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A Bayesian synthesis of evidence for estimating HIV prevalence and incidence

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Disease incidence and prevalence are not always directly measurable, and increasingly are being estimated by synthesising diverse sources of evidence in a full probability model, typically in a Bayesian framework. In this talk I will describe such a model for estimating HIV incidence among men who have sex with men in England and Wales. We start with a two-stage process: first, estimating prevalence from surveillance and other ad-hoc survey data; then estimating incidence from the posterior prevalence estimates together with further data on diagnosis rates, demographics and risk behaviour change. This model is then expanded to a dynamic transmission model, and finally prevalence and incidence are simultaneously estimated in a single large evidence synthesis.

This talk is part of the Worms and Bugs series.

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