scholarly journals Extending the Range of COVID-19 Risk Factors in a Bayesian Network Model for Personalised Risk Assessment

2020 ◽  
Vol 4 (6) ◽  
Author(s):  
Prodhan G
Keyword(s):  
Risk Factors ◽  
Risk Assessment ◽  
Bayesian Network ◽  
Network Model ◽  
Bayesian Network Model

An Artificial Neural Network and Bayesian Network model for liquidity risk assessment in banking

2018 ◽  
Vol 275 ◽  
pp. 2525-2554 ◽  
Author(s):  
Madjid Tavana ◽  
Amir-Reza Abtahi ◽  
Debora Di Caprio ◽  
Maryam Poortarigh
Keyword(s):  
Neural Network ◽  
Risk Assessment ◽  
Artificial Neural Network ◽  
Bayesian Network ◽  
Network Model ◽  
Liquidity Risk ◽  
Bayesian Network Model ◽  
Artificial Neural

scholarly journals A Bayesian network model for personalised COVID19 risk assessment and contact tracing

2020 ◽  
Author(s):  
Norman E Fenton ◽  
Scott McLachlan ◽  
Peter Lucas ◽  
Kudakwashe Dube ◽  
Graham A Hitman ◽  
...  
Keyword(s):  
Risk Factors ◽  
Risk Assessment ◽  
Bayesian Network ◽  
Social Interactions ◽  
Probabilistic Model ◽  
Personal Information ◽  
Contact Tracing ◽  
Smartphone App ◽  
Bayesian Network Model ◽  
The Uk

AbstractConcerns about the practicality and effectiveness of using Contact Tracing Apps (CTA) to reduce the spread of COVID19 have been well documented and, in the UK, led to the abandonment of the NHS CTA shortly after its release in May 2020. We present a causal probabilistic model (a Bayesian network) that provides the basis for a practical CTA solution that addresses some of the concerns and which has the advantage of minimal infringement of privacy. Users of the model can provide as much or little personal information as they wish about relevant risk factors, symptoms, and recent social interactions. The model then provides them feedback about the likelihood of the presence of asymptotic, mild or severe COVID19 (past, present and projected). When the model is embedded in a smartphone app, it can be used to detect new outbreaks in a monitored population and identify outbreak locations as early as possible. For this purpose, the only data needed to be centrally collected is the probability the user has COVID19 and the GPS location.

scholarly journals Study on risk assessment of expressway nighttime maintenance construction: A Dynamic Bayesian Network model

2021 ◽  
Vol 257 ◽  
pp. 02047
Author(s):  
Zhen Tian ◽  
Jinhua Fan ◽  
Qianqian Chen ◽  
Huaichen Hu ◽  
Yanyang Shen
Keyword(s):  
Risk Factors ◽  
Risk Assessment ◽  
Bayesian Network ◽  
Traffic Accidents ◽  
Dynamic Bayesian Network ◽  
Extended Model ◽  
Time Varying ◽  
Bayesian Network Model ◽  
Change Curve ◽  
Or Gate

There are many risk factors and large uncertainties in expressway nighttime maintenance construction(ENMC), and the state of risk factors will change dynamically with time. In this study, a Dynamic Bayesian Network (DBN) model was proposed to investigate the dynamic characteristics of the time-varying probability of traffic accidents during expressway maintenance at night. Combined with Leaky Noisy-or gate extended model, the calculation method of conditional probability is determined . By setting evidences for DBN reasoning, the time series change curve of the probability of traffic accidents and other risk factors are obtained. The results show that DBN can be applied to risk assessment of ENMC.

scholarly journals Information Security Risk Assessment of Smartphones Using Bayesian Networks

2015 ◽  
Author(s):  
Kristian Herland ◽  
Heikki Hämmäinen ◽  
Pekka Kekolahti
Keyword(s):  
Risk Assessment ◽  
Information Security ◽  
Bayesian Networks ◽  
Bayesian Network ◽  
Network Model ◽  
Security Risk ◽  
Bayesian Network Model ◽  
Information Security Risk ◽  
Security Risk Assessment ◽  
Causal Bayesian Network

This study comprises an information security risk assessment of smartphone use in Finland using Bayesian networks. The primary research method is a knowledge-based approach to build a causal Bayesian network model of information security risks and consequences. The risks, consequences, probabilities and impacts are identified from domain experts in a 2-stage interview process with 8 experts as well as from existing research and statistics. This information is then used to construct a Bayesian network model which lends itself to different use cases such as sensitivity and scenario analysis. The identified risks’probabilities follow a long tail wherein the most probable risks include unintentional data disclosure, failures of device or network, shoulder surfing or eavesdropping and loss or theft of device. Experts believe that almost 50% of users share more information to other parties through their smartphones than they acknowledge or would be willing to share. This study contains several implications for consumers as well as indicates a clear need for increasing security awareness among smartphone users.  

