EVALUATING JOINT ROUTING AND SCHEDULING IN OPPORTUNISTIC SENSOR NETWORK UNDER LEVY WALK NATURE OF HUMAN MOBILITY MODEL

This chapter addresses diffusion, random walks and congestion in multilayer networks. Here it is revealed that diffusion on a multilayer network can be significantly speed up with respect to diffusion taking place on its single layers taken in isolation, and that sometimes it is possible also to observe super-diffusion. Diffusion is here characterized on multilayer network structures by studying the spectral properties of the supra-Laplacian and the dependence on the diffusion constant among different layers. Random walks and its variations including the Lévy Walk are shown to reflect the improved navigability of multilayer networks with more layers. These results are here compared with the results of traffic on multilayer networks that, on the contrary, point out that increasing the number of layers could be detrimental and could lead to congestion.

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A novel geo-hierarchical population mobility model for spatial spreading of resurgent epidemics

Scientific Reports ◽

10.1038/s41598-021-93810-8 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Alexandru Topîrceanu ◽

Radu-Emil Precup

Keyword(s):

Computational Models ◽

Human Mobility ◽

Discrete Event ◽

Mobility Model ◽

Hierarchical Organization ◽

Population Mobility ◽

Mobility Patterns ◽

Fine Grained ◽

Epidemic Size ◽

Control Of Epidemics

AbstractComputational models for large, resurgent epidemics are recognized as a crucial tool for predicting the spread of infectious diseases. It is widely agreed, that such models can be augmented with realistic multiscale population models and by incorporating human mobility patterns. Nevertheless, a large proportion of recent studies, aimed at better understanding global epidemics, like influenza, measles, H1N1, SARS, and COVID-19, underestimate the role of heterogeneous mixing in populations, characterized by strong social structures and geography. Motivated by the reduced tractability of studies employing homogeneous mixing, which make conclusions hard to deduce, we propose a new, very fine-grained model incorporating the spatial distribution of population into geographical settlements, with a hierarchical organization down to the level of households (inside which we assume homogeneous mixing). In addition, population is organized heterogeneously outside households, and we model the movement of individuals using travel distance and frequency parameters for inter- and intra-settlement movement. Discrete event simulation, employing an adapted SIR model with relapse, reproduces important qualitative characteristics of real epidemics, like high variation in size and temporal heterogeneity (e.g., waves), that are challenging to reproduce and to quantify with existing measures. Our results pinpoint an important aspect, that epidemic size is more sensitive to the increase in distance of travel, rather that the frequency of travel. Finally, we discuss implications for the control of epidemics by integrating human mobility restrictions, as well as progressive vaccination of individuals.

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Amoebic Foraging Model of Metastatic Cancer Cells

Symmetry ◽

10.3390/sym13071140 ◽

2021 ◽

Vol 13 (7) ◽

pp. 1140

Author(s):

Daiki Andoh ◽

Yukio-Pegio Gunji

Keyword(s):

Bayesian Inference ◽

Cancer Cells ◽

Metastatic Cancer ◽

Gait Patterns ◽

Lévy Walk ◽

Levy Walk ◽

Unicellular Organisms ◽

Living Organisms ◽

Walk Algorithm ◽

Metastatic Cancer Cells

The Lévy walk is a pattern that is often seen in the movement of living organisms; it has both ballistic and random features and is a behavior that has been recognized in various animals and unicellular organisms, such as amoebae, in recent years. We proposed an amoeba locomotion model that implements Bayesian and inverse Bayesian inference as a Lévy walk algorithm that balances exploration and exploitation, and through a comparison with general random walks, we confirmed its effectiveness. While Bayesian inference is expressed only by P(h) = P(h|d), we introduce inverse Bayesian inference expressed as P(d|h) = P(d) in a symmetry fashion. That symmetry contributes to balancing contracting and expanding the probability space. Additionally, the conditions of various environments were set, and experimental results were obtained that corresponded to changes in gait patterns with respect to changes in the conditions of actual metastatic cancer cells.

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