Dynamical network models for cattle trade: towards economy-based epidemic risk assessment

In the context of its plan to fight against bioterrorism, the Ministry of Health asked the Institut de Veille Sanitaire to evaluate the epidemic risk from a release of the small pox virus, and to make recommendations on potential vaccination strategies to be implemented. A benefit/risk assessment of various vaccination scenarios, including vaccination of the whole French population, was carried out to evaluate the severity of a terrorist action threat. This analysis concludes that at this stage, vaccination action does not seem to be justified. Even in the case of a real threat, the vaccination of frontline healthcare personnel, and in particular of contacts of cases, must be given priority.

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COVID-19 seeding time and doubling time model: an early epidemic risk assessment tool

Infectious Diseases of Poverty ◽

10.1186/s40249-020-00685-4 ◽

2020 ◽

Vol 9 (1) ◽

Cited By ~ 2

Author(s):

Lei Zhou ◽

Jiang-Mei Liu ◽

Xiao-Ping Dong ◽

Jennifer M. McGoogan ◽

Zun-You Wu

Keyword(s):

Risk Assessment ◽

Doubling Time ◽

Assessment Tool ◽

Risk Assessment Tool ◽

Time Model ◽

Seeding Time ◽

Epidemic Risk

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The use of network models in the inspection of objects of industrial installations.

MATEC Web of Conferences ◽

10.1051/matecconf/201821002052 ◽

2018 ◽

Vol 210 ◽

pp. 02052

Author(s):

Jarosław Napiórkowski

Keyword(s):

Risk Assessment ◽

Network Analysis ◽

Risk Analysis ◽

Network Model ◽

Network Models ◽

Single Element ◽

Simple Method ◽

Safety Requirements ◽

Centrality Analysis ◽

The Relationship

Today’s industrial installations consist of many interconnected elements. The source of a threat or failure of an installation can be not only damage to a single element (device), but also the interaction between individual elements of such a system. In order for the installation to meet safety requirements, the risk of failure should be properly assessed and appropriate prevention systems should be selected and the effects of its occurrence reduced. In order to achieve this, many care for the safety of such structures, inspection organizations carry out risk analysis and risk assessment with various methods. Due to the large number of elements that comprise installations of this class, the problem is to find cleary, uncomplicated and easy to implemented (cheap and low time-consuming) way to identify the relationship between its various elements. Possibility of specify and visualize relationships between elements of installations, their vulnerabilities, safeguards and the effects of threats is an unquestionable advantage of the network models in case of risk assessment carried out on a model built in this way. They allow network analysis based on centrality analysis. Relatively simple method of analysis of these networks. This article discusses few from many areas of application the network model in risk analysis in technical structures.

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Formulation of Pruning Maps with Rhythmic Neural Firing

Mathematics ◽

10.3390/math7121247 ◽

2019 ◽

Vol 7 (12) ◽

pp. 1247

Author(s):

Feng-Sheng Tsai ◽

Yi-Li Shih ◽

Chin-Tzong Pang ◽

Sheng-Yi Hsu

Keyword(s):

Network Dynamics ◽

Network Models ◽

Feedforward Neural Networks ◽

Oscillatory Activity ◽

Control Network ◽

Neural Function ◽

Network System ◽

Neural Firing ◽

Dynamical Network ◽

Neuronal Connections

Rhythmic neural firing is thought to underlie the operation of neural function. This triggers the construction of dynamical network models to investigate how the rhythms interact with each other. Recently, an approach concerning neural path pruning has been proposed in a dynamical network system, in which critical neuronal connections are identified and adjusted according to the pruning maps, enabling neurons to produce rhythmic, oscillatory activity in simulation. Here, we construct a sort of homomorphic functions based on different rhythms of neural firing in network dynamics. Armed with the homomorphic functions, the pruning maps can be simply expressed in terms of interactive rhythms of neural firing and allow a concrete analysis of coupling operators to control network dynamics. Such formulation of pruning maps is applied to probe the consolidation of rhythmic patterns between layers of neurons in feedforward neural networks.

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Neural network models for growth ofSalmonellaserotypes in ground chicken subjected to temperature abuse during cold storage for application in HACCP and risk assessment

International Journal of Food Science & Technology ◽

10.1111/ijfs.13242 ◽

2016 ◽

Vol 52 (1) ◽

pp. 214-221 ◽

Cited By ~ 6

Author(s):

Thomas. P. Oscar

Keyword(s):

Neural Network ◽

Risk Assessment ◽

Cold Storage ◽

Network Models ◽

Neural Network Models

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Network Models and Systemic Risk Assessment

Handbook on Systemic Risk ◽

10.1017/cbo9781139151184.016 ◽

2013 ◽

pp. 287-305 ◽

Cited By ~ 27

Author(s):

Helmut Elsinger ◽

Alfred Lehar ◽

Martin Summer

Keyword(s):

Risk Assessment ◽

Systemic Risk ◽

Network Models

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A New Model for Complex Dynamical Networks Considering Random Data Loss

Entropy ◽

10.3390/e21080797 ◽

2019 ◽

Vol 21 (8) ◽

pp. 797

Author(s):

Xu Wu ◽

Guo-Ping Jiang ◽

Xinwei Wang

Keyword(s):

Network Model ◽

Network Models ◽

Lyapunov Stability Theory ◽

Complex Dynamical Networks ◽

Output Coupling ◽

Data Loss ◽

Complex Dynamical Network ◽

Dynamical Networks ◽

Dynamical Network ◽

Proposed Model

Model construction is a very fundamental and important issue in the field of complex dynamical networks. With the state-coupling complex dynamical network model proposed, many kinds of complex dynamical network models were introduced by considering various practical situations. In this paper, aiming at the data loss which may take place in the communication between any pair of directly connected nodes in a complex dynamical network, we propose a new discrete-time complex dynamical network model by constructing an auxiliary observer and choosing the observer states to compensate for the lost states in the coupling term. By employing Lyapunov stability theory and stochastic analysis, a sufficient condition is derived to guarantee the compensation values finally equal to the lost values, namely, the influence of data loss is finally eliminated in the proposed model. Moreover, we generalize the modeling method to output-coupling complex dynamical networks. Finally, two numerical examples are provided to demonstrate the effectiveness of the proposed model.

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