Multi-source information driven epidemic disease co-evolution modeling method and system

Abstract

The invention provides an epidemic co-evolution modeling method and system driven by multi-source information, and belongs to the technical field of epidemic propagation kinetics, and the method comprises the steps: obtaining cognitive information and official information; constructing a double-layer self-adaptive sequential network in which the virtual social contact layer and the physical contact layer are in one-to-one correspondence; integrating a self-adaptive reconnection mechanism driven by cognitive information and a mandatory adjustment mechanism driven by official information in the framework; constructing and solving a kinetic model based on the integrated framework, and obtaining a quantitative mapping relationship between the propagation threshold and the behavior parameters; and based on the relationship, simulating and evaluating propagation scales under different behavior strategies, and outputting prevention and control optimization indexes. By uniformly modeling heterogeneous behavior response driven by multi-source information and real-time dynamic coupling of the heterogeneous behavior response and disease transmission, the defect that an existing model behavior feedback mechanism is single or split is overcome, more accurate simulation of epidemic disease transmission is achieved, and a decision basis is provided for formulating an accurate and efficient public health intervention strategy.

Description

Technical Field

[0001] This invention belongs to the field of epidemic transmission dynamics technology, and specifically relates to a multi-source information-driven method and system for modeling the co-evolution of epidemics. Background Technology

[0002] In the field of epidemic transmission dynamics, constructing accurate mathematical models to simulate the co-evolutionary process of information diffusion and epidemic spread is of great significance for understanding the patterns of epidemic development and formulating effective public health intervention strategies. Classical co-evolutionary models are typically based on a coupled framework of unconscious-conscious-unconscious (UAU) and susceptible-infected-susceptible (SIS). In this paradigm, researchers generally believe that an individual's risk awareness after acquiring epidemic information can prompt them to take certain protective behaviors, thereby inhibiting disease transmission.

[0003] However, with a deeper understanding of the complexities of the real world, existing modeling techniques based on such classic frameworks have gradually revealed their shortcomings in terms of realism and predictive ability. On the one hand, in modeling behavioral responses, existing methods tend to simplify or homogenize information sources and the behavioral patterns they trigger. In real life, information from different channels and of different natures (e.g., neighborhood risk perception from social networks versus public health directives from official channels) has fundamentally different driving mechanisms and manifestations on individual behavior, and existing models struggle to meticulously characterize the coexistence and synergistic effects of such heterogeneous behavioral responses within a unified framework. On the other hand, when describing the dynamic coupling of interaction structures and transmission processes, existing technologies are often limited by pre-defined, relatively static network topologies, failing to fully characterize the dynamic evolution of network structures caused by individuals actively adjusting their contact relationships based on real-time states (such as risk awareness), and the complex, bidirectional, real-time feedback between this evolution and disease transmission. This inadequacy in characterizing the heterogeneous driving mechanisms of behavior and the dynamic network coupling process restricts the model's ability to perform high-fidelity simulations and accurate assessments of the evolution of real-world epidemics, especially the intervention effects under large-scale public health emergencies. Summary of the Invention

[0004] To address the technical problems of inaccurate simulation and prediction of complex behavior-epidemic interactions in existing prediction models, this invention provides an epidemic co-evolution modeling method and system that integrates multi-source information-driven mechanisms, adaptive reconnection and official information-driven mandatory regulation, and includes a two-layer adaptive temporal network framework.

[0005] To achieve the above objectives, the first aspect of the present invention provides a multi-source information-driven method for modeling the co-evolution of epidemics, comprising: Acquire multi-source information input, including cognitive information disseminated in virtual social networks and official information released by authoritative institutions that triggers widespread preventative behavior; Based on the cognitive and official information, a two-layer adaptive temporal network evolution framework is constructed, which includes a virtual social layer and a physical contact layer. The virtual social layer is used to simulate the evolution of an individual's state of consciousness, and the physical contact layer is used to simulate the evolution of an individual's epidemic state. The nodes of the two layers correspond one-to-one. In the two-layer adaptive temporal network evolution framework, an adaptive reconnection mechanism driven by cognitive information is integrated to dynamically reorganize the network topology of the physical contact layer, and a mandatory adjustment mechanism driven by official information is integrated to dynamically regulate the disease transmission parameters of the physical contact layer. Based on a network framework integrating dual behavioral response mechanisms, a system dynamics model is constructed and solved to obtain a quantitative mapping relationship between epidemic transmission thresholds and behavioral response parameters. Simulations are performed based on the quantified mapping relationship to evaluate the scale of epidemic spread under different behavioral response strategies and output optimization indicators for prevention and control strategies.

