Objectives: To propose a Bayesian approach to uncertainty analysis of sexually transmitted infection (STI) models, that can be used to quantify uncertainty in model assessments of policy options, estimate regional STI prevalence from sentinel surveillance data and make inferences about STI transmission and natural history parameters.
Methods: Prior distributions are specified to represent uncertainty regarding STI parameters. A likelihood function is defined using a hierarchical approach that takes account of variation between study populations, variation in diagnostic accuracy as well as random binomial variation. The method is illustrated using a model of syphilis, gonorrhoea, chlamydial infection and trichomoniasis in South Africa.
Results: Model estimates of STI prevalence are in good agreement with observations. Out-of-sample projections and cross-validations also show that the model is reasonably well calibrated. Model predictions of the impact of interventions are subject to significant uncertainty: the predicted reductions in the prevalence of syphilis by 2020, as a result of doubling the rate of health seeking, increasing the proportion of private practitioners using syndromic management protocols, and screening all pregnant women for syphilis, are 43% (95% CI: 3-77%), 9% (95% CI: 1-19%) and 6% (95% CI: 4-7%) respectively.
Conclusions: This study extends uncertainty analysis techniques for fitted HIV/AIDS models to models that are fitted to other STI prevalence data. There is significant uncertainty regarding the relative effectiveness of different STI control strategies. The proposed technique is reasonable for estimating uncertainty in past STI prevalence levels and for projections of future STI prevalence.
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