The Effects of Imitation Dynamics on Vaccination Behaviours in SIR-Network Model

We present a series of SIR-network models, extended with a game-theoretic treatment of imitation dynamics which result from regular population mobility across residential and work areas and the ensuing interactions. Each considered SIR-network model captures a class of vaccination behaviours influenced by epidemic characteristics, interaction topology, and imitation dynamics. Our focus is the resultant vaccination coverage, produced under voluntary vaccination schemes, in response to these varying factors. Using the next generation matrix method, we analytically derive and compare expressions for the basic reproduction number R 0 for the proposed SIR-network models. Furthermore, we simulate the epidemic dynamics over time for the considered models, and show that if individuals are sufficiently responsive towards the changes in the disease prevalence, then the more expansive travelling patterns encourage convergence to the endemic, mixed equilibria. On the contrary, if individuals are insensitive to changes in the disease prevalence, we find that they tend to remain unvaccinated. Our results concur with earlier studies in showing that residents from highly connected residential areas are more likely to get vaccinated. We also show that the existence of the individuals committed to receiving vaccination reduces R 0 and delays the disease prevalence, and thus is essential to containing epidemics.

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Methods of Network Planning and Management when Implementing Territory Planning Projects

Promyshlennoe i Grazhdanskoe Stroitel stvo ◽

10.33622/0869-7019.2019.08.49-54 ◽

2019 ◽

pp. 49-54

Keyword(s):

Large Scale ◽

Housing Stock ◽

Network Planning ◽

Network Models ◽

Point Of View ◽

Residential Areas ◽

Basic Principles ◽

Planning And Management ◽

Planning Project ◽

Complex Process

The success of the Program of housing stock renovation in Moscow depends on the efficiency of resource management. One of the main urban planning documents that determine the nature of the reorganization of residential areas included in the Program of renovation is the territory planning project. The implementation of the planning project is a complex process that has a time point of its beginning and end, and also includes a set of interdependent parallel-sequential activities. From an organizational point of view, it is convenient to use network planning and management methods for project implementation. These methods are based on the construction of network models, including its varieties – a Gantt chart. A special application has been developed to simulate the implementation of planning projects. The article describes the basic principles and elements of modeling. The list of the main implementation parameters of the Program of renovation obtained with the help of the developed software for modeling is presented. The variants of using the results obtained for a comprehensive analysis of the implementation of large-scale urban projects are proposed.

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Likelihood-based approach to discriminate mixtures of network models that vary in time

Scientific Reports ◽

10.1038/s41598-021-84085-0 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Naomi A. Arnold ◽

Raul J. Mondragón ◽

Richard G. Clegg

Keyword(s):

Network Model ◽

Change Point ◽

Network Science ◽

Preferential Attachment ◽

Network Models ◽

Explanatory Models ◽

Growth Mechanisms ◽

Artificial Data ◽

Network Growth ◽

Over Time

AbstractDiscriminating between competing explanatory models as to which is more likely responsible for the growth of a network is a problem of fundamental importance for network science. The rules governing this growth are attributed to mechanisms such as preferential attachment and triangle closure, with a wealth of explanatory models based on these. These models are deliberately simple, commonly with the network growing according to a constant mechanism for its lifetime, to allow for analytical results. We use a likelihood-based framework on artificial data where the network model changes at a known point in time and demonstrate that we can recover the change point from analysis of the network. We then use real datasets and demonstrate how our framework can show the changing importance of network growth mechanisms over time.

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Assessing Interventions against Coronavirus Disease 2019 (COVID-19) in Osaka, Japan: A Modeling Study

Journal of Clinical Medicine ◽

10.3390/jcm10061256 ◽

2021 ◽

Vol 10 (6) ◽

pp. 1256

Author(s):

Ko Nakajo ◽

Hiroshi Nishiura

Keyword(s):

