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Please use this identifier to cite or link to this item: http://arks.princeton.edu/ark:/88435/dsp01kp78gj86v
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dc.contributor.advisorGrenfell, Bryan T-
dc.contributor.authorBirger, Ruthie Breina-
dc.contributor.otherEcology and Evolutionary Biology Department-
dc.date.accessioned2016-11-22T21:36:39Z-
dc.date.available2017-11-21T09:05:17Z-
dc.date.issued2016-
dc.identifier.urihttp://arks.princeton.edu/ark:/88435/dsp01kp78gj86v-
dc.description.abstractThe Human Immunodeficiency Virus (HIV) pandemic is one of the largest events to impact global human health in the past 30 years. Epidemic and within-host infection dynamics are affected by co-infections, especially by Hepatitis C Virus (HCV). In this dissertation, I present mathematical modeling analyses across scales to answer the question of which strategies have the greatest impact on controlling the co-epidemics of HIV and HCV. I address this question from the perspective of public health policy and intervention (Chapters 1-2), discussing the epidemiological dynamics for HIV in Newark, NJ, a city with one of the most severe epidemics in the US. This research shows that for there to be significant impact on incidence, care-continuum interventions must be bundled; and Pre-Exposure Prophylaxis (PrEP) is most effective when targeted at specific high-risk populations. These results underscore the need for addressing the ``leaky'' care pipeline. I highlight the role of immune function in HCV clearance in a within-host model of HCV/HIV coinfection dynamics that incorporates treatment efficacy. Our analysis sheds light on the tradeoffs involved in choosing between treatment protocols, and how both duration and efficacy need to be taken into account carefully in coinfected patients, especially in light of new direct-acting antiviral treatments (DAAs) that are becoming available (Chapter 3). Focusing in on HCV mono-infection, I build on the methodology and framework discussed in Chapter 3 to explore HCV's unusual viral evolution dynamics. Testing various hypotheses including spatial-structure, latency, extra-hepatic replication and selective sweeps in a model of viral evolution can help elucidate HCV within-host dynamics, which can aid in effective treatment design (Chapter 4). Coinfection and epidemiological modeling are combined in a nested approach that I use to explore relative impacts of antiretroviral and methadone maintenance treatment scale-up, and HCV treatment rollout on HIV/HCV disease burden in Ho Chi Minh City, Vietnam (Chapter 5). These model results indicate that scale-up of antiretroviral therapy has a major impact on HIV, but a negligible impact on HCV. Methadone scale-up has an impact on both infections, and HCV treatment roll-out can increase multifold the reductions in death rates afforded by the other interventions.-
dc.language.isoen-
dc.publisherPrinceton, NJ : Princeton University-
dc.relation.isformatofThe Mudd Manuscript Library retains one bound copy of each dissertation. Search for these copies in the library's main catalog: <a href=http://catalog.princeton.edu> catalog.princeton.edu </a>-
dc.subjectCoinfection-
dc.subjectHepatitis C-
dc.subjectHIV-
dc.subjectMathematical Model-
dc.subject.classificationBiology-
dc.subject.classificationMathematics-
dc.subject.classificationPublic health-
dc.titleWithin-Host and Population-level Modeling of Human Immunodeficiency Virus and Hepatitis C Virus Dynamics-
dc.typeAcademic dissertations (Ph.D.)-
pu.projectgrantnumber690-2143-
pu.embargo.terms2017-11-21-
Appears in Collections:Ecology and Evolutionary Biology

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