scholarly journals Ebola Impact and Quarantine in a Network Model

2016 ◽  
Vol 13 (4) ◽  
Author(s):  
Anca Radulescu ◽  
Joanna Herron
Keyword(s):  
Information Transfer ◽  
Short Range ◽  
Compartmental Model ◽  
Dynamic Networks ◽  
Network Connectivity ◽  
Disease Dynamics ◽  
Epidemic Spread ◽  
Connectivity Graph ◽  
Connectivity Patterns ◽  
Local Parameters

Much effort has been directed towards using mathematical models to understand and predict contagious disease, in particular Ebola outbreaks. Classical SIR (susceptible-infected-recovered) compartmental models capture well the dynamics of the outbreak in certain communities, and accurately describe the differences between them based on a variety of parameters. However, repeated resurgence of Ebola contagions suggests that there are components of the global disease dynamics that we don’t yet fully understand and can’t effectively control. In order to understand the dynamics of a more widespread contagion, we placed SIR models within the framework of dynamic networks, with the communities at risk of contracting the virus acting as nonlinear systems, coupled based on a connectivity graph. We study how the effects of the disease (measured as the outbreak impact and duration) change with respect to local parameters, but also with changes in both short-range and long-range connectivity patterns in the graph. We discuss the implications of optimizing both these measures in increasingly realistic models of coupled communities. KEYWORDS: Epidemic Spread; Network Dynamics; Network Connectivity; Coupled Differential Equations; Compartmental Model; Information Transfer; Outbreak Impact; Outbreak Duration

scholarly journals Integration of photocatalytic and dark-operating catalytic biomimetic transformations through DNA-based constitutional dynamic networks

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Chen Wang ◽  
Michael P. O’Hagan ◽  
Ehud Neumann ◽  
Rachel Nechushtai ◽  
Itamar Willner
Keyword(s):  
Nucleic Acid ◽  
Energy Harvesting ◽  
Information Transfer ◽  
Dynamic Networks ◽  
Research Direction ◽  
Cellular Biology ◽  
Dynamic Feedback ◽  
Dynamic Coupling ◽  
Metabolic Processes ◽  
Catalytic Functions

AbstractNucleic acid-based constitutional dynamic networks (CDNs) have recently emerged as versatile tools to control a variety of catalytic processes. A key challenge in the application of these systems is achieving intercommunication between different CDNs to mimic the complex interlinked networks found in cellular biology. In particular, the possibility to interface photochemical ‘energy-harvesting’ processes with dark-operating ‘metabolic’ processes, in a similar way to plants, represents an up to now unexplored yet enticing research direction. The present study introduces two CDNs that allow the intercommunication of photocatalytic and dark-operating catalytic functions mediated by environmental components that facilitate the dynamic coupling of the networks. The dynamic feedback-driven intercommunication of the networks is accomplished via information transfer between the two CDNs effected by hairpin fuel strands in the environment of the system, leading to the coupling of the photochemical and dark-operating modules.

scholarly journals Multiple overlapping dynamic patterns of the visual sensory network in schizophrenia

2020 ◽  
Author(s):  
Mohammad S.E Sendi ◽  
Godfrey D Pearlson ◽  
Daniel H Mathalon ◽  
Judith M Ford ◽  
Adrian Preda ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Visual Processing ◽  
Information Transfer ◽  
Pearson Correlation ◽  
Visual Learning ◽  
Network Connectivity ◽  
Resting State Fmri ◽  
Connectivity Pattern ◽  
Middle Temporal Gyrus ◽  
Middle Temporal

Although visual processing impairments have been explored in schizophrenia (SZ), their underlying neurobiology of the visual processing impairments has not been widely studied. Also, while some research has hinted at differences in information transfer and flow in SZ, there are few investigations of the dynamics of functional connectivity within visual networks. In this study, we analyzed resting-state fMRI data of the visual sensory network (VSN) in 160 healthy control (HC) subjects and 151 SZ subjects. We estimated 9 independent components within the VSN. Then, we calculated the dynamic functional network connectivity (dFNC) using the Pearson correlation. Next, using k-means clustering, we partitioned the dFNCs into five distinct states, and then we calculated the portion of time each subject spent in each state, that we termed the occupancy rate (OCR). Using OCR, we compared HC with SZ subjects and investigated the link between OCR and visual learning in SZ subjects. Besides, we compared the VSN functional connectivity of SZ and HC subjects in each state. We found that this network is indeed highly dynamic. Each state represents a unique connectivity pattern of fluctuations in VSN FNC, and all states showed significant disruption in SZ. Overall, HC showed stronger connectivity within the VSN in states. SZ subjects spent more time in a state in which the connectivity between the middle temporal gyrus and other regions of VNS is highly negative. Besides, OCR in a state with strong positive connectivity between middle temporal gyrus and other regions correlated significantly with visual learning scores in SZ.

scholarly journals Speaker gaze increases information coupling between infant and adult brains

2017 ◽  
Vol 114 (50) ◽  
pp. 13290-13295 ◽  
Author(s):  
Victoria Leong ◽  
Elizabeth Byrne ◽  
Kaili Clackson ◽  
Stanimira Georgieva ◽  
Sarah Lam ◽  
...  
Keyword(s):  
Neural Activity ◽  
Information Transfer ◽  
Eye Gaze ◽  
Network Connectivity ◽  
Neural Connectivity ◽  
Partial Directed Coherence ◽  
Social Signals ◽  
Early Communication ◽  
Direct Gaze ◽  
Communicative Intention

When infants and adults communicate, they exchange social signals of availability and communicative intention such as eye gaze. Previous research indicates that when communication is successful, close temporal dependencies arise between adult speakers’ and listeners’ neural activity. However, it is not known whether similar neural contingencies exist within adult–infant dyads. Here, we used dual-electroencephalography to assess whether direct gaze increases neural coupling between adults and infants during screen-based and live interactions. In experiment 1 (n = 17), infants viewed videos of an adult who was singing nursery rhymes with (i) direct gaze (looking forward), (ii) indirect gaze (head and eyes averted by 20°), or (iii) direct-oblique gaze (head averted but eyes orientated forward). In experiment 2 (n = 19), infants viewed the same adult in a live context, singing with direct or indirect gaze. Gaze-related changes in adult–infant neural network connectivity were measured using partial directed coherence. Across both experiments, the adult had a significant (Granger) causal influence on infants’ neural activity, which was stronger during direct and direct-oblique gaze relative to indirect gaze. During live interactions, infants also influenced the adult more during direct than indirect gaze. Further, infants vocalized more frequently during live direct gaze, and individual infants who vocalized longer also elicited stronger synchronization from the adult. These results demonstrate that direct gaze strengthens bidirectional adult–infant neural connectivity during communication. Thus, ostensive social signals could act to bring brains into mutual temporal alignment, creating a joint-networked state that is structured to facilitate information transfer during early communication and learning.

scholarly journals Internally generated population activity in cortical networks hinders information transmission

2020 ◽  
Author(s):  
Chengcheng Huang ◽  
Alexandre Pouget ◽  
Brent Doiron
Keyword(s):  
Information Transfer ◽  
Network Connectivity ◽  
Network Activity ◽  
Population Code ◽  
Neuron Models ◽  
Population Activity ◽  
Variable Activity ◽  
Code Performance ◽  
Wide Variability ◽  
Pattern Forming

AbstractHow neuronal variability impacts neuronal codes is a central question in systems neuroscience, often with complex and model dependent answers. Most population models are parametric, with a tacitly assumed structure of neuronal tuning and population-wide variability. While these models provide key insights, they purposely divorce any mechanistic relationship between trial average and trial variable neuronal activity. By contrast, circuit based models produce activity with response statistics that are reflection of the underlying circuit structure, and thus any relations between trial averaged and trial variable activity are emergent rather than assumed. In this work, we study information transfer in networks of spatially ordered spiking neuron models with strong excitatory and inhibitory interactions, capable of producing rich population-wide neuronal variability. Motivated by work in the visual system we embed a columnar stimulus orientation map in the network and measure the population estimation of an orientated input. We show that the spatial structure of feedforward and recurrent connectivity are critical determinants for population code performance. In particular, when network wiring supports stable firing rate activity then with a sufficiently large number of decoded neurons all available stimulus information is transmitted. However, if the inhibitory projections place network activity in a pattern forming regime then the population-wide dynamics compromise information flow. In total, network connectivity determines both the stimulus tuning as well as internally generated population-wide fluctuations and thereby dictates population code performance in complicated ways where modeling efforts provide essential understanding.

scholarly journals Study of SEIRD Adaptive-Compartmental Model of COVID-19 Epidemic Spread in Russian Federation Using Optimization Methods

2021 ◽  
Vol 16 (1) ◽  
pp. 136-151
Author(s):  
S.P. Levashkin ◽  
S.N. Agapov ◽  
O.I. Zakharova ◽  
K.N. Ivanov ◽  
E.S. Kuzmina ◽  
...  
Keyword(s):  
Russian Federation ◽  
Compartmental Model ◽  
Intelligent System ◽  
Optimization Methods ◽  
Daily Basis ◽  
Automated System ◽  
Model Parameters ◽  
Epidemic Spread ◽  
The Russian Federation ◽  
The Government

A systemic approach to the study of a new multi-parameter model of the COVID-19 pandemic spread is proposed, which has the ultimate goal of optimizing the manage parameters of the model. The approach consists of two main parts: 1) an adaptive-compartmental model of the epidemic spread, which is a generalization of the classical SEIR model, and 2) a module for adjusting the parameters of this model from the epidemic data using intelligent optimization methods. Data for testing the proposed approach using the pandemic spread in some regions of the Russian Federation were collected on a daily basis from open sources during the first 130 days of the epidemic, starting in March 2020. For this, a so-called data farm was developed and implemented on a local server (an automated system for collecting, storing and preprocessing data from heterogeneous sources, which, in combination with optimization methods, allows most accurately tune the parameters of the model, thus turning it into an intelligent system to support management decisions). Among all model parameters used, the most important are the rate of infection transmission, the government actions and the population reaction.

scholarly journals Impact of Population Mixing Between a Vaccinated Majority and Unvaccinated Minority on Disease Dynamics. Implications for SARS-CoV-2

2021 ◽  
Author(s):  
Ashleigh Tuite ◽  
Afia Amoako ◽  
David Fisman
Keyword(s):  
Compartmental Model ◽  
Vaccine Development ◽  
Health And Safety ◽  
Infection Risk ◽  
Base Case ◽  
Disease Dynamics ◽  
Population Mixing ◽  
Contact Frequency ◽  
Random Mixing ◽  
The Impact

Background: The speed of vaccine development has been a singular achievement during the SARS-CoV-2 pandemic. However, anti-vaccination movements and disinformation efforts have resulted in suboptimal uptake of available vaccines. Vaccine opponents often frame their opposition in terms of the rights of the unvaccinated. Our objective was to explore the impact of mixing of vaccinated and unvaccinated populations on risk among vaccinated individuals. Methods: We constructed a simple Susceptible-Infectious-Recovered (SIR) compartmental model of a respiratory infectious disease with two connected sub-populations: vaccinated individuals and unvaccinated individuals (Figure 1). We modeled the non-random mixing of these two groups using a matrix approach with a mixing constant varied to simulate a spectrum of patterns ranging from random mixing to complete assortativity. We evaluated the dynamics of an epidemic within each subgroup, and in the population as a whole, and also evaluated the contact-frequency-adjusted contribution of unvaccinated individuals to risk among the vaccinated. Results: As expected, the relative risk of infection was markedly higher among unvaccinated individuals than among vaccinated individuals. However, the contact-adjusted contribution of unvaccinated individuals to infection risk during the epidemic was disproportionate with unvaccinated individuals contributing to infection risk among the vaccinated at a rate up to 6.4 times higher than would have been expected based on contact numbers alone in the base case. As assortativity increased the final attack rate decreased among vaccinated individuals, but the contact-adjusted contribution to risk among vaccinated individuals derived from contact with unvaccinated individuals increased. Interpretation: While risk associated with avoiding vaccination during a virulent pandemic accrues chiefly to the unvaccinated, the choices of these individuals are likely to impact the health and safety of vaccinated individuals in a manner disproportionate to the fraction of unvaccinated individuals in the population.

scholarly journals Extensions of Granger Causality Calculations on Brain Networks for Efficient and Accurate Seizure Focus Identification via iEEGs

2021 ◽  
Vol 11 (9) ◽  
pp. 1167
Author(s):  
Victor B. Yang ◽  
Joseph R. Madsen
Keyword(s):  
Granger Causality ◽  
Epilepsy Surgery ◽  
Graph Algorithms ◽  
Rank Order ◽  
Brain Connectivity ◽  
Brain Networks ◽  
Network Connectivity ◽  
Surgery Planning ◽  
Connectivity Patterns ◽  
Potential Applications

Current epilepsy surgery planning protocol determines the seizure onset zone (SOZ) through resource-intensive, invasive monitoring of ictal events. Recently, we have reported that Granger Causality (GC) maps produced from analysis of interictal iEEG recordings have potential in revealing SOZ. In this study, we investigate GC maps’ network connectivity patterns to determine possible clinical correlation with patients’ SOZ and resection zone (RZ). While building understanding of interictal network topography and its relationship to the RZ/SOZ, we identify algorithmic tools with potential applications in epilepsy surgery planning. These graph algorithms are retrospectively tested on data from 25 patients and compared to the neurologist-determined SOZ and surgical RZ, viewed as sources of truth. Centrality algorithms yielded statistically significant RZ rank order sums for 16 of 24 patients with RZ data, representing an improvement from prior algorithms. While SOZ results remained largely the same, this study validates the applicability of graph algorithms to RZ/SOZ detection, opening the door to further exploration of iEEG datasets. Furthermore, this study offers previously inaccessible insights into the relationship between interictal brain connectivity patterns and epileptic brain networks, utilizing the overall topology of the graphs as well as data on edge weights and quantity of edges contained in GC maps.

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