[0006] Furthermore, the construction of the two-layer adaptive temporal network evolution framework, which includes a virtual social layer and a physical contact layer, also includes: Each individual is defined to have a composite state, which is a combination of a conscious state and an epidemic state, wherein the conscious state includes an unconscious state U and a conscious state A, and the epidemic state includes a susceptible state S and an infected state I; The evolution of the state of consciousness is influenced by information diffusion and individual self-awareness within the virtual social layer; The evolution of the epidemic state is influenced by disease transmission and individual recovery within the physical contact layer.

[0007] Furthermore, the adaptive reconnection mechanism driven by cognitive information specifically involves: in the physical contact layer, an individual in a conscious state A, based on an adaptive reconnection rate... Prioritize establishing or maintaining connections with individuals who have the same epidemic status; The mandatory regulation mechanism driven by official information specifically involves: dynamically calculating the intensity of behavioral responses based on the real-time proportion of infected individuals in the physical contact layer, and adjusting the core parameters of disease transmission accordingly.

[0008] Furthermore, the mandatory adjustment mechanism dynamically calculates the behavioral response intensity and adjusts the core parameters, following the rules below: according to Proportion of individuals in state I at any given time Calculate the intensity of response to mandatory behavior The calculation formula is as follows: ,in, The preset maximum response strength; According to the response intensity Compared to the baseline infection rate Perform real-time corrections to obtain Effective infection rate at any time The calculation formula is as follows: .

[0009] Furthermore, the construction and solution of the system dynamics model specifically includes: Based on the micro-Markov chain method, a dynamic equation describing the state transition of an individual in the two-layer network is constructed. By performing matrix eigenvalue analysis on the aforementioned dynamic equations, the stability boundary of the system near the zero-infection state is solved, thus obtaining the critical threshold for epidemic transmission. ; The critical threshold Recovery rate Adaptive reconnection rate Proportion of conscious individuals in a steady state And functions that describe the physical contact layer network structure characteristics.

[0010] Furthermore, the simulation based on the quantized mapping relationship to evaluate the scale of epidemic transmission under different behavioral response strategies specifically includes: Set a combination of behavioral parameters, including the adaptive reconnection rate f and the maximum forced response strength α0, as well as network parameters and disease transmission parameters; Initialize the network, and in each discrete time step, iteratively perform network topology updates, individual consciousness state updates, and individual epidemic state updates; Iterate until the system reaches a stable state, and then count the proportion of infected individuals at the stable state. ; Repeat the simulation multiple times to eliminate randomness and output the proportion of infected individuals. The average value is used as a quantitative indicator to assess the scale of epidemic spread.

[0011] A second aspect of the present invention provides a multi-source information-driven epidemiological co-evolution modeling system, comprising: The information acquisition module is used to acquire multi-source information input, including cognitive information disseminated in virtual social networks and official information released by authoritative institutions that triggers widespread preventive behavior; The network construction module is used to construct a two-layer adaptive temporal network evolution framework, which includes a virtual social layer and a physical contact layer, based on the cognitive information and official information. The virtual social layer is used to simulate the evolution of an individual's state of consciousness, and the physical contact layer is used to simulate the evolution of an individual's epidemic state. The nodes of the two layers correspond one-to-one. The mechanism integration module is used to integrate, within the two-layer adaptive temporal network evolution framework, an adaptive reconnection mechanism driven by cognitive information to dynamically reorganize the network topology of the physical contact layer, and a mandatory regulation mechanism driven by official information to dynamically regulate the disease transmission parameters of the physical contact layer. The model solving module is used to construct and solve the system dynamics model based on a network framework with an integrated dual behavioral response mechanism, and obtain the quantitative mapping relationship between the epidemic transmission threshold and behavioral response parameters. The simulation evaluation module is used to perform simulations based on the quantified mapping relationship, evaluate the scale of epidemic spread under different behavioral response strategies, and output optimization indicators for prevention and control strategies.

[0012] A third aspect of the present invention provides an electronic device including a memory, a processor, and a program stored in the memory and running on the processor, wherein the processor executes the program to implement the steps in the multi-source information-driven epidemiological co-evolution modeling method as described in the first aspect of the present invention.

[0013] A fourth aspect of the present invention provides a computer-readable storage medium having a program stored thereon that, when executed by a processor, implements the steps in the multi-source information-driven epidemiological co-evolution modeling method as described in the first aspect of the present invention.

[0014] A fifth aspect of the present invention provides a computer program product comprising software code, wherein a program in the software code performs steps in the multi-source information-driven epidemiological co-evolution modeling method as described in the first aspect of the present invention.

[0015] Compared with existing technologies, the multi-source information-driven epidemiological co-evolution modeling method and system provided by this invention have the following beneficial effects: (1) The dual-layer adaptive temporal network evolution framework constructed in this invention, comprising a virtual social layer and a physical contact layer, topologically links the evolution of an individual's state of consciousness (virtual social layer) with the evolution of an epidemic state (physical contact layer). Simultaneously, it defines composite states such as US, AS, UI, and AI, merging these two layers into a unified system description unit. This allows individuals, after acquiring consciousness at the information layer, to directly influence their connection behavior at the physical layer (e.g., adaptive reconnection), while the infection state at the physical layer can in turn influence the acquisition of consciousness at the information layer (e.g., self-awareness). This achieves bidirectional, real-time, and closed-loop coupling feedback between the two dynamic processes, enhancing the model's ability to characterize complex interactions in the real world.

[0016] (2) The present invention provides an integrated adaptive reconnection mechanism driven by cognitive information and a mandatory adjustment mechanism driven by official information, enabling refined modeling and quantitative evaluation of multi-source heterogeneous behavioral responses and their synergistic effects. Specifically, the adaptive reconnection mechanism simulates proactive, localized network reconstruction behavior based on individual risk perception (driven by cognitive information) (in terms of probability). Prioritizing connections to nodes in the same state directly cuts off potential propagation paths from the topology; the mandatory adjustment mechanism simulates global, parameterized protective behavior based on collective social response (driven by official information) (through functions). Dynamically reduce the effective infection rate This involves regulating the dissemination efficiency. By integrating these two behavioral response mechanisms with different underlying mechanisms into the same model, the model can simultaneously characterize bottom-up individual avoidance and top-down social intervention, providing decision support for comprehensively evaluating the effectiveness and synergistic effects of hybrid intervention strategies.

[0017] (3) This invention utilizes the microscopic Markov chain method to accurately derive the system's state transition probability equations under the influence of dual behavioral mechanisms; furthermore, by performing matrix eigenvalue analysis on these equations, the critical condition for the system's equilibrium stability, namely the propagation threshold, can be mathematically rigorously solved. And express it as recovery rate. Reconnection rate Proportion of conscious individuals Functions of key parameters. This transforms the traditionally fuzzy assessment of intervention effects, which relied on extensive simulations, into precise early warning indicators that can be directly calculated using analytical formulas. This improves the model's interpretability and decision-making efficiency, providing a quantitative basis for rapidly evaluating the effectiveness of prevention and control strategies. Attached Figure Description

[0018] The accompanying drawings, which form part of this disclosure, are used to provide a further understanding of this disclosure. The illustrative embodiments of this disclosure and their descriptions are used to explain this disclosure and do not constitute an undue limitation of this disclosure.

[0019] Figure 1 This is a flowchart of the multi-source information-driven epidemiological co-evolution modeling method provided in Embodiment 1 of the present invention; Figure 2 This is a schematic diagram of the behavior-epidemic co-evolution model driven by multi-source information on a multi-layer adaptive temporal network provided in Embodiment 1 of the present invention; Figure 3 This is an architecture diagram of the multi-source information-driven epidemiological co-evolution modeling system provided in Embodiment 2 of the present invention. Detailed Implementation

[0020] It should be noted that the following detailed descriptions are exemplary and intended to provide further illustration of the invention. Unless otherwise specified, all technical and scientific terms used in this invention have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0021] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention. As used herein, unless the context clearly indicates otherwise, the singular form is intended to include the plural form as well. Furthermore, it should be understood that the terms “comprising” and “having”, and any variations thereof, are intended to cover non-exclusive inclusion, for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0022] Where there is no conflict, the embodiments and features in the embodiments of the present invention can be combined with each other.

[0023] All data acquisition in this embodiment is carried out in accordance with laws and regulations and with user consent, and the data is used legally.

[0024] Example 1 like Figure 1 and Figure 2 As shown, this embodiment provides a multi-source information-driven method for modeling the co-evolution of epidemics, including: Acquire multi-source information input, including cognitive information disseminated in virtual social networks and official information released by authoritative institutions that triggers widespread preventative behavior; Based on the cognitive and official information, a two-layer adaptive temporal network evolution framework is constructed, which includes a virtual social layer and a physical contact layer. The virtual social layer is used to simulate the evolution of an individual's state of consciousness, and the physical contact layer is used to simulate the evolution of an individual's epidemic state. The nodes of the two layers correspond one-to-one. In the two-layer adaptive temporal network evolution framework, an adaptive reconnection mechanism driven by cognitive information is integrated to dynamically reorganize the network topology of the physical contact layer, and a mandatory adjustment mechanism driven by official information is integrated to dynamically regulate the disease transmission parameters of the physical contact layer. Based on a network framework integrating dual behavioral response mechanisms, a system dynamics model is constructed and solved to obtain a quantitative mapping relationship between epidemic transmission thresholds and behavioral response parameters. Simulations are performed based on the quantified mapping relationship to evaluate the scale of epidemic spread under different behavioral response strategies and output optimization indicators for prevention and control strategies.

[0025] By distinguishing and inputting two heterogeneous information sources—cognitive information and official information—the multi-source premise of behavioral response was established. Then, a two-layer adaptive temporal network with one-to-one node correspondence was constructed, providing an independent yet interconnected evolutionary space for information diffusion and disease transmission. Within this framework, two mechanisms—adaptive reconnection and mandatory regulation—are integrated to intervene in the disease transmission process from two dimensions: network topology and propagation parameters. Finally, through modeling, solving, and simulation, the intensity of behavioral intervention was quantified into its impact on the transmission threshold and final scale. Ultimately, the simulation of the complex co-evolutionary process of behavior and epidemics was achieved.

[0026] Specifically, the construction of the two-layer adaptive temporal network evolution framework, which includes a virtual social layer and a physical contact layer, further includes: Each individual is defined to have a composite state, which is a combination of a conscious state and an epidemic state, wherein the conscious state includes an unconscious state U and a conscious state A, and the epidemic state includes a susceptible state S and an infected state I; The evolution of the state of consciousness is influenced by information diffusion and individual self-awareness within the virtual social layer; The evolution of the epidemic state is influenced by disease transmission and individual recovery within the physical contact layer.

[0027] This model combines an individual's state of consciousness (U / A) with their state of the epidemic (S / I) into four composite states: US, AS, UI, and AI. It couples individual state variables between the virtual social layer and the physical contact layer, establishing a unified state description system. This enables the model to accurately describe the complete causal chain of how an individual's state of consciousness changes under the influence of information, leading to different behaviors based on that state of consciousness (such as reconnection), and ultimately affecting their epidemic state.

[0028] Specifically, the adaptive reconnection mechanism driven by cognitive information is as follows: