PSAM 16 - Session F03 Overview

PSAM 16 Conference Session F03 Overview

Session Chair: Garill Coles (garill.coles@pnnl.gov)

Paper 1 BR91

Lead Author: Stefan Bracke Co-author(s): Alicia Puls; apuls@uni-wuppertal.de

COVID-19 pandemic: Analyzing spreading behavior of different infection waves within the first two years in Germany by the use of reliability methods
Since December 2019, the world is confronted with the COVID-19 pandemic, caused by the Coronavirus SARS-CoV-2. The COVID-19 pandemic with its incredible speed of spread shows the vulnerability of a globalized and networked world. The first two years of the pandemic were characterized by several infections waves, marked by different spreading behaviors, described by length, peak and speed. The infection waves caused a heavy burden on health systems and severe restrictions on public life within a lot of countries, like educational system shutdown, travel restrictions, limitations regarding public life or a comprehensive lockdown. The goal of the presented research study is the analysis of the development of the four dominant infection waves in Germany within the first two years of COVID-19 pandemic time period (February 2020 – February 2022). The analyses are focusing on infection occurrence and spreading behavior, in detail on attributes like length, peak and speed of each wave. Furthermore various impacts of lockdown strategies (hard, soft) or health protection measures, vaccination status and virus mutations are considered. The analyze of the infection waves is based on a transfer and application of methods – especially the Weibull distribution model and statistical hypothesis tests – used in reliability engineering for analyzing the upcoming failure development within product fleets in the field. This study continues previous research; cf. Puls and Bracke (2021), Bracke et al. (2021) and Puls and Bracke. The spreading behavior of a COVID-19 infection wave can be described by Weibull distribution model in a sound way, related to a short time interval. The interpretation of the Weibull model parameters allows the assessment of the COVID-19 infection wave characteristics and generates additional information to classical infection analyzing models like the SIR model (Kermack and Kendrick 1927). Furthermore, statistical hypothesis tests are used to analyze the observed characteristics of the infection waves with regard to their significance. Observations like change points, trends, peaks and further systematically developments within the infection situation and the resulting potential risk can be substantiated. Finally, the characteristics of the COVID-19 infection waves are analyzed in the context of other common infectious diseases in Germany like Influenza or Norovirus. Differences in the spreading behavior of COVID-19 in comparison to these well-known infectious diseases are underlined for different pandemic phases. REFERENCES A. Puls and S. Bracke (2021). Reliability Methods for analyzing COVID-10 pandemic spreading behavior, lockdown impact and infectiousness. In: Proceedings of the 31th European Safety and Reliability Conference (Eds.: Bruno Castanier, Marko Cepin, David Bigaud and Christophe Berenguer). Research Publishing Services, Singapore. S. Bracke, A. Puls and M. Inoue (2021). COVID-19 pandemic: Analyzing of restrictions, medical care and prevention measures in Germany and Japan. In: Proceedings of the 31th European Safety and Reliability Conference (Eds.: Bruno Castanier, Marko Cepin, David Bigaud and Christophe Berenguer), Research Publishing Services, Singapore. A. Puls and S. Bracke (2021). COVID-19 pandemic risk analytics: Data mining with reliability engineering methods for analyzing spreading behavior and comparison with infectious diseases. In: van Gulijk C., Zaitseva E. (eds) Reliability Engineering and Computational Intelligence. Studies in Computational Intelligence, vol 976. Springer. Kermack, W.O., A.G. McKendrick (1927), A Contribution to the Mathematical Theory of Epidemics. Proc. Roy. Soc. A, Vol. 115, pp. 700-721.

COVID-19 pandemic: Analyzing spreading behavior of different infection waves within the first two years in Germany by the use of reliability methods