Model Comparison ◽

Reproduction Number ◽

State Of Emergency ◽

Illness Onset ◽

Epidemic Dynamics ◽

Osaka Prefecture ◽

Time Of Infection ◽

Single Intervention ◽

Second Wave ◽

Combined Interventions

Estimation of the effective reproduction number, R(t), of coronavirus disease (COVID-19) in real-time is a continuing challenge. R(t) reflects the epidemic dynamics based on readily available illness onset data, and is useful for the planning and implementation of public health and social measures. In the present study, we proposed a method for computing the R(t) of COVID-19, and applied this method to the epidemic in Osaka prefecture from February to September 2020. We estimated R(t) as a function of the time of infection using the date of illness onset. The epidemic in Osaka came under control around 2 April during the first wave, and 26 July during the second wave. R(t) did not decline drastically following any single intervention. However, when multiple interventions were combined, the relative reductions in R(t) during the first and second waves were 70% and 51%, respectively. Although the second wave was brought under control without declaring a state of emergency, our model comparison indicated that relying on a single intervention would not be sufficient to reduce R(t) < 1. The outcome of the COVID-19 pandemic continues to rely on political leadership to swiftly design and implement combined interventions capable of broadly and appropriately reducing contacts.

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Can Network Models Represent Personality Structure and Processes Better than Trait Models Do?

European Journal of Personality ◽

10.1002/per.1875 ◽

2012 ◽

Vol 26 (4) ◽

pp. 444-445 ◽

Cited By ~ 2

Author(s):

Tobias Rothmund ◽

Anna Baumert ◽

Manfred Schmitt

Keyword(s):

Network Model ◽

Explanatory Power ◽

Network Models ◽

Personality Structure ◽

Psychological Processes ◽

Target Article ◽

Flexible Framework ◽

Trait Model ◽

Better Than

We argue that replacing the trait model with the network model proposed in the target article would be immature for three reasons. (i) If properly specified and grounded in substantive theories, the classic state–trait model provides a flexible framework for the description and explanation of person × situation transactions. (ii) Without additional substantive theories, the network model cannot guide the identification of personality components. (iii) Without assumptions about psychological processes that account for causal links among personality components, the concept of equilibrium has merely descriptive value and lacks explanatory power. Copyright © 2012 John Wiley & Sons, Ltd.

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Statistical Comparison of Spike Responses to Natural Stimuli in Monkey Area V1 With Simulated Responses of a Detailed Laminar Network Model for a Patch of V1

Journal of Neurophysiology ◽

10.1152/jn.00845.2009 ◽

2011 ◽

Vol 105 (2) ◽

pp. 757-778 ◽

Cited By ~ 17

Author(s):

Malte J. Rasch ◽

Klaus Schuch ◽

Nikos K. Logothetis ◽

Wolfgang Maass

Keyword(s):

Network Model ◽

Network Models ◽

Cortical Network ◽

Inhibitory Neurons ◽

Sensory Stimuli ◽

Natural Stimuli ◽

Cortical Areas ◽

Model Response ◽

Spiking Activity

A major goal of computational neuroscience is the creation of computer models for cortical areas whose response to sensory stimuli resembles that of cortical areas in vivo in important aspects. It is seldom considered whether the simulated spiking activity is realistic (in a statistical sense) in response to natural stimuli. Because certain statistical properties of spike responses were suggested to facilitate computations in the cortex, acquiring a realistic firing regimen in cortical network models might be a prerequisite for analyzing their computational functions. We present a characterization and comparison of the statistical response properties of the primary visual cortex (V1) in vivo and in silico in response to natural stimuli. We recorded from multiple electrodes in area V1 of 4 macaque monkeys and developed a large state-of-the-art network model for a 5 × 5-mm patch of V1 composed of 35,000 neurons and 3.9 million synapses that integrates previously published anatomical and physiological details. By quantitative comparison of the model response to the “statistical fingerprint” of responses in vivo, we find that our model for a patch of V1 responds to the same movie in a way which matches the statistical structure of the recorded data surprisingly well. The deviation between the firing regimen of the model and the in vivo data are on the same level as deviations among monkeys and sessions. This suggests that, despite strong simplifications and abstractions of cortical network models, they are nevertheless capable of generating realistic spiking activity. To reach a realistic firing state, it was not only necessary to include both N -methyl-d-aspartate and GABAB synaptic conductances in our model, but also to markedly increase the strength of excitatory synapses onto inhibitory neurons (>2-fold) in comparison to literature values, hinting at the importance to carefully adjust the effect of inhibition for achieving realistic dynamics in current network models.

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Prediction Power of Logistic Regression (LR) and Multi-Layer Perceptron (MLP) Models in Exploring Driving Forces of Urban Expansion to Be Sustainable in Estonia

Sustainability ◽

10.3390/su14010160 ◽

2021 ◽

Vol 14 (1) ◽

pp. 160

Author(s):

Najmeh Mozaffaree Pour ◽

Tõnu Oja

Keyword(s):

Land Cover ◽

Driving Forces ◽

Urban Expansion ◽

Network Models ◽

Influential Factors ◽

Residential Areas ◽

Neural Network Models ◽

Independent Variables ◽

The Core ◽

Mlp Model

Estonia mainly experienced urban expansion after regaining independence in 1991. Employing the CORINE Land Cover dataset to analyze the dynamic changes in land use/land cover (LULC) in Estonia over 28 years revealed that urban land increased by 33.96% in Harju County and by 19.50% in Tartu County. Therefore, after three decades of LULC changes, the large number of shifts from agricultural and forest land to urban ones in an unplanned manner have become of great concern. To this end, understanding how LULC change contributes to urban expansion will provide helpful information for policy-making in LULC and help make better decisions for future transitions in urban expansion orientation and plan for more sustainable cities. Many different factors govern urban expansion; however, physical and proximity factors play a significant role in explaining the spatial complexity of this phenomenon in Estonia. In this research, it was claimed that urban expansion was affected by the 12 proximity driving forces. In this regard, we applied LR and MLP neural network models to investigate the prediction power of these models and find the influential factors driving urban expansion in two Estonian counties. Using LR determined that the independent variables “distance from main roads (X7)”, “distance from the core of main cities of Tallinn and Tartu land (X2)”, and “distance from water land (X11)” had a higher negative correlation with urban expansion in both counties. Indeed, this investigation requires thinking towards constructing a balance between urban expansion and its driving forces in the long term in the way of sustainability. Using the MLP model determined that the “distance from existing residential areas (X10)” in Harju County and the “distance from the core of Tartu (X2)” in Tartu County were the most influential driving forces. The LR model showed the prediction power of these variables to be 37% for Harju County and 45% for Tartu County. In comparison, the MLP model predicted nearly 80% of variability by independent variables for Harju County and approximately 50% for Tartu County, expressing the greater power of independent variables. Therefore, applying these two models helped us better understand the causative nature of urban expansion in Harju County and Tartu County in Estonia, which requires more spatial planning regulation to ensure sustainability.

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Emerging, crystalizing, and changing psychological contracts over time: Introducing the iPC-network model

10.31234/osf.io/8ydmf ◽

2019 ◽

Author(s):

Tim Vantilborgh

Keyword(s):

Network Model ◽

Psychological Contract ◽

Dynamic Network ◽

Network Models ◽

Psychological Contracts ◽

Exchange Relationships ◽

Contract Breach ◽

Alternative Approach ◽

The Individual ◽

Over Time

This chapter introduces the individual Psychological Contract (iPC) network model as an alternative approach to study psychological contracts. This model departs from the basic idea that a psychological contract forms a mental schema containing obligated inducements and contributions, which are exchanged for each other. This mental schema is captured by a dynamic network, in which the nodes represent the inducements and contributions and the ties represent the exchanges. Building on dynamic systems theory, I propose that these networks evolve over time towards attractor states, both at the level of the network structure and at the level of the nodes (i.e., breach and fulfilment attractor states). I highlight how the iPC-network model integrates recent theoretical developments in the psychological contract literature and explain how it may advance scholars understanding of exchange relationships. In particular, I illustrate how iPC-network models allow researchers to study the actual exchanges in the psychological contract over time, while acknowledging its idiosyncratic nature. This would allow for more precise predictions of psychological contract breach and fulfilment consequences and explains how content and process of the psychological contract continuously influence each other.

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A configurable model of the synaptic proteome reveals the molecular mechanisms of disease co-morbidity

10.1101/2020.10.27.356899 ◽

2020 ◽

Author(s):

Oksana Sorokina ◽

Colin Mclean ◽

Mike DR Croning ◽

Katharina F Heil ◽

Emilia Wysocka ◽

...

Keyword(s):

Network Model ◽

Molecular Mechanisms ◽

Interaction Network ◽

Network Models ◽

Disease Association ◽

Synaptic Proteins ◽

Protein Interaction Data ◽

Co Morbidity ◽

Genes Encoding ◽

Protein Components

AbstractSynapses contain highly complex proteomes which control synaptic transmission, cognition and behaviour. Genes encoding synaptic proteins are associated with neuronal disorders many of which show clinical co-morbidity. Our hypothesis is that there is mechanistic overlap that is emergent from the network properties of the molecular complex. To test this requires a detailed and comprehensive molecular network model.We integrated 57 published synaptic proteomic datasets obtained between 2000 and 2019 that describe over 7000 proteins. The complexity of the postsynaptic proteome is reaching an asymptote with a core set of ~3000 proteins, with less data on the presynaptic terminal, where each new study reveals new components in its landscape. To complete the network, we added direct protein-protein interaction data and functional metadata including disease association.The resulting amalgamated molecular interaction network model is embedded into a SQLite database. The database is highly flexible allowing the widest range of queries to derive custom network models based on meta-data including species, disease association, synaptic compartment, brain region, and method of extraction.This network model enables us to perform in-depth analyses that dissect molecular pathways of multiple diseases revealing shared and unique protein components. We can clearly identify common and unique molecular profiles for co-morbid neurological disorders such as Schizophrenia and Bipolar Disorder and even disease comorbidities which span biological systems such as the intersection of Alzheimer’s Disease with Hypertension.

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Network Models of Post-traumatic Stress Disorder: A Meta-analysis

10.31234/osf.io/8k4u6 ◽

2020 ◽

Author(s):

Adela-Maria Isvoranu ◽

Sacha Epskamp ◽

Mike W.-L. Cheung

Keyword(s):

Network Model ◽

Post Traumatic Stress Disorder ◽

Traumatic Stress ◽

Stress Disorder ◽

Ptsd Symptom ◽

Network Models ◽

Post Traumatic Stress ◽

Study Heterogeneity ◽

Post Traumatic ◽

Network Studies

Post-traumatic stress disorder (PTSD) researchers have increasingly used psychological network models to investigate PTSD symptom interactions, as well as to identify central driver symptoms. It is unclear, however, how generalizable such results are. We have developed a meta-analytic framework for aggregating network studies while taking between-study heterogeneity into account, and applied this framework to the first-ever meta-analytic study of network models. We analyzed the correlational structures of 52 different samples with a total sample size of n = 29,561, and estimated a single pooled network model underlying the datasets, investigated the scope of between-study heterogeneity, and assessed the performance of network models estimated from single studies. Our main findings are that: (1) While several clear symptom-links and interpretable clusters can be identified in the network, most symptoms feature very similar levels of centrality. To this end, aiming to identify central symptoms in PTSD symptom networks may not be fruitful. (2) We identified large between-study heterogeneity, indicating that it should be expected for networks of single studies to not perfectly align with one-another, and meta-analytic approaches are vital for the study of PTSD networks. (3) Nonetheless, we found evidence that networks estimated from single studies may give rise to generalizable results, as our results aligned with previous descriptive analyses of reported network studies, and network models estimated from single samples lead to similar network structures as the pooled network model. We discuss the implications of these findings for both the PTSD literature as well as methodological literature on network psychometrics.

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Research on Deformation Prediction of Foundation Pit Based on PSO-GM-BP Model

Advances in Civil Engineering ◽

10.1155/2021/8822929 ◽

2021 ◽

Vol 2021 ◽

pp. 1-17

Author(s):

Dongge Cui ◽

Chuanqu Zhu ◽

Qingfeng Li ◽

Qiyun Huang ◽

Qi Luo

Keyword(s):

Network Model ◽

Network Models ◽

Particle Swarm Algorithm ◽

Neural Network Models ◽

Foundation Pit ◽

Bp Network ◽

Deformation Prediction ◽

Application Range ◽

Limited Applicability ◽

Bp Model

Deformation prediction is significant to the safety of foundation pits. Against with low accuracy and limited applicability of a single model in forecasting, a PSO-GM-BP model was established, which used the PSO optimization algorithm to optimize and improve the GM (1, 1) model and the BP network model, respectively. Combining a small amount of measured data during the excavation of a bottomless foundation pit in a Changsha subway station, the calculations based on the PSO-GM model, the PSO-BP network model, and the PSO-GM-BP model compared. The results show that both the GM (1, 1) and BP neural network models can predict accurate results. The prediction optimized by the particle swarm algorithm is more accurate and has more substantial applicability. Due to its reliable accuracy and wide application range, the PSO-GM-BP model can effectively guide the construction of foundation pits, and it also has certain reference significance for other engineering applications.

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