Bayesian evidence synthesis for a transmission dynamic model for HIV among men who have sex with men

A. M. Presanis*, Daniela De Angelis, A. Goubar, Owen Gill, A. E. Ades

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review

26 Citations (Scopus)


Understanding infectious disease dynamics and the effect on prevalence and incidence is crucial for public health policies. Disease incidence and prevalence are typically not observed directly and increasingly are estimated through the synthesis of indirect information from multiple data sources. We demonstrate how an evidence synthesis approach to the estimation of human immunodeficiency virus (HIV) prevalence in England and Wales can be extended to infer the underlying HIV incidence. Diverse time series of data can be used to obtain yearly "snapshots" (with associated uncertainty) of the proportion of the population in 4 compartments: not at risk, susceptible, HIV positive but undiagnosed, and diagnosed HIV positive. A multistate model for the infection and diagnosis processes is then formulated by expressing the changes in these proportions by a system of differential equations. By parameterizing incidence in terms of prevalence and contact rates, HIV transmission is further modeled. Use of additional data or prior information on demographics, risk behavior change and contact parameters allows simultaneous estimation of the transition rates, compartment prevalences, contact rates, and transmission probabilities. 2011 The Author(s)2011This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (, which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Original languageEnglish
Pages (from-to)666-681
Number of pages16
Issue number4
Publication statusPublished - Oct 2011


  • Bayesian
  • Dynamic transmission model
  • Evidence synthesis
  • HIV
  • Incidence
  • Prevalence


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