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Inference of Intervention Impact and Epidemic Intensity for Infectious Diseases
Inference of Intervention Impact and Epidemic Intensity for Infectious Diseases
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20260202103002
- ISBN
- 9798280709799
- DDC
- 614.4
- 서명/저자
- Inference of Intervention Impact and Epidemic Intensity for Infectious Diseases
- 발행사항
- [Sl] : Harvard University, 2025
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2025
- 형태사항
- 136 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 86-12, Section: B.
- 주기사항
- Advisor: Lipsitch, Marc.
- 학위논문주기
- Thesis (Ph.D.)--Harvard University, 2025.
- 초록/해제
- 요약Outbreaks of SARS-CoV-2, HIV, and other infectious pathogens have a profound impact on public health globally. Public health surveillance, defined as "ongoing systematic collection, analysis, and interpretation of health data," serves two primary purposes in informing public health decisions: Evaluating the impact of infectious disease interventions and estimating epidemic intensity.In Chapter 2, we use surveillance data to estimate vaccine-preventable COVID-19-associated deaths among unvaccinated individuals in the United States. We estimate that hundreds of thousands of deaths could have been directly prevented through vaccination among unvaccinated adults during the 15-month study period. In Chapter 3, we examine the commonly held assumption that due to the indirect effects of vaccination in preventing transmissions, vaccination could have prevented more outcomes overall across the entire population than could have directly among unvaccinated individuals (or that vaccination has prevented more outcomes overall than it has directly among the vaccinated individuals). We demonstrate that the direct impact of vaccination among vaccinated (or unvaccinated) individuals is a lower bound on overall impact across all individuals when indirect effects are non-negative. Using simulations, we illustrate how this lower bound may fail under common violations to assumptions on time-invariant vaccine efficacy, pathogen properties, or behavioral parameters.In Chapter 4, we explore key considerations when using routinely collected data on HIV diagnosis and status ascertainment among pregnant women attending antenatal care (ANC) as a sentinel population to monitor HIV incidence trends in the population at large in generalized HIV epidemic settings. However, ancillary factors-such as those related to fertility, non-disclosure of status, and testing patterns outside of ANC-may also influence trends in ANC surveillance measures, even though incidence remain unchanged. Using simulations and data from a recent study as an example, we demonstrate that trends in ANC surveillance measures may be explained by changes in these ancillary factors, leading to biased incidence estimates if unaccounted. Our findings highlight the importance of accounting for these ancillary factors when interpreting trends in the ANC measures to infer incidence trends. In addition, we show that when incidence is low, a modest, unaccounted change in non-disclosure proportion produce trends in the new diagnoses rate similar to those caused by a large decrease in incidence. Therefore, true new diagnoses should be distinguished from non-disclosing re-tests for the new diagnoses rate to be a more useful measure for estimating incidence trend.
- 일반주제명
- Epidemiology
- 일반주제명
- Public health
- 키워드
- Causal inference
- 키워드
- COVID-19
- 키워드
- HIV
- 기타저자
- Harvard University Population Health Sciences
- 기본자료저록
- Dissertations Abstracts International. 86-12B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■020 ▼a9798280709799
■035 ▼a(MiAaPQ)AAI31840235
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a614.4
■1001 ▼aJia, Katherine Min.▼0(orcid)0000-0001-8875-2415
■24510▼aInference of Intervention Impact and Epidemic Intensity for Infectious Diseases
■260 ▼a[Sl]▼bHarvard University▼c2025
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2025
■300 ▼a136 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 86-12, Section: B.
■500 ▼aAdvisor: Lipsitch, Marc.
■5021 ▼aThesis (Ph.D.)--Harvard University, 2025.
■520 ▼aOutbreaks of SARS-CoV-2, HIV, and other infectious pathogens have a profound impact on public health globally. Public health surveillance, defined as "ongoing systematic collection, analysis, and interpretation of health data," serves two primary purposes in informing public health decisions: Evaluating the impact of infectious disease interventions and estimating epidemic intensity.In Chapter 2, we use surveillance data to estimate vaccine-preventable COVID-19-associated deaths among unvaccinated individuals in the United States. We estimate that hundreds of thousands of deaths could have been directly prevented through vaccination among unvaccinated adults during the 15-month study period. In Chapter 3, we examine the commonly held assumption that due to the indirect effects of vaccination in preventing transmissions, vaccination could have prevented more outcomes overall across the entire population than could have directly among unvaccinated individuals (or that vaccination has prevented more outcomes overall than it has directly among the vaccinated individuals). We demonstrate that the direct impact of vaccination among vaccinated (or unvaccinated) individuals is a lower bound on overall impact across all individuals when indirect effects are non-negative. Using simulations, we illustrate how this lower bound may fail under common violations to assumptions on time-invariant vaccine efficacy, pathogen properties, or behavioral parameters.In Chapter 4, we explore key considerations when using routinely collected data on HIV diagnosis and status ascertainment among pregnant women attending antenatal care (ANC) as a sentinel population to monitor HIV incidence trends in the population at large in generalized HIV epidemic settings. However, ancillary factors-such as those related to fertility, non-disclosure of status, and testing patterns outside of ANC-may also influence trends in ANC surveillance measures, even though incidence remain unchanged. Using simulations and data from a recent study as an example, we demonstrate that trends in ANC surveillance measures may be explained by changes in these ancillary factors, leading to biased incidence estimates if unaccounted. Our findings highlight the importance of accounting for these ancillary factors when interpreting trends in the ANC measures to infer incidence trends. In addition, we show that when incidence is low, a modest, unaccounted change in non-disclosure proportion produce trends in the new diagnoses rate similar to those caused by a large decrease in incidence. Therefore, true new diagnoses should be distinguished from non-disclosing re-tests for the new diagnoses rate to be a more useful measure for estimating incidence trend.
■590 ▼aSchool code: 0084.
■650 4▼aEpidemiology
■650 4▼aPublic health
■653 ▼aCausal inference
■653 ▼aCOVID-19
■653 ▼aHIV
■653 ▼aInfectious disease modeling
■653 ▼aVaccine-averted deaths
■653 ▼aVaccine-preventable deaths
■690 ▼a0766
■690 ▼a0573
■690 ▼a0769
■71020▼aHarvard University▼bPopulation Health Sciences.
■7730 ▼tDissertations Abstracts International▼g86-12B.
■790 ▼a0084
■791 ▼aPh.D.
■792 ▼a2025
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17356611▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
