The clinical characteristics of patients with COVID-19 were analysed to determine the factors influencing the prognosis and virus shedding time to facilitate early detection of disease progression. Logistic regression analysis was used to explore the relationships among prognosis, clinical characteristics and laboratory indexes. The predictive value of this model was assessed with receiver operating characteristic curve analysis, calibration and internal validation. The viral shedding duration was calculated using the Kaplan–Meier method, and the prognostic factors were analysed by univariate log-rank analysis and the Cox proportional hazards model. A retrospective study was carried out with patients with COVID-19 in Tianjin, China. A total of 185 patients were included, 27 (14.59%) of whom were severely ill at the time of discharge and three (1.6%) of whom died. Our findings demonstrate that patients with an advanced age, diabetes, a low PaO2/FiO2 value and delayed treatment should be carefully monitored for disease progression to reduce the incidence of severe disease. Hypoproteinaemia and the fever duration warrant special attention. Timely interventions in symptomatic patients and a time from symptom onset to treatment <4 days can shorten the duration of viral shedding.

La fiabilité des matériels mécaniques est largement conditionnée par les caractéristiques propres à chaque système, à ses conditions d'utilisation, son environnement... De plus, les mécanismes de dégradations comme la fatigue et le stress créent des phénomènes de vieillissement. Les bases de données de fiabilité, en fournissant des taux de défaillance génériques et constants, ne permettent pas de prendre en considération ces particularités. L'utilisation de telles bases entraîne par conséquent de grandes incertitudes quant aux résultats des évaluations fiabilistes. Nous proposons dans cet article une modélisation des taux de défaillance, fonction du temps, qui prend en compte les facteurs d'influence. Une application permet de valider le modèle.

Monitoring is a statutory requirement for the cultivation of genetically modified (GM) crops in the European Community. Questionnaires for farmers to report on observations of effects linked with the cultivation of GM crops can form a useful part of a monitoring regime. A questionnaire for GM maize (Zea mays L.) was designed, with questions focusing on potential effects related to the GM maize grown, as well as on background information about cultivation methods and on individual field situations. In this paper we present the methodological approach of the monitoring regime, the structuring of the data, and the contents and structure of the questionnaire. The statistical requirements and background for an appropriate evaluation and interpretation of the data are described. Results of interviews made from 2001 to 2005 are also presented. It is envisaged that this approach will be developed for monitoring other cultivated GM plants and traits, and may be applicable in monitoring certain non-farmed environments.