Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.656820
Title: Using mathematical models to characterize HIV epidemics for the design of HIV prevention strategies
Author: Mishra, Sharmistha
ISNI:       0000 0004 5349 6687
Awarding Body: Imperial College London
Current Institution: Imperial College London
Date of Award: 2014
Availability of Full Text:
Access through EThOS:
Full text unavailable from EThOS. Please try the link below.
Access through Institution:
Abstract:
Since 2000, we have been trying to characterize and classify HIV epidemics to guide the strategic design of HIV prevention policies and focus HIV programmes and resource allocation by a regions' epidemic type. We have used arbitrary thresholds of HIV prevalence across different risk-groups in a given population, 'static' mathematical models and classical epidemiological measures of the population attributable fraction that do not account for chains of transmission. As a result, these traditional approaches could be missing the underlying transmission dynamics and the role of key populations - such as female sex workers and their clients - on HIV spread. In this thesis, I build on a growing paradigm shift on how we should re-classify HIV epidemics based on the epidemiological features that lead to HIV emergence and persistence (i.e. the 'epidemic drivers' that influence the basic reproductive ratio, R0). I examine the extent to which our traditional approaches have been underestimating the contribution of sex work to HIV spread and likely misclassifying epidemic type by developing dynamic mathematical models of HIV transmission and simulating a large number of plausible 'synthetic' HIV epidemics. I then develop - as proof-of-concept - a novel algorithm to diagnose epidemic type using these synthetic epidemics and glean the key epidemiological data that would be most useful to help distinguish between 'epidemic drivers', and therefore would be most useful to collect as part of HIV surveillance and future empirical research.
Supervisor: Boily, Marie-Claude; Garnett, Geoffrey; Moses, Stephen Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.656820  DOI: Not available
Share: