Event
Jeffrey W. Eaton, PhD, Imperial College London
Monday, February 5, 2018 16:00to17:00
McIntyre Medical Building
Room 521, 3655 promenade Sir William Osler, Montreal, QC, H3G 1Y6, CA
New Models And Data For Estimating HIV Epidemic Trends In Sub-Saharan Africa.
Jeff Eaton is a Senior Lecturer in HIV Epidemiology at Imperial College London. His research interests involve developing new mathematical models, statistical methods, and surveillance tools to better characterize epidemiologic trends, transmission dynamics, and the demographic impacts of HIV epidemics in sub-Saharan Africa. He holds a PhD in Infectious Disease Epidemiology from Imperial College London and an MS in Statistics from the University of Washington. Jeff is co-chair of the UNAIDS Reference Group on Estimates, Modelling, and Projections () and co-Investigator of the HIV Modelling Consortium (). Finally, he has a deep interest in the collection analysis of longitudinal population cohort data, working extensively with the Manicaland Centre for Public Health Research in eastern Zimbabwe and as a co-investigator of the ALPHA Network of general population HIV cohort studies.
Every year, UNAIDS supports national governments to create estimates of their HIV epidemic, including estimates of HIV prevalence, new HIV infections, AIDS deaths, and the coverage of HIV treatment and prevention programmes. These estimates form official national HIV estimates and are used by organizations such as PEPFAR and the Global Fund to guide the global HIV response. Estimates are created by fitting a flexible but simple HIV epidemic model to national data sources about HIV prevalence and in a Bayesian statistical framework. In this talk, I will introduce the data, mathematical models, and statistical methods that are used for estimating HIV epidemic trends, describe newly developed models that combine epidemic modelling of transmission stochastic processes to more flexibly and accurately estimate and project recent trends, and discuss opportunities and challenges arising from new data sources for tracking HIV trends, in particular routinely collected health system data from HIV testing, care, and treatment.
Every year, UNAIDS supports national governments to create estimates of their HIV epidemic, including estimates of HIV prevalence, new HIV infections, AIDS deaths, and the coverage of HIV treatment and prevention programmes. These estimates form official national HIV estimates and are used by organizations such as PEPFAR and the Global Fund to guide the global HIV response. Estimates are created by fitting a flexible but simple HIV epidemic model to national data sources about HIV prevalence and in a Bayesian statistical framework. In this talk, I will introduce the data, mathematical models, and statistical methods that are used for estimating HIV epidemic trends, describe newly developed models that combine epidemic modelling of transmission stochastic processes to more flexibly and accurately estimate and project recent trends, and discuss opportunities and challenges arising from new data sources for tracking HIV trends, in particular routinely collected health system data from HIV testing, care, and treatment.