@article{Clark_27_26,
author = {Clark, Samuel J. and Thomas, Jason and Bao, Le},
title={{Estimates of Age-Specific Reductions in HIV Prevalence in Uganda: Bayesian Melding Estimation  and Probabilistic Population Forecast with an HIV-enabled Cohort Component Projection Model}},
journal = {Demographic Research},
volume = {27},
number = {26},
pages = {743--774},
doi = {10.4054/DemRes.2012.27.26},
year = {2012},
abstract = {Background: Much of our knowledge of the epidemiology and demography of HIV epidemics in Africa
is derived from models fit to sparse, non-representative data. These often average over age
and other important dimensions, rarely quantify uncertainty, and typically do not impose
consistency on the epidemiology and the demography of the population. 

Objective: This work conducts an empirical investigation of the history of the HIV epidemic in
Uganda and Tanzania through the late 1990s, focusing on sex-age-specific incidence,
uses those results to produce probabilistic forecasts of HIV prevalence ten years later,
and compares those to measures of HIV prevalence at the later time to describe the sexage
pattern of changes in prevalence over the intervening period. 

Methods: We adapt an epidemographic model of a population affected by HIV so that its parameters
can be estimated using both the Bayesian melding with IMIS estimation method
and maximum likelihood methods. Using the Bayesian version of the model we produce
probabilistic forecasts of the population with HIV. 

Results: We produce estimates of sex-age-specific HIV incidence in Uganda and Tanzania in the
late 1990s, produce probabilistic forecasts of the HIV epidemics in Uganda and Tanzania
during the early 2000s, describe the sex-age pattern of changes in HIV prevalence in
Uganda during the early 2000s, and compare the performance and results of the Bayesian
and maximum likelihood estimation procedures. 

Conclusions: We demonstrate that: (1) it is possible to model HIV epidemics in Africa taking account
of sex and age, (2) there are important advantages to the Bayesian estimation method,
including rigorous quantification of uncertainty and the ability to make probabilistic forecasts,
and (3) that there were important age-specific changes in HIV incidence in Uganda
during the early 2000s. },
URL = {https://www.demographic-research.org/volumes/vol27/26/},
eprint = {https://www.demographic-research.org/volumes/vol27/26/27-26.pdf}
}