Bayesian Network Modeling in Discovering Risk Factors of Dental Caries in Three-Year-Old Children

2019 ◽  
Vol 1 ◽  
pp. 118-125
Author(s):  
W. Łaguna ◽  
J. Bagińska ◽  
A. Oniśko
Keyword(s):  
Risk Factors ◽  
Dental Caries ◽  
Bayesian Network ◽  
Network Model ◽  
Graphical Models ◽  
Network Modeling ◽  
Significant Risk ◽  
Probabilistic Graphical Models ◽  
Bayesian Network Model ◽  
Bayesian Network Modeling

<br/><b>Purpose</b> - The aim of this study was to use probabilistic graphical models to determine dental caries risk factors in three-year-old children. The analysis was conducted on the basis of the questionnaire data and resulted in building probabilistic graphical models to investigate dependencies among the features gathered in the surveys on dental caries. <br/><b>Materials and Methods</b> - The data available in this analysis came from dental examinations conducted in children and from a questionnaire survey of their parents or guardians. The data represented 255 children aged between 36 and 48 months. Self-administered questionnaires contained 34 questions of socioeconomic and medical nature such as nutritional habits, wealth, or the level of education. The data included also the results of oral examination by a dentist. We applied the Bayesian network modeling to construct a model by learning it from the collected data. The process of Bayesian network model building was assisted by a dental expert. <br/><b>Results</b> - The model allows to identify probabilistic relationships among the variables and to indicate the most significant risk factors of dental caries in three-year-old children. The Bayesian network model analysis illustrates that cleaning teeth and falling asleep with a bottle are the most significant risk factors of dental caries development in three-year-old children, whereas socioeconomic factors have no significant impact on the condition of teeth. <br/><b>Conclusions</b> - Our analysis results suggest that dietary and oral hygiene habits have the most significant impact on the occurrence of dental caries in three-year-olds.

scholarly journals Extending the range of COVID-19 risk factors in a Bayesian network model for personalised risk assessment

2020 ◽  
Author(s):  
Georgina Prodhan ◽  
Norman Fenton
Keyword(s):  
Risk Factors ◽  
Bayesian Network ◽  
Network Model ◽  
Time Lag ◽  
Transmission Risk ◽  
Contact Tracing ◽  
Individual Risk ◽  
Bayesian Network Model ◽  
Health Authorities ◽  
The Uk

AbstractA need is emerging for individuals to gauge their own risks of coronavirus infection as it becomes apparent that contact tracing to contain the spread of the virus is not working in many societies. This paper presents an extension of an existing Bayesian network model for an application in which people can add their own personal risk factors to calculate their probability of exposure to the virus and likely severity if they do catch the illness. The data need not be shared with any central authority. In this way, people can become more aware of their individual risks and adjust their behaviour accordingly, as many countries prepare for a second wave of infections or a prolonged pandemic. This has the advantage not only of preserving privacy but also of containing the virus more effectively by allowing users to act without the time lag of waiting to be informed that a contact has been tested and confirmed COVID-19 positive. Through a nuanced assessment of individual risk, it could also release many people from isolation who are judged highly vulnerable using cruder measures, helping to boost economic activity and decrease social isolation without unduly increasing transmission risk. Although much has been written and reported about single risk factors, little has been done to bring these factors together in a user-friendly way to give an overall risk rating. The causal probabilistic model presented here shows the power of Bayesian networks to represent the interplay of multiple, dependent variables and to predict outcomes. The network, designed for use in the UK, is built using detailed data from government and health authorities and the latest research, and is capable of dynamic updates as new information becomes available. The focus of the paper is on the extended set of risk factors.

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