No abstract

This study determined the influence of various meteorological variables and air pollutants on airway disorders in general, and asthma and/or chronic obstructive pulmonary disease in particular, in Munich, Bavaria, during 2006 and 2007. This was achieved through an evaluation of the daily frequency of calls to medical and emergency call centres, ambulatory medical care visits at general practitioners, and prescriptions of antibiotics for respiratory diseases. Meteorological parameters were extracted from data supplied by the European Centre for Medium Range Weather Forecast. Data on air pollutant levels were extracted from the air quality database of the European Environmental Agency for different measurement sites. In addition to descriptive analyses, a backward elimination procedure was performed to identify variables associated with medical outcome variables. Afterwards, generalised additive models (GAM) were used to verify whether the selected variables had a linear or nonlinear impact on the medical outcomes. The analyses demonstrated associations between environmental parameters and daily frequencies of different medical outcomes, such as visits at GPs and air pressure (-27 % per 10 hPa change) or ozone (-24 % per 10 μg/m(3) change). The results of the GAM indicated that the effects of some covariates, such as carbon monoxide on consultations at GPs, or humidity on medical calls in general, were nonlinear, while the type of association varied between medical outcomes. These data suggest that the multiple, complex effect of environmental factors on medical outcomes should not be assumed homogeneous or linear a priori and that different settings might be associated with different types of associations.

Quantum computing promises to overcome computational limitations with better and faster solutions for optimization, simulation, and machine learning problems. Europe and Germany are in the process of successfully establishing research and funding programs with the objective to advance the technology’s ecosystem and industrialization, thereby ensuring digital sovereignty, security, and competitiveness. Such an ecosystem comprises hardware/software solution providers, system integrators, and users from research institutions, start-ups, and industry. The vision of the Quantum Technology and Application Consortium (QUTAC) is to establish and advance the quantum computing ecosystem, supporting the ambitious goals of the German government and various research programs. QUTAC is comprised of ten members representing different industries, in particular automotive manufacturing, chemical and pharmaceutical production, insurance, and technology. In this paper, we survey the current state of quantum computing in these sectors as well as the aerospace industry and identify the contributions of QUTAC to the ecosystem. We propose an application-centric approach for the industrialization of the technology based on proven business impact. This paper identifies 24 different use cases. By formalizing high-value use cases into well-described reference problems and benchmarks, we will guide technological progress and eventually commercialization. Our results will be beneficial to all ecosystem participants, including suppliers, system integrators, software developers, users, policymakers, funding program managers, and investors.

Predictive Modeling (PM) techniques are gaining importance in the worldwide health insurance business. Modern PM methods are used for customer relationship management, risk evaluation or medical management. This article illustrates a PM approach that enables the economic potential of (cost-) effective disease management programs (DMPs) to be fully exploited by optimized candidate selection as an example of successful data-driven business management. The approach is based on a Generalized Linear Model (GLM) that is easy to apply for health insurance companies. By means of a small portfolio from an emerging country, we show that our GLM approach is stable compared to more sophisticated regression techniques in spite of the difficult data environment. Additionally, we demonstrate for this example of a setting that our model can compete with the expensive solutions offered by professional PM vendors and outperforms non-predictive standard approaches for DMP selection commonly used in the market.