts and population immunity levels. However, traditional mathematical models often focus on only one of these scales. This dissertation addresses this gap by leveraging multi-scale modeling to examine how within-host dynamics influence population-level transmission. By integrating models of within-host viral and immune dynamics with between-host epidemiological transmission models, this work provides insights into infectious disease spread and intervention strategies.First, a multi-scale model is developed to estimate testing effectiveness, the reduction in transmission due to testing and subsequent isolation, using a probabilistic framework which incorporates viral kinetics, test attributes, and testing behaviors. This model provides a general framework for comparing testing strategies for any virus. Second, these results are incorporated into a compartmental modeling framework to analyze the effectiveness of vaccinate-or-test policies for COVID-19. Lastly, a framework is developed to evaluate our ability to learn about correlates of protection by linking immunological marker concentrations with observed infection events in test-negative design studies. This research highlights the importance of incorporating both within-host and between-host processes to understand infectious disease dynamics and evaluate intervention strategies.
- 일반주제명
- Epidemiology
- 일반주제명
- Applied mathematics
- 일반주제명
- Public health
- 일반주제명
- Computer science
- 키워드
- COVID-19
- 키워드
- Pathogens
- 키워드
- Viral kinetics
- 기타저자
- University of Colorado at Boulder Computer Science
- 기본자료저록
- Dissertations Abstracts International. 86-11B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■020 ▼a9798314899380
■035 ▼a(MiAaPQ)AAI31938683
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a614.4
■1001 ▼aMiddleton, Casey.▼0(orcid)0000-0002-9665-6121
■24510▼aMultiscale Infectious Disease Dynamics: Linking Epidemiology and Testing for Outbreak Neutralization
■260 ▼a[Sl]▼bUniversity of Colorado at Boulder▼c2025
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2025
■300 ▼a152 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 86-11, Section: B.
■500 ▼aAdvisor: Larremore, Daniel.
■5021 ▼aThesis (Ph.D.)--University of Colorado at Boulder, 2025.
■520 ▼aInfectious disease dynamics are shaped by interactions across multiple scales. Within a host, pathogens interact with cellular and immune processes, driving fluctuations in pathogen concentrations and immune responses. Between hosts, infectious contacts facilitate pathogen transmission, driving fluctuations in case counts and population immunity levels. However, traditional mathematical models often focus on only one of these scales. This dissertation addresses this gap by leveraging multi-scale modeling to examine how within-host dynamics influence population-level transmission. By integrating models of within-host viral and immune dynamics with between-host epidemiological transmission models, this work provides insights into infectious disease spread and intervention strategies.First, a multi-scale model is developed to estimate testing effectiveness, the reduction in transmission due to testing and subsequent isolation, using a probabilistic framework which incorporates viral kinetics, test attributes, and testing behaviors. This model provides a general framework for comparing testing strategies for any virus. Second, these results are incorporated into a compartmental modeling framework to analyze the effectiveness of vaccinate-or-test policies for COVID-19. Lastly, a framework is developed to evaluate our ability to learn about correlates of protection by linking immunological marker concentrations with observed infection events in test-negative design studies. This research highlights the importance of incorporating both within-host and between-host processes to understand infectious disease dynamics and evaluate intervention strategies.
■590 ▼aSchool code: 0051.
■650 4▼aEpidemiology
■650 4▼aApplied mathematics
■650 4▼aPublic health
■650 4▼aComputer science
■653 ▼aInfectious diseases
■653 ▼aCOVID-19
■653 ▼aPathogens
■653 ▼aViral kinetics
■653 ▼aMulti-scale modeling
■690 ▼a0766
■690 ▼a0364
■690 ▼a0573
■690 ▼a0984
■71020▼aUniversity of Colorado at Boulder▼bComputer Science.
■7730 ▼tDissertations Abstracts International▼g86-11B.
■790 ▼a0051
■791 ▼aPh.D.
■792 ▼a2025
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17357065▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
