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10.1016/j.jlp.2020.104310 http://scihub22266oqcxt.onion/10.1016/j.jlp.2020.104310 33013002!7525359!33013002     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=33013002&cmd=llinks): Failed to open stream: HTTP request failed! 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How can process safety and a risk management approach guide pandemic risk management?|pdf=|usr=}}{{33013002}} gab.com Text How can process safety and a risk management approach guide pandemic risk management JO: J Loss Prev Process Ind http://www.kidney.de/pget.php?&tw=1&pmid=33013002 #FOAvip The coronavirus disease (COVID-19) brought the world to a halt in March 2020. Various prediction and risk management approaches are being explored worldwide for decision making. This work adopts an advanced mechanistic model and utilizes tools for process safety to propose a framework for risk management for the current pandemic. A parameter tweaking and an artificial neural network-based parameter learning model have been developed for effective forecasting of the dynamic risk. Monte Carlo simulation was used to capture the randomness of the model parameters. A comparative analysis of the proposed methodologies has been carried out by using the susceptible, exposed, infected, quarantined, recovered, deceased (SEIQRD) model. A SEIQRD model was developed for four distinct locations: Italy, Germany, Ontario, and British Columbia. The learning-based approach resulted in better outcomes among the models tested in the present study. The layer of protection analysis is a useful framework to analyze the effect of different safety measures. This framework is used in this work to study the effect of non-pharmaceutical interventions on pandemic risk. The risk profiles suggest that a stage-wise releasing scenario is the most suitable approach with negligible resurgence. The case study provides valuable insights to practitioners in both the health sector and the process industries to implement advanced strategies for risk assessment and management. Both sectors can benefit from each other by using the mathematical models and the management tools used in each, and, more importantly, the lessons learned from crises. Twit Text FOAVip How can process safety and a risk management approach guide pandemic risk management ????J Loss Prev Process Ind http://www.kidney.de/pget.php?&tw=1&pmid=33013002 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33013002 #FOAvip English Wikipedia <ref>{{Cite pmid|33013002}}</ref> How can process safety and a risk management approach guide pandemic risk management? #MMPMID33013002 Alauddin M; Islam Khan MA; Khan F; Imtiaz S; Ahmed S; Amyotte P J Loss Prev Process Ind 2020[Nov]; 68 (ä): 104310 PMID33013002show ga The coronavirus disease (COVID-19) brought the world to a halt in March 2020. Various prediction and risk management approaches are being explored worldwide for decision making. This work adopts an advanced mechanistic model and utilizes tools for process safety to propose a framework for risk management for the current pandemic. A parameter tweaking and an artificial neural network-based parameter learning model have been developed for effective forecasting of the dynamic risk. Monte Carlo simulation was used to capture the randomness of the model parameters. A comparative analysis of the proposed methodologies has been carried out by using the susceptible, exposed, infected, quarantined, recovered, deceased (SEIQRD) model. A SEIQRD model was developed for four distinct locations: Italy, Germany, Ontario, and British Columbia. The learning-based approach resulted in better outcomes among the models tested in the present study. The layer of protection analysis is a useful framework to analyze the effect of different safety measures. This framework is used in this work to study the effect of non-pharmaceutical interventions on pandemic risk. The risk profiles suggest that a stage-wise releasing scenario is the most suitable approach with negligible resurgence. The case study provides valuable insights to practitioners in both the health sector and the process industries to implement advanced strategies for risk assessment and management. Both sectors can benefit from each other by using the mathematical models and the management tools used in each, and, more importantly, the lessons learned from crises. äHow can process safety and a risk management approach guide pandemic r(epmc).pdf DeepDyve Pubget Overpricing