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Information-epidemic disease co-evolution analysis method under action of dynamic multi-source information and behavior response

A multi-source information and collaborative evolution technology, applied in the field of information-epidemic collaborative evolution analysis, can solve the problem of ignoring the temporal characteristics of individuals in a layer, not considering the partial mapping relationship of corresponding nodes between layers, and being unable to accurately describe information-epidemic collaboration evolutionary process etc.

Pending Publication Date: 2022-03-18
SHANGHAI UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

In addition, most of the existing research is based on the information-epidemic co-evolution analysis method on the multi-layer static network, ignoring the temporal characteristics of the interaction between individuals in the layer, and not considering the part of the corresponding nodes between layers The mapping relationship cannot accurately describe the information-epidemic co-evolution process in real life

Method used

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  • Information-epidemic disease co-evolution analysis method under action of dynamic multi-source information and behavior response
  • Information-epidemic disease co-evolution analysis method under action of dynamic multi-source information and behavior response
  • Information-epidemic disease co-evolution analysis method under action of dynamic multi-source information and behavior response

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Embodiment 1

[0067] see figure 1 , figure 2 and image 3 , an information-epidemic co-evolution analysis method under the action of dynamic multi-source information and behavioral response, applied to the multi-layer time series network with partial node coupling, information-epidemic co-evolution technology under the action of dynamic multi-source information and behavioral response In the field, the operation steps are as follows:

[0068] Step S1.1: Using the vector (a i ,b i ) to represent the activity of node i in the physical contact layer and the information layer, respectively obeying the exponent γ a and gamma b A power-law distribution for :

[0069] Step S1.2: Use the vector C=[c 1 ,c 2 ,...,c N ] to represent the mapping relationship between network layers, and the interlayer coupling coefficient φ( N represents the total number of nodes). For node i, if there is an inter-layer mapping relationship (c i = 1), then node i can receive multi-source information or...

Embodiment 2

[0112] This embodiment is basically the same as the above-mentioned embodiment, and the special features are as follows:

[0113] Step S1: This step is the same as the first embodiment;

[0114] Step S2: This step is the same as the first embodiment;

[0115] Step S3: This step is the same as the first embodiment;

[0116] Step S4: Simulate the dynamic evolution process of epidemic spread and information diffusion, including:

[0117] Step S4.1: Given individual self-awareness and response strengths P and V of behavioral responses;

[0118] Step S4.2: Randomly initialize a certain proportion of nodes as I-state nodes and A-state nodes;

[0119] Step S4.3: Every time Δt, the nodes in the information layer and physical layer evolve according to the rules of steps S1.3-S1.5;

[0120] Step S4.4: Carry out numerical simulation according to formula (7) to determine the average self-awareness and behavioral response of the individual as and where β max i...

Embodiment 3

[0133] This embodiment is basically the same as the above-mentioned embodiment, and the special features are as follows:

[0134] Step S1: This step is the same as the first embodiment;

[0135] Step S2: This step is the same as the first embodiment;

[0136] Step S3: This step is the same as the first embodiment;

[0137] Step S4: Simulate the dynamic evolution process of epidemic spread and information diffusion, including:

[0138] Step S4.1: Given the information dissemination rate λ and the epidemic infection rate β, set the response strength P and V of individual self-awareness and behavioral response to vary from 0 to 1;

[0139] Step S4.2: Randomly initialize a certain proportion of nodes in the physical contact layer as I-state nodes and A-state nodes;

[0140] Step S4.3: Every time Δt, the nodes in the information layer and physical layer evolve according to the rules of steps S1.3-S1.5;

[0141] Step S4.4: Calculate the ratio of I-state nodes a...

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Abstract

The invention discloses an information-epidemic disease co-evolution analysis method under the action of dynamic multi-source information and behavior response. The method comprises the following steps: firstly, constructing a multi-layer sequential network model with coupled node parts; then constructing an unconsciousness-consciousness-unconsciousness, susceptibility-infection-susceptibility model analysis information-epidemic disease propagation dynamics process; then describing conversion between an epidemic disease state and an information state of the node, and deriving an epidemic disease threshold value betac; then simulating a dynamic evolution process of epidemic propagation and information diffusion; and finally, counting the proportion of the infected state nodes and the conscious state nodes in the network in a steady state, and completing an information-epidemic collaborative evolution process. According to the method, the influence of individual time-varying self-consciousness and behavior response on information-epidemic disease co-evolution is considered in a multi-layer sequential network with partially coupled nodes, state conversion of the nodes is described through a micro Markov chain method, and a critical threshold value beta c of the epidemic disease is theoretically solved; and guidance is provided for intervention and control of COVID-19 or other sudden epidemic diseases possibly outbreak in the future.

Description

technical field [0001] The invention relates to the technical field of information-epidemic co-evolution, in particular to an information-epidemic co-evolution analysis method under the action of dynamic multi-source information and behavioral responses. Background technique [0002] In the process of epidemic transmission, it is usually accompanied by the diffusion of information related to the epidemic. Information has typical multi-source and dynamic characteristics. On the one hand, individuals can obtain information through word of mouth and social media (such as WeChat, Sina Weibo, Facebook, and Twitter); on the other hand, infected individuals can Different degrees of self-awareness arise according to the dynamic evolution of the epidemic. At the same time, the scale of information diffusion will dynamically affect the behavioral response of susceptible individuals, such as wearing a mask or avoiding direct contact with others to reduce the risk of individual infecti...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G16H50/80G06F30/20G06F119/14
CPCG16H50/80G06F30/20G06F2119/14
Inventor 王冰洪潇韩越兴
Owner SHANGHAI UNIV
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