Modelling the Effects of Population Structure on Childhood Disease: The Case of Varicella

Realistic, individual-based models based on detailed census data are increasingly used to study disease transmission. Whether the rich structure of such models improves predictions is debated. This is studied here for the spread of varicella, a childhood disease, in a realistic population of children where infection occurs in the household, at school, or in the community at large. A methodology is first presented for simulating households with births and aging. Transmission probabilities were fitted for schools and community, which reproduced the overall cumulative incidence of varicella over the age range of 0–11 years old

Real-time numerical forecast of global epidemic spreading: Case study of 2009 A/H1N1pdm

Background Mathematical and computational models for infectious diseases are increasingly used to support public-health decisions; however, their reliability is currently under debate. Real-time forecasts of epidemic spread using data-driven models have been hindered by the technical challenges posed by parameter estimation and validation. Data gathered for the 2009 H1N1 influenza crisis represent an unprecedented opportunity to validate real-time model predictions and define the main success criteria for different approaches. Methods We used the Global Epidemic and Mobility Model to generate stochastic simulations of epidemic spread worldwide, yielding (among other measures) the incidence and seeding events at a daily resolution for 3,362 subpopulations in 220 countries. Using a Monte Carlo Maximum Likelihood analysis, the model provided an estimate of the seasonal transmission potential during the early phase of the H1N1 pandemic and generated ensemble forecasts for the activity peaks in the northern hemisphere in the fall/winter wave. These results were validated against the real-life surveillance data collected in 48 countries, and their robustness assessed by focusing on 1) the peak timing of the pandemic; 2) the level of spatial resolution allowed by the model; and 3) the clinical attack rate and the effectiveness of the vaccine. In addition, we studied the effect of data incompleteness on the prediction reliability. Results Real-time predictions of the peak timing are found to be in good agreement with the empirical data, showing strong robustness to data that may not be accessible in real time (such as pre-exposure immunity and adherence to vaccination campaigns), but that affect the predictions for the attack rates. The timing and spatial unfolding of the pandemic are critically sensitive to the level of mobility data integrated into the model. Conclusions Our results show that large-scale models can be used to provide valuable real-time forecasts of influenza spreading, but they require high-performance computing. The quality of the forecast depends on the level of data integration, thus stressing the need for high-quality data in population-based models, and of progressive updates of validated available empirical knowledge to inform these models

Organic-inorganic supramolecular solid catalyst boosts organic reactions in water

[EN] Coordination polymers and metal-organic frameworks are appealing as synthetic hosts for mediating chemical reactions. Here we report the preparation of a mesoscopic metal-organic structure based on single-layer assembly of aluminium chains and organic alkylaryl spacers. The material markedly accelerates condensation reactions in water in the absence of acid or base catalyst, as well as organocatalytic Michael-type reactions that also show superior enantioselectivity when comparing with the host-free transformation. The mesoscopic phase of the solid allows for easy diffusion of products and the catalytic solid is recycled and reused. Saturation transfer difference and two-dimensional H-1 nuclear Overhauser effect NOESY NMR spectroscopy show that non-covalent interactions are operative in these host-guest systems that account for substrate activation. 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Perivascular macrophages in health and disease

Macrophages are a heterogeneous group of cells that are capable of carrying out distinct functions in different tissues, as well as in different locations within a given tissue. Some of these tissue macrophages lie on, or close to, the outer (abluminal) surface of blood vessels and perform several crucial activities at this interface between the tissue and the blood. In steady-state tissues, these perivascular macrophages maintain tight junctions between endothelial cells and limit vessel permeability, phagocytose potential pathogens before they enter tissues from the blood and restrict inappropriate inflammation. They also have a multifaceted role in diseases such as cancer, Alzheimer disease, multiple sclerosis and type 1 diabetes. Here, we examine the important functions of perivascular macrophages in various adult tissues and describe how these functions are perturbed in a broad array of pathological conditions

Nonlinear constrained principal component analysis in the quality control framework

Many problems in industrial quality control involve n measurements on\ud p process variables Xn;p. Generally, we need to know how the quality characteristics of a product behavior as process variables change. Nevertheless, there may be two problems: the linear hypothesis is not always respected and q quality variables Yn;q are not measured frequently because of high costs. B-spline transformation remove nonlinear hypothesis while principal component analysis with linear con-\ud straints (CPCA) onto subspace spanned by column X matrix. Linking Yn;q and\ud Xn;p variables gives us information on the Yn;q without expensive measurements and off-line analysis. Finally, there are few uncorrelated latent variables which contain the information about the Yn;q and may be monitored by multivariate control\ud charts. The purpose of this paper is to show how the conjoint employment of different statistical methods, such as B-splines, Constrained PCA and multivariate control charts allow a better control on product or service quality by monitoring directly\ud the process variables. The proposed approach is illustrated by the discussion of a real problem in an industrial process

Difference-based ridge estimator of parameters in partial linear model

Differencing estimator, Differencing matrix, Multicollinearity, Ridge regression estimator, 62G08, 62J07,