Simultaneously characterizing the comparative economics of routine female adolescent nonavalent human papillomavirus (HPV) vaccination and assortativity of sexual mixing in Hong Kong Chinese: a modeling analysis.

BACKGROUND: Although routine vaccination of females before sexual debut against human papillomavirus (HPV) has been found to be cost-effective around the world, its cost-benefit has rarely been examined. We evaluate both the cost-effectiveness and cost-benefit of routine female adolescent nonavalent HPV vaccination in Hong Kong to guide its policy, and by extension that of mainland China, on HPV vaccination. One major obstacle is the lack of data on assortativity of sexual mixing. Such difficulty could be overcome by inferring sexual mixing parameters from HPV epidemiologic data. METHODS: We use an age-structured transmission model coupled with stochastic individual-based simulations to estimate the health and economic impact of routine nonavalent HPV vaccination for girls at age 12 on cervical cancer burden and consider vaccine uptake at 25%, 50%, and 75% with at least 20 years of vaccine protection. Bayesian inference was employed to parameterize the model using local data on HPV prevalence and cervical cancer incidence. We use the human capital approach in the cost-benefit analysis (CBA) and GDP per capita as the indicative willingness-to-pay threshold in the cost-effectiveness analysis (CEA). Finally, we estimate the threshold vaccine cost (TVC), which is the maximum cost for fully vaccinating one girl at which routine female adolescent nonavalent HPV vaccination is cost-beneficial or cost-effective. RESULTS: As vaccine uptake increased, TVC decreased (i.e., economically more stringent) in the CBA but increased in the CEA. When vaccine uptake was 75% and the vaccine provided only 20 years of protection, the TVC was US$444 ($373-506) and $689 ($646-734) in the CBA and CEA, respectively, increasing by approximately 2-4% if vaccine protection was assumed lifelong. TVC is likely to be far higher when non-cervical diseases are included. The inferred sexual mixing parameters suggest that sexual mixing in Hong Kong is highly assortative by both age and sexual activity level. CONCLUSIONS: Routine HPV vaccination of 12-year-old females is highly likely to be cost-beneficial and cost-effective in Hong Kong. Inference of sexual mixing parameters from epidemiologic data of prevalent sexually transmitted diseases (i.e., HPV, chlamydia, etc.) is a potentially fruitful but largely untapped methodology for understanding sexual behaviors in the population

Normalization of contrast in document images using generalized fuzzy operator with least square method

The visual effect of non-uniform contrast and brightness surrounds in the image is a very common problem in the applications of photocopying, IC manufacture and medicine. In using the digital/CCD camera to capture documents and photos based on non-uniform illumination condition, the poor image will be seen. The poor image can result in achieving the inaccurate reading from the optical character recognition (OCR) system. This paper present a new approach to normalize the local contrast in documentation based on the least square method and also enhance the object of interest using generalized fuzzy operator (GFO). Two typical examples are used for evaluating the method. © 2002 IEEE.published_or_final_versio

Principle Components for Diagnosing Dispersion in Multivariate Statistical Process Control

We provide an easily implemented procedure to help data analysts systematically diagnose which quality characteristics may be driving the dispersion of a multivariate process out of control. Multivariate statistical process control commonly uses Hotelling\u27s T2 statistic to indicate when a multivariate observation goes out-of-control. Several techniques currently exist that accurately diagnose which specific variables are driving the T2 statistic out-of-control. For subgroups of independently and identically distributed multivariate normal observations, we advocate decomposing the overall T2 into independent T2 statistics for separate monitoring of location and dispersion. We propose a procedure based on principle components to diagnose the specific variables responsible for driving subgroup dispersion out-of-control. The procedure is demonstrated on a publicly available data-set

IDETC2006-99599 SOME METRICS AND A BAYESIAN PROCEDURE FOR VALIDATING PREDICTIVE MODELS IN ENGINEERING DESIGN

ABSTRACT Even though model-based simulations are widely used in engineering design, it remains a challenge to validate models and assess the risks and uncertainties associated with the use of predictive models for design decision making. In most of the existing work, model validation is viewed as verifying the model accuracy, measured by the agreement between computational and experimental results. However, from the design perspective, a good model is considered as the one that can provide the discrimination (good resolution) between design candidates. In this work, a Bayesian approach is presented to assess the uncertainty in model prediction by combining data from both physical experiments and the computer model. Based on the uncertainty quantification of model prediction, some design-oriented model validation metrics are further developed to guide designers for achieving high confidence of using predictive models in making a specific design decision. We demonstrate that the Bayesian approach provides a flexible framework for drawing inferences for predictions in the intended but may be untested design domain, where design settings of physical experiments and the computer model may or may not overlap. The implications of the proposed validation metrics are studied, and their potential roles in a model validation procedure are highlighted. general term of design validation metric k number of design candidates space INTRODUCTION With rapid increase of computational capability, modeling and simulation based design has been increasingly used for designing new engineering systems. However, it remains a challenge on assessing the risks and uncertainties associated with the use of predictive models in engineering design. Eve