Evaluation of Risk Management of Foodborne Infection in Hospital

As being at the head of the establishment, the hospital manager＇s major concern is food safety in his hospital. Unfortunately, because of their health weaknesses, patients are more susceptible to foodborne illness than any other population group As match as food prepared according to less stringent hygiene＇s rules can infect or intoxicate more than it would be in a healthy population, as hygiene＇s rules must be observed in a particularly strict way, at the kitchen where meals are prepared for health care facilities. In fact, hospitals＇ food hygiene＇s rules are those who defined catering. They are made to avoid food contamination and microbial growth throughout the food chain from raw materials＇ delivery to the consumer. The main objective of taking the meal as a patients＇ tracer is to detect and study the infection＇s risk associated with feeding at hospital, and to provide practical tools to better manage this risk. Methodology was based on an analysis of the last six years＇ work concerning hygiene＇s audits and bacteriological analyses. The results showed that the food infectious risk has an important frequency, on one side; on another, they were used as a standard model of such risk＇s management.

Bayesian network-based risk evaluation model for the operational requirements of the China Railway Express under the Belt and Road initiative

The operation of the China Railway Express features numerous links across several regions and countries. Effectively controlling therisks involved in the operation of the China Railway Express is crucial for ensuring safety and efficiency and promoting the sustainabledevelopment of the China Railway Express. The Bayesian network-based risk-management model was built corresponding to theactual operation of the China Railway Express;the model used an advanced risk-management theory. The sensitivity analysis of riskfactors and Bayesian network inference were realized using the expectation-maximization and clique-tree propagation algorithms.Using the risk-checklistmethod, 17 risk-related factors were analysed on 17 nodes of the Bayesian network fromthree perspectives—safety, efficiency and effectiveness—based on expert opinions and the actual operating conditions of the China Railway Express.Data from a sensitivity analysis and evidence inference of the Bayesian network model indicated that the sensitivity coefficients ofnodes N01, N04, N07, N08 and N17 of the network were high. Moreover, the risk-occurrence probabilities for nodes N01, N04, N06,N07 and N09 were higher in the case of reverse inference. Our results revealed the crucial factors influencing the risk. The identifiedrisk factors included the stability of the political environment in countries along the route, conditions of station infrastructures andthe complexity of the process of changing rails and reloading. Further, risk-management suggestions were provided. By establishinga sound risk-management framework, reliable assessment and management could be realized in accordance with changes in theoperating conditions of the China Railway Express.