Since December 2019, the world is confronted with the COVID-19 pandemic, caused by the Coronavirus SARS-CoV-2. The COVID-19 pandemic with its incredible speed of spread shows the vulnerability of a globalized and networked world. The first two years of the pandemic were characterized by several infections waves, marked by different spreading behaviors, described by length, peak and speed. The infection waves caused a heavy burden on health systems and severe restrictions on public life within a lot of countries, like educational system shutdown, travel restrictions, limitations regarding public life or a comprehensive lockdown. The goal of the presented research study is the analysis of the development of the four dominant infection waves in Germany within the first two years of COVID-19 pandemic time period (February 2020 – February 2022). The analyses are focusing on infection occurrence and spreading behavior, in detail on attributes like length, peak and speed of each wave. Furthermore various impacts of lockdown strategies (hard, soft) or health protection measures, vaccination status and virus mutations are considered. The analyze of the infection waves is based on a transfer and application of methods – especially the Weibull distribution model and statistical hypothesis tests – used in reliability engineering for analyzing the upcoming failure development within product fleets in the field. This study continues previous research; cf. Puls and Bracke (2021), Bracke et al. (2021) and Puls and Bracke. The spreading behavior of a COVID-19 infection wave can be described by Weibull distribution model in a sound way, related to a short time interval. The interpretation of the Weibull model parameters allows the assessment of the COVID-19 infection wave characteristics and generates additional information to classical infection analyzing models like the SIR model (Kermack and Kendrick 1927). Furthermore, statistical hypothesis tests are used to analyze the observed characteristics of the infection waves with regard to their significance. Observations like change points, trends, peaks and further systematically developments within the infection situation and the resulting potential risk can be substantiated. Finally, the characteristics of the COVID-19 infection waves are analyzed in the context of other common infectious diseases in Germany like Influenza or Norovirus. Differences in the spreading behavior of COVID-19 in comparison to these well-known infectious diseases are underlined for different pandemic phases. REFERENCES A. Puls and S. Bracke (2021). Reliability Methods for analyzing COVID-10 pandemic spreading behavior, lockdown impact and infectiousness. In: Proceedings of the 31th European Safety and Reliability Conference (Eds.: Bruno Castanier, Marko Cepin, David Bigaud and Christophe Berenguer). Research Publishing Services, Singapore. S. Bracke, A. Puls and M. Inoue (2021). COVID-19 pandemic: Analyzing of restrictions, medical care and prevention measures in Germany and Japan. In: Proceedings of the 31th European Safety and Reliability Conference (Eds.: Bruno Castanier, Marko Cepin, David Bigaud and Christophe Berenguer), Research Publishing Services, Singapore. A. Puls and S. Bracke (2021). COVID-19 pandemic risk analytics: Data mining with reliability engineering methods for analyzing spreading behavior and comparison with infectious diseases. In: van Gulijk C., Zaitseva E. (eds) Reliability Engineering and Computational Intelligence. Studies in Computational Intelligence, vol 976. Springer. Kermack, W.O., A.G. McKendrick (1927), A Contribution to the Mathematical Theory of Epidemics. Proc. Roy. Soc. A, Vol. 115, pp. 700-721.

Paper BR91 | Download the paper file. |

Name: Stefan Bracke (bracke@uni-wuppertal.de)

Bio: 1991 – 1995 Studies of Mechanical Engineering; University of Bochum 1997 – 1999 Doctor´s Thesis: „Quality strategies regarding to the reuse of components of technical products within the product remanufacturing (product recycling)” 1996 – 1999 University of Bochum, Chair of Manufacturing System Planning, Prof. Dr.-Ing. H. Schnauber, Section: Quality Planning and Control 1996 – 2000 Consultancy INNOSYS GmbH & Co. KG; Quality Management, Bochum, Germany 2000 – 2006 Dr. Ing. h.c. F. Porsche AG, Department of Quality Management, Stuttgart, Germany 2007 – 2010 Cologne University of Applied Sciences, Cologne, GermanyFaculty of Vehicle Systems and Production, Institute of Production,Professor; Department of Quality Management and Production Metrology Since 10.2010 University of Wuppertal, Germany, Faculty of Safety EngineeringProfessorship: Chair of Reliability Engineering and Risk Analytics Since 04.2016 Guest Professorship at Meiji University, Kawasaki, Tokio, Japan

Country: DEU
Company: University of Wuppertal
Job Title: Professor

Paper 2 KS45

Lead Author: Karthik Sankaran Co-author(s): Bineh Ndefru (bndefru@ucla.edu), Theresa Stewart (theresa@risksciences.ucla.edu), Prof. Ali Mosleh (mosleh@g.ucla.edu), Arjun Earthperson (aarjun@ncsu.edu), Natalie Zawalick (nataliez@ucla.edu)

Risk-Informed Decision-Making Tool for COVID-19 Community Behavior and Intervention Scenario Assessment
The spread of the COVID-19 pandemic across the world has presented a unique problem to researchers and policymakers alike. In addition to uncertainty around the nature of the virus itself, the impact of rapidly changing policy decisions on the spread of the virus has been difficult to predict. Using an epidemiological SIRD model as a basis, this paper presents a methodology developed to address the wide variety of uncertain factors impacting disease spread, and ultimately to understand how a policy decision may impact the community long term. The model being presented, named the COVID-19 Decision Support (CoviDeS) tool, is an agent-based time simulation model which uses Bayesian networks to determine state changes of each individual. The CoviDeS model has a level of interpretability more extensive than many of the existing models, allowing for insights to be drawn regarding the relationships between various inputs and the transmission of the disease. Test cases will be presented for different scenarios that demonstrate relative differences in transmission resulting from different policy decisions. Further, we will demonstrate the model’s ability to support decisions for a smaller sub-community that is contained in a larger population center (e.g. a university within a city). For example, one might question if a university like UCLA should reopen with the emergence of new variants of the disease, or how media coverage might influence spread, or why masks continue to be mandated even after vaccines have been administered to large portions of the population. This paper details an approach for modelling a complex, dynamic problem such as COVID-19 that allows modelers to answer these and other difficult questions. Results of simulations for the city of Los Angeles are presented to demonstrate the use of the model for parametric analysis that could give insight to real world scenarios of interest. Though improvements can be made in the model’s accuracy relative to real case data, the methods presented offer value for future use either as a predictive tool or as a decision-making tool for COVID-19 or future pandemic scenarios.

Risk-Informed Decision-Making Tool for COVID-19 Community Behavior and Intervention Scenario Assessment

The spread of the COVID-19 pandemic across the world has presented a unique problem to researchers and policymakers alike. In addition to uncertainty around the nature of the virus itself, the impact of rapidly changing policy decisions on the spread of the virus has been difficult to predict. Using an epidemiological SIRD model as a basis, this paper presents a methodology developed to address the wide variety of uncertain factors impacting disease spread, and ultimately to understand how a policy decision may impact the community long term. The model being presented, named the COVID-19 Decision Support (CoviDeS) tool, is an agent-based time simulation model which uses Bayesian networks to determine state changes of each individual. The CoviDeS model has a level of interpretability more extensive than many of the existing models, allowing for insights to be drawn regarding the relationships between various inputs and the transmission of the disease. Test cases will be presented for different scenarios that demonstrate relative differences in transmission resulting from different policy decisions. Further, we will demonstrate the model’s ability to support decisions for a smaller sub-community that is contained in a larger population center (e.g. a university within a city). For example, one might question if a university like UCLA should reopen with the emergence of new variants of the disease, or how media coverage might influence spread, or why masks continue to be mandated even after vaccines have been administered to large portions of the population. This paper details an approach for modelling a complex, dynamic problem such as COVID-19 that allows modelers to answer these and other difficult questions. Results of simulations for the city of Los Angeles are presented to demonstrate the use of the model for parametric analysis that could give insight to real world scenarios of interest. Though improvements can be made in the model’s accuracy relative to real case data, the methods presented offer value for future use either as a predictive tool or as a decision-making tool for COVID-19 or future pandemic scenarios.

Paper KS45 | Download the paper file. |

A PSAM Profile is not yet available for this author.

Paper 3 SB270

Lead Author: Stefania Benucci Co-author(s): Enrico Casu ecasu@aurigaconsulting.it Andrea Mancini amancini@aurigaconsulting.it

A new simplified methodology for Quantitative Risk Assessment of Carbon Capture and Storage Plant
Currently the most quantitative risk assessment performed for Carbon Capture and Storage Project neglected several known consequences, as well as cryogenic effects, visibility issues, focusing only on toxicological characteristics of carbon dioxide. In this paper, we would like to present as the absolutely not negligible contribution related to cryogenic effects and visibility issues can significantly change the results in terms of risk for personnel, people and assets, proposing a new simplified methodology for performing quantitative risk assessment capable to take into account all the facets of phenomena consequent from carbon dioxide releases (only with the use of PHAST software for consequences evaluation). In fact, contact of equipment with solid carbon dioxide created during the release when temperature and pressure fallen below the triple point can lead to significant domino effect due to structural embrittlement. Cold burns and visibility issues can increase the risk for people related to carbon dioxide, because the damage distances associated to toxicological effects can be lower than damage distances linked to escape ways impairment and potential personnel injuries.

A new simplified methodology for Quantitative Risk Assessment of Carbon Capture and Storage Plant

Currently the most quantitative risk assessment performed for Carbon Capture and Storage Project neglected several known consequences, as well as cryogenic effects, visibility issues, focusing only on toxicological characteristics of carbon dioxide. In this paper, we would like to present as the absolutely not negligible contribution related to cryogenic effects and visibility issues can significantly change the results in terms of risk for personnel, people and assets, proposing a new simplified methodology for performing quantitative risk assessment capable to take into account all the facets of phenomena consequent from carbon dioxide releases (only with the use of PHAST software for consequences evaluation). In fact, contact of equipment with solid carbon dioxide created during the release when temperature and pressure fallen below the triple point can lead to significant domino effect due to structural embrittlement. Cold burns and visibility issues can increase the risk for people related to carbon dioxide, because the damage distances associated to toxicological effects can be lower than damage distances linked to escape ways impairment and potential personnel injuries.

Paper SB270 | Download the paper file. | Download the presentation PowerPoint file.

A PSAM Profile is not yet available for this author.