COVID-19 symptoms at hospital admission vary with age and sex: results from the ISARIC prospective multinational observational study

Background: The ISARIC prospective multinational observational study is the largest cohort of hospitalized patients with COVID-19. We present relationships of age, sex, and nationality to presenting symptoms. Methods: International, prospective observational study of 60 109 hospitalized symptomatic patients with laboratory-confirmed COVID-19 recruited from 43 countries between 30 January and 3 August 2020. Logistic regression was performed to evaluate relationships of age and sex to published COVID-19 case definitions and the most commonly reported symptoms. Results: ‘Typical’ symptoms of fever (69%), cough (68%) and shortness of breath (66%) were the most commonly reported. 92% of patients experienced at least one of these. Prevalence of typical symptoms was greatest in 30- to 60-year-olds (respectively 80, 79, 69%; at least one 95%). They were reported less frequently in children (≤ 18 years: 69, 48, 23; 85%), older adults (≥ 70 years: 61, 62, 65; 90%), and women (66, 66, 64; 90%; vs. men 71, 70, 67; 93%, each P < 0.001). The most common atypical presentations under 60 years of age were nausea and vomiting and abdominal pain, and over 60 years was confusion. Regression models showed significant differences in symptoms with sex, age and country. Interpretation: This international collaboration has allowed us to report reliable symptom data from the largest cohort of patients admitted to hospital with COVID-19. Adults over 60 and children admitted to hospital with COVID-19 are less likely to present with typical symptoms. Nausea and vomiting are common atypical presentations under 30 years. Confusion is a frequent atypical presentation of COVID-19 in adults over 60 years. Women are less likely to experience typical symptoms than men

Stabilisation d'état de charges séparées par adsorption de polyaromatiques dans des zéolithes ZSM-5

LILLE1-BU (590092102) / SudocSudocFranceF

Stabilisation d'état de charges séparées par adsorption de polyaromatiques dans des zéolithes ZSM-5

LILLE1-BU (590092102) / SudocSudocFranceF

Amélioration des performances des recyclés en domaine routier par optimisation des unités de traitement

Amélioration des performances des recyclés en domaine routier par optimisation des unités de traitemen

Assessing fire safety using complex numerical models with a Bayesian multi-fidelity approach

International audienceNowadays, fire safety engineers are increasingly relying on sophisticated numerical simulators, typically based on Computational Fluid Dynamics (CFD) solvers, to conduct their analyses. However, the complexity of these numerical models often limits drastically the number of simulations that can be afforded, making traditional methods of safety analysis difficult or impossible to apply. This paper proposes a statistical method to evaluate a quantity of interest with an expensive simulator while saving computation time. The method is based on Bayesian statistics and multi-fidelity. We use Gaussian process regression to construct a Bayesian model of the complex simulator. This model is based on a multi-fidelity approach, which consists in simulating at different levels of accuracy, for instance by varying the spatial discretization in a CFD solver. We illustrate the method on an example of fire safety analysis, where the quantity of interest is the probability of exceeding a tenability threshold in a building on fire

Sequential design of multi-fidelity computer experiments: maximizing the rate of stepwise uncertainty reduction

International audienceThis article deals with the sequential design of experiments for (deterministic or stochastic) multi-fidelity numerical simulators, that is, simulators that offer control over the accuracy of simulation of the physical phenomenon or system under study. Very often, accurate simulations correspond to high computational efforts whereas coarse simulations can be obtained at a smaller cost. In this setting, simulation results obtained at several levels of fidelity can be combined in order to estimate quantities of interest (the optimal value of the output, the probability that the output exceeds a given threshold...) in an efficient manner. To do so, we propose a new Bayesian sequential strategy called Maximal Rate of Stepwise Uncertainty Reduction (MR-SUR), that selects additional simulations to be performed by maximizing the ratio between the expected reduction of uncertainty and the cost of simulation. This generic strategy unifies several existing methods, and provides a principled approach to develop new ones. We assess its performance on several examples, including a computationally intensive problem of fire safety analysis where the quantity of interest is the probability of exceeding a tenability threshold during a building fire

Planification d’expériences numériques en multi-fidélité, appliquée à la sécurité en ingénierie incendie

International audienceLes travaux présentés portent sur l’étude de modèles numériques multifidèles, déterministes ou stochastiques. Plus précisément, les modèles considérés disposent d’un paramètre réglant la qualité de la simulation, comme une taille de maille dans un modèle par différences finies. Dans ce cas, il est possible de lancer des simulations basses-fidélités, rapides mais grossières, et des simulations hautes-fidélités, fiables mais coûteuses. L’intérêt d’une approche multi-fidèle est de combiner les résultats obtenus aux différents niveaux de fidélité afin d’économiser du temps de simulation. La méthode que nous considérons est fondée sur une approche bayésienne. Le simulateur est décrit grâce à l’un des modèles de processus gaussiens multi-niveaux développés dans la littérature que nous adaptons aux cas stochastiques. Ce méta-modèle du simulateur permet d’estimer des quantités d’intérêt ainsi que leurs incertitudes associées. L’objectif est alors de choisir de nouvelles expériences à lancer afin d’améliorer les estimations. En particulier, la planification doit sélectionner le niveau de fidélité réalisant le meilleur compromis entre coût d’observation et gain d’information. Pour cela, nous proposons une stratégie séquentielle, intitulée Maximum Rate of Uncertainty Reduction (MRUR), consistant à choisir le point d’observation maximisant le rapport entre la réduction d’incertitude et le coût. La méthodologie est illustrée en sécurité incendie, où nous estimons des probabilités de défaillance d’un système de désenfumage