The Marshall-Olkin (MO) copula model has emerged as the standard tool for capturing dependency between components in failure analysis in reliability. In this model shocks arise at exponential random times, that affect one or several components inducing a natural correlation in the failure process. However, because the number of parameter of the model grows exponentially with the number of components, MO suffers of the "curse of dimensionality". MO models are usually intended to be applied to design a network before its construction, therefore it is natural to assume that only partial information about failure behavior can be gathered, mostly from similar existing networks. To construct such a MO model, we propose an optimization approach to define the shock's parameters in the MO copula, in order to match marginal failures probabilities and correlations between these failures. To deal with the exponential number of parameters of this problem, we use a column-generation technique. We also discuss additional criteria that can be incorporated to obtain a suitable model. Our computational experiments show that the resulting MO model produces a close estimation of the network reliability, especially when the correlation between component failures is significant. Index TermsReliability, copula theory, optimization methods, failure analysis, network design.O. Matus, J. Barrera and E. Moreno are with the Faculty of Engineering and Sciences, Universidad Adolfo Ibáñez, Santiago, Chile. G. Rubino is with INRIA Rennes -Bretagne Atlantique, Rennes, France.

We identified factors associated with gestational weight gain (GWG) in 1,654 Chilean pregnant women with full-term pregnancies. At baseline, we collected information about sociodemographic, gyneco-obstetric, anthropometric, and health-care-related factors. We found that prepregnancy nutritional body mass index was the most important factor related to GWG above recommendations (overweight: ratio of relative risks [RRR] = 2.31, 95% confidence interval [CI, 1.73, 3.09] and obesity: RRR = 2.90, 95% CI [2.08, 4.03]). We believe that women who are overweight/obese at the beginning of pregnancy should be identified because of their higher risk, and that adequate strategies should be designed and implemented to help them achieve a healthy GWG.

No abstract

In recent years, highly effective treatments for hepatitis C virus (HCV) have become available. However, high prices of new treatments call for a careful policy evaluation when considering economic constraints. Although the current medical advice is to administer the new therapies to all patients, economic and capacity constraints require an efficient allocation of these scarce resources. We use stochastic dynamic programming to determine the optimal policy for prescribing the new treatment based on the age and disease progression of the patient. We show that, in a simplified version of the model, new drugs should be administered to patients at a given level of fibrosis if they are within prespecified age limits; otherwise, a conservative approach of closely monitoring the evolution of the patient should be followed. We use a cohort of Spanish patients to study the optimal policy regarding costs and health indicators. For this purpose, we compare the performance of the optimal policy against a liberal policy of treating all sick patients. In this analysis, we achieve similar results in terms of the number of transplants, HCV-related deaths, and quality of adjusted life years, with a significant reduction in overall expenditure. Furthermore, the budget required during the first year of implementation when using the proposed methodology is only 12% of that when administering the treatment to all patients at once. Finally, we propose a method to prioritize patients when there is a shortage (surplus) in the annual budget constraint and, therefore, some recommended treatments must be postponed (added).