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Dynamics, Inference, and Simulation Studies in Epidemiology: Relating Transmission and Aetiology to Treatment for Infectious Organisms of Note- [electronic resource]
Dynamics, Inference, and Simulation Studies in Epidemiology: Relating Transmission and Aetiology to Treatment for Infectious Organisms of Note- [electronic resource]
상세정보
- 자료유형
- 학위논문파일 국외
- 최종처리일시
- 20240214100113
- ISBN
- 9798380128384
- DDC
- 614.4
- 서명/저자
- Dynamics, Inference, and Simulation Studies in Epidemiology: Relating Transmission and Aetiology to Treatment for Infectious Organisms of Note - [electronic resource]
- 발행사항
- [S.l.]: : The University of Utah., 2023
- 발행사항
- Ann Arbor : : ProQuest Dissertations & Theses,, 2023
- 형태사항
- 1 online resource(150 p.)
- 주기사항
- Source: Dissertations Abstracts International, Volume: 85-02, Section: B.
- 주기사항
- Advisor: Adler, Frederick R.
- 학위논문주기
- Thesis (Ph.D.)--The University of Utah, 2023.
- 사용제한주기
- This item must not be sold to any third party vendors.
- 초록/해제
- 요약What does a future with SARS-CoV-2 look like? Will it continue to place a severe strain on our healthcare systems, or could it transition to becoming a common cold-causing virus? Either way, as the COVID-19 pandemic transitions to endemicity, bacterial infections, such as those caused by Staphylococcus aureus, will take on renewed importance. As far as their transmission dynamics are concerned, there has been much debate surrounding the notion that some people serve as a reservoir, but is that necessarily true? And when infections do occur, what makes them pathogenic or virulent? To add yet another complication: how do all the diverse species of pathogens that cause infection interact with each other to shape the epidemiology of infectious disease considered as a whole?In the following chapters, we delve into each of these questions. The first two chapters are previously published work, where we use mathematical models to make projections about the future of SARS-CoV-2, and develop statistical frameworks to link mathematical models to data of bacterial colonization. The third chapter studies virulence regulation in S. aureus with a mathematical model, and the fourth leverages mathematical models and likelihood-based statistics in the study of Syndromic Trend data from bioMerieux to address a fundamental question: how often do distinct pathogens coinfect the same host?
- 일반주제명
- Epidemiology.
- 일반주제명
- Biology.
- 일반주제명
- Microbiology.
- 일반주제명
- Biostatistics.
- 키워드
- SARS-CoV-2
- 키워드
- Virulence
- 기타저자
- The University of Utah Mathematics
- 기본자료저록
- Dissertations Abstracts International. 85-02B.
- 기본자료저록
- Dissertation Abstract International
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■00520240214100113
■006m o d
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■020 ▼a9798380128384
■035 ▼a(MiAaPQ)AAI30422115
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a614.4
■1001 ▼aBeams, Alexander Brown.
■24510▼aDynamics, Inference, and Simulation Studies in Epidemiology: Relating Transmission and Aetiology to Treatment for Infectious Organisms of Note▼h[electronic resource]
■260 ▼a[S.l.]:▼bThe University of Utah. ▼c2023
■260 1▼aAnn Arbor :▼bProQuest Dissertations & Theses, ▼c2023
■300 ▼a1 online resource(150 p.)
■500 ▼aSource: Dissertations Abstracts International, Volume: 85-02, Section: B.
■500 ▼aAdvisor: Adler, Frederick R.
■5021 ▼aThesis (Ph.D.)--The University of Utah, 2023.
■506 ▼aThis item must not be sold to any third party vendors.
■520 ▼aWhat does a future with SARS-CoV-2 look like? Will it continue to place a severe strain on our healthcare systems, or could it transition to becoming a common cold-causing virus? Either way, as the COVID-19 pandemic transitions to endemicity, bacterial infections, such as those caused by Staphylococcus aureus, will take on renewed importance. As far as their transmission dynamics are concerned, there has been much debate surrounding the notion that some people serve as a reservoir, but is that necessarily true? And when infections do occur, what makes them pathogenic or virulent? To add yet another complication: how do all the diverse species of pathogens that cause infection interact with each other to shape the epidemiology of infectious disease considered as a whole?In the following chapters, we delve into each of these questions. The first two chapters are previously published work, where we use mathematical models to make projections about the future of SARS-CoV-2, and develop statistical frameworks to link mathematical models to data of bacterial colonization. The third chapter studies virulence regulation in S. aureus with a mathematical model, and the fourth leverages mathematical models and likelihood-based statistics in the study of Syndromic Trend data from bioMerieux to address a fundamental question: how often do distinct pathogens coinfect the same host?
■590 ▼aSchool code: 0240.
■650 4▼aEpidemiology.
■650 4▼aBiology.
■650 4▼aMicrobiology.
■650 4▼aBiostatistics.
■653 ▼aDifferential equations
■653 ▼aMaximum likelihood
■653 ▼aRespiratory pathogens
■653 ▼aSARS-CoV-2
■653 ▼aStaphylococcus aureus
■653 ▼aVirulence
■690 ▼a0766
■690 ▼a0306
■690 ▼a0410
■690 ▼a0308
■71020▼aThe University of Utah▼bMathematics.
■7730 ▼tDissertations Abstracts International▼g85-02B.
■773 ▼tDissertation Abstract International
■790 ▼a0240
■791 ▼aPh.D.
■792 ▼a2023
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T16931755▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
■980 ▼a202402▼f2024
