amei: An R Package for the Adaptive Management of Epidemiological Interventions

The amei package for R is a tool that provides a flexible statistical framework for generating optimal epidemiological interventions that are designed to minimize the total expected cost of an emerging epidemic. Uncertainty regarding the underlying disease parameters is propagated through to the decision process via Bayesian posterior inference. The strategies produced through this framework are adaptive: vaccination schedules are iteratively adjusted to reflect the anticipated trajectory of the epidemic given the current population state and updated parameter estimates. This document briefly covers the background and methodology underpinning the implementation provided by the package and contains extensive examples showing the functions and methods in action.

Mental Health Services: An International Perspective

This paper provides an introduction to this special edition on international mental health perspectives. The importance of an international perspective is discussed and key questions are raised to provide the reader with a frame of reference for examining the mental health systems in the countries presented. An orientation to some of the current mental health issues in Europe, the United States, and developing countries is given as point of comparison for the reader. Questions discussed relate to the status of institutional care, outpatient services, the composition of mental health staff, the role of community interventions and prevention, and the availability and accessibility of mental health services

Validating soil denitrification models based on laboratory N2 and N2O fluxes and underlying processes derived by stable isotope approaches: concept, methods and regulation of measured fluxes

Robust denitrification data suitable to validate soil N2 fluxes in denitrification models are scarce due to methodical limitations and the extreme spatio-temporal heterogeneity of denitrification in soils. Numerical models have become essential tools to predict denitrification at different scales. Model performance could either be tested for total gaseous flux (NO + N2O + N2), individual denitrification products (e.g. N2O and/or NO) or for the effect of denitrification factors (e.g. C-availability, respiration, diffusivity, anaerobic volume, etc.). While there are numerous examples for validating N2O fluxes, there are neither robust field data of N2 fluxes nor sufficiently resolved measurements of control factors used as state variables in the models. Here we present the concept, methods and first results of collecting model validation data. This is part of the coordinated research unit “Denitrification in Agricultural Soils: Integrated Control and Modelling at Various Scales” (DASIM). Novel approaches are used including analysis of stable isotopes, microbial communities, pore structure and organic matter fractions to provide denitrification data sets comprising as much detail on activity and regulation as possible. This will be the basis to validate existing and calibrate new denitrification models that are applied and/or developed by DASIM subprojects. To allow model testing in a wide range of conditions, denitrification control factors are varied in the initial settings (pore volume, plant residues, mineral N, pH) but also over time, where moisture, temperature, and mineral N are manipulated according to typical time patterns in the field. This is realized by including precipitation events, fertilization (via irrigation), drainage (via water potential) and temperature in the course of incubations. Moreover, oxygen concentration is varied to simulate anaerobic events. The 15N gas flux method is employed to quantify N2 and N2O emissions from various pools and processes

Compatibility studies of several molten uranium and thorium alloys in niobium, tantalum, and yttrium

Niobium, tantalum, yttrium, and Inconel have been used to contain molten aluminum, lead, tin, zinc, and several of their respective uranium and thorium alloys for various times up to 3000 hours and at temperatures ranging from 600 to 1100° C. Altogether 76 capsule tests were run, almost all in a static isothermal condition. Tantalum showed the best resistance followed by niobium, Inconel, and yttrium respectively. The systems, lead in tantalum and lead in niobium, showed the greatest potentials for possible liquid-metal fuel carrier systems. An alloy of uranium-bismuth-tin contained in tantalum also exhibited promising possibilities . The tabulated test data include a classification of the type of corrosion attack which occurred and a measured value of the amount of corrosive penetration. Each test was also given an arbitrary rating for easy reference comparisons. A number of photomicrographs are included for each set of tests

amei: An R Package for the Adaptive Management of Epidemiological Interventions

The <b>amei</b> package for <b>R</b> is a tool that provides a flexible statistical framework for generating optimal epidemiological interventions that are designed to minimize the total expected cost of an emerging epidemic. Uncertainty regarding the underlying disease parameters is propagated through to the decision process via Bayesian posterior inference. The strategies produced through this framework are adaptive: vaccination schedules are iteratively adjusted to reflect the anticipated trajectory of the epidemic given the current population state and updated parameter estimates. This document briefly covers the background and methodology underpinning the implementation provided by the package and contains extensive examples showing the functions and methods in action

A Statistical Framework for the Adaptive Management of Epidemiological Interventions

Background: Epidemiological interventions aim to control the spread of infectious disease through various mechanisms, each carrying a different associated cost. Methodology: We describe a flexible statistical framework for generating optimal epidemiological interventions that are designed to minimize the total expected cost of an emerging epidemic while simultaneously propagating uncertainty regarding the underlying disease model parameters through to the decision process. The strategies produced through this framework are adaptive: vaccination schedules are iteratively adjusted to reflect the anticipated trajectory of the epidemic given the current population state and updated parameter estimates. Conclusions: Using simulation studies based on a classic influenza outbreak, we demonstrate the advantages of adaptiv