Predicting the effectiveness of prevention: a role for epidemiological modeling

It is well known that the current combination of aging populations and advances in health technology is resulting in burgeoning health costs in developed countries. Prevention is a potentially important way of containing health costs. In an environment of intense cost pressures, coupled with developments in disease prevention and health promotion, it is increasingly important for decision-makers to have a systematic, coordinated approach to the targeting and prioritization of preventive strategies. However, such a systematic approach is made difficult by the fact that preventive strategies need to be compared over the long term, in a variety of populations, and in real life settings not found in most trials. Information from epidemiological models can provide the required evidence base. In this review, we outline the role of epidemiological modeling in this context and detail its application using examples. Editors' Strategic Implications: Policymakers and researchers will benefit from this description of the utility of epidemiological modeling as a means of generating translational evidence that helps to prioritize data-based prevention approaches and bridge the gap between clinical research and public health practice.

An alternative data collection scheduling scheme in wireless sensor networks

Despite significant advancements in wireless sensor networks (WSNs), energy conservation remains one of the most important research challenges. Recently, the problem of energy conservation has been addressed by applying mobile sink as an effective technique that can enhance efficiency of energy consumption in the networks. In this paper, the energy conservation problem is firstly formulated to maximize the lifetime of WSN subject to delay and node energy constraints. Then, to solve the defined energy conservation problem, a data collection scheduling with a mobile sink scheme is proposed. In the proposed approach, the sink movement is governed by a type-2 fuzzy controller to be located at the best location and time to collect sensory data. We conducted extensive experiments to study the effectiveness of the proposed protocol and compared it against the streaming data delivery (SDD) and virtual circle combined straight routing (VCCS) protocols. We observed that the proposed protocol outperforms both SDD and VCCS approaches by reducing energy consumption, minimize delays and enhance data collection quality.

Vector autoregressions and macroeconomic modeling: an error taxonomy

In this article we investigate the theoretical behaviour of finite lag VAR(n) models fitted to time series that in truth come from an infinite order VAR(∞) data generating mechanism. We show that the overall error can be broken down into two basic components, an estimation error that stems from the difference between the parameter estimates and their population ensemble VAR(n) counterparts, and an approximation error that stems from the difference between the VAR(n) and the true VAR(∞). The two sources of error are shown to be present in other performance indicators previously employed in the literature to characterize, so called, truncation effects. Our theoretical analysis indicates that the magnitude of the estimation error exceeds that of the approximation error, but experimental results based upon a prototypical real business cycle model and a practical example indicate that the approximation error approaches its asymptotic position far more slowly than does the estimation error, their relative orders of magnitude notwithstanding. The experimental results suggest that with sample sizes and lag lengths like those commonly employed in practice VAR(n) models are likely to exhibit serious errors of both types when attempting to replicate the dynamics of the true underlying process and that inferences based on VAR(n) models can be very untrustworthy.