title: Improvements in prevalence trend fitting and incidence estimation in EPP 2013.
abstract: OBJECTIVE
  Describe modifications to the latest version of the Joint United Nations Programme on AIDS (UNAIDS) Estimation and Projection Package component of Spectrum (EPP 2013) to improve prevalence fitting and incidence trend estimation in national epidemics and global estimates of HIV burden.
  
  
  METHODS
  Key changes made under the guidance of the UNAIDS Reference Group on Estimates, Modelling and Projections include: availability of a range of incidence calculation models and guidance for selecting a model; a shift to reporting the Bayesian median instead of the maximum likelihood estimate; procedures for comparison and validation against reported HIV and AIDS data; incorporation of national surveys as an integral part of the fitting and calibration procedure, allowing survey trends to inform the fit; improved antenatal clinic calibration procedures in countries without surveys; adjustment of national antiretroviral therapy reports used in the fitting to include only those aged 15-49 years; better estimates of mortality among people who inject drugs; and enhancements to speed fitting.
  
  
  RESULTS
  The revised models in EPP 2013 allow closer fits to observed prevalence trend data and reflect improving understanding of HIV epidemics and associated data.
  
  
  CONCLUSIONS
  Spectrum and EPP continue to adapt to make better use of the existing data sources, incorporate new sources of information in their fitting and validation procedures, and correct for quantifiable biases in inputs as they are identified and understood. These adaptations provide countries with better calibrated estimates of incidence and prevalence, which increase epidemic understanding and provide a solid base for program and policy planning.
authors.affiliation: aResearch Program, East-West Center, Honolulu, Hawaii bDepartment of Statistics, Pennsylvania State University, University Park, Pennsylvania, USA cDepartment of Infectious Disease Epidemiology, Imperial College London, London, United Kingdom dDepartment of Health Statistics and Information Systems, World Health Organization eUNAIDS, Geneva, Switzerland fThe Kirby Institute, University of New South Wales, Sydney, New South Wales, Australia.
authors.forename: Tim
authors.initials: T
authors.lastname: Brown
authors.forename: Le
authors.initials: L
authors.lastname: Bao
authors.forename: Jeffrey W
authors.initials: JW
authors.lastname: Eaton
authors.forename: Daniel R
authors.initials: DR
authors.lastname: Hogan
authors.forename: Mary
authors.initials: M
authors.lastname: Mahy
authors.forename: Kimberly
authors.initials: K
authors.lastname: Marsh
authors.forename: Bradley M
authors.initials: BM
authors.lastname: Mathers
authors.forename: Robert
authors.initials: R
authors.lastname: Puckett
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D058872:Epidemics; D004812:Epidemiologic Methods; D014943:Global Health; D015658:HIV Infections / Q000453:epidemiology*; D006801:Humans; D015994:Incidence; D008875:Middle Aged; D015995:Prevalence; D014480:United Nations; D055815:Young Adult