TY - JOUR
T1 - A stochastic infection rate model for estimating and projecting national HIV prevalence rates.
AU - Bao, L.
AU - Raftery, Adrian E.
PY - 2010/12
Y1 - 2010/12
N2 - 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. 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. 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.
AB - 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. 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. 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.
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U2 - 10.1136/sti.2010.044529
DO - 10.1136/sti.2010.044529
M3 - Article
C2 - 21106521
AN - SCOPUS:79953220184
SN - 1368-4973
VL - 86 Suppl 2
SP - ii93-99
JO - Sexually transmitted infections
JF - Sexually transmitted infections
ER -