308 research outputs found
Enhancing Bayesian risk prediction for epidemics using contact tracing
Contact tracing data collected from disease outbreaks has received relatively
little attention in the epidemic modelling literature because it is thought to
be unreliable: infection sources might be wrongly attributed, or data might be
missing due to resource contraints in the questionnaire exercise. Nevertheless,
these data might provide a rich source of information on disease transmission
rate. This paper presents novel methodology for combining contact tracing data
with rate-based contact network data to improve posterior precision, and
therefore predictive accuracy. We present an advancement in Bayesian inference
for epidemics that assimilates these data, and is robust to partial contact
tracing. Using a simulation study based on the British poultry industry, we
show how the presence of contact tracing data improves posterior predictive
accuracy, and can directly inform a more effective control strategy.Comment: 40 pages, 9 figures. Submitted to Biostatistic
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Value of Information: Sensitivity Analysis and Research Design in Bayesian Evidence Synthesis.
Suppose we have a Bayesian model that combines evidence from several different sources. We want to know which model parameters most affect the estimate or decision from the model, or which of the parameter uncertainties drive the decision uncertainty. Furthermore, we want to prioritize what further data should be collected. These questions can be addressed by Value of Information (VoI) analysis, in which we estimate expected reductions in loss from learning specific parameters or collecting data of a given design. We describe the theory and practice of VoI for Bayesian evidence synthesis, using and extending ideas from health economics, computer modeling and Bayesian design. The methods are general to a range of decision problems including point estimation and choices between discrete actions. We apply them to a model for estimating prevalence of HIV infection, combining indirect information from surveys, registers, and expert beliefs. This analysis shows which parameters contribute most of the uncertainty about each prevalence estimate, and the expected improvements in precision from specific amounts of additional data. These benefits can be traded with the costs of sampling to determine an optimal sample size. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement
Estimation of HIV burden through Bayesian evidence synthesis
Planning, implementation and evaluation of public health policies to control
the human immunodeficiency virus (HIV) epidemic require regular monitoring of
disease burden. This includes the proportion living with HIV, whether diagnosed
or not, and the rate of new infections in the general population and in
specific risk groups and regions. Estimation of these quantities is not
straightforward: data informing them directly are not typically available, but
a wealth of indirect information from surveillance systems and ad hoc studies
can inform functions of these quantities. In this paper we show how the
estimation problem can be successfully solved through a Bayesian evidence
synthesis approach, relaxing the focus on "best available" data to which
classical methods are typically restricted. This more comprehensive and
flexible use of evidence has led to the adoption of our proposed approach as
the official method to estimate HIV prevalence in the United Kingdom since
2005
Modeling of the HIV infection epidemic in the Netherlands: A multi-parameter evidence synthesis approach
Multi-parameter evidence synthesis (MPES) is receiving growing attention from
the epidemiological community as a coherent and flexible analytical framework
to accommodate a disparate body of evidence available to inform disease
incidence and prevalence estimation. MPES is the statistical methodology
adopted by the Health Protection Agency in the UK for its annual national
assessment of the HIV epidemic, and is acknowledged by the World Health
Organization and UNAIDS as a valuable technique for the estimation of adult HIV
prevalence from surveillance data. This paper describes the results of
utilizing a Bayesian MPES approach to model HIV prevalence in the Netherlands
at the end of 2007, using an array of field data from different study designs
on various population risk subgroups and with a varying degree of regional
coverage. Auxiliary data and expert opinion were additionally incorporated to
resolve issues arising from biased, insufficient or inconsistent evidence. This
case study offers a demonstration of the ability of MPES to naturally integrate
and critically reconcile disparate and heterogeneous sources of evidence, while
producing reliable estimates of HIV prevalence used to support public health
decision-making.Comment: Published in at http://dx.doi.org/10.1214/11-AOAS488 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Bayesian evidence synthesis to estimate HIV prevalence in men who have sex with men in Poland at the end of 2009.
HIV spread in men who have sex with men (MSM) is an increasing problem in Poland. Despite the existence of a surveillance system, there is no direct evidence to allow estimation of HIV prevalence and the proportion undiagnosed in MSM. We extracted data on HIV and the MSM population in Poland, including case-based surveillance data, diagnostic testing prevalence data and behavioural data relating to self-reported prior diagnosis, stratified by age (⩽35, >35 years) and region (Mazowieckie including the capital city of Warsaw; other regions). They were integrated into one model based on a Bayesian evidence synthesis approach. The posterior distributions for HIV prevalence and the undiagnosed fraction were estimated by Markov Chain Monte Carlo methods. To improve the model fit we repeated the analysis, introducing bias parameters to account for potential lack of representativeness in data. By placing additional constraints on bias parameters we obtained precisely identified estimates. This family of models indicates a high undiagnosed fraction [68·3%, 95% credibility interval (CrI) 53·9-76·1] and overall low prevalence (2·3%, 95% CrI 1·4-4·1) of HIV in MSM. Additional data are necessary in order to produce more robust epidemiological estimates. More effort is urgently needed to ensure timely diagnosis of HIV in Poland
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