title: A stochastic infection rate model for estimating and projecting national HIV prevalence rates.
abstract: BACKGROUND
  Every 2 years, the Joint United Nations Programme on HIV/AIDS (UNAIDS) produces probabilistic estimates and projections of HIV prevalence rates for countries with generalised HIV/AIDS epidemics. To do this they use a simple epidemiological model and data from antenatal clinics and household surveys. The estimates are made using the Bayesian melding method, implemented by the incremental mixture importance sampling technique. This methodology is referred to as the 'estimation and projection package (EPP) model'. This has worked well for estimating and projecting prevalence in most countries. However, there has recently been an 'uptick' in prevalence in Uganda after a long sustained decline, which the EPP model does not predict.
  
  
  METHODS
  To address this problem, a modification of the EPP model, called the 'r stochastic model' is proposed, in which the infection rate is allowed to vary randomly in time and is applied to the entire non-infected population.
  
  
  RESULTS
  The resulting method yielded similar estimates of past prevalence to the EPP model for four countries and also similar median ('best') projections, but produced prediction intervals whose widths increased over time and that allowed for the possibility of an uptick after a decline. This seems more realistic given the recent Ugandan experience.
authors.affiliation: Department of Statistics, University of Washington, Seattle, WA 98195-4322, USA. lebao@uw.edu
authors.forename: Le
authors.initials: L
authors.lastname: Bao
authors.forename: Adrian E
authors.initials: AE
authors.lastname: Raftery
mesh_terms: D000349:Africa / Q000453:epidemiology; D001499:Bayes Theorem; D058872:Epidemics / Q000706:statistics & numerical data*; D015658:HIV Infections / Q000453:epidemiology*; D006801:Humans; D015233:Models, Statistical; D015995:Prevalence; D013269:Stochastic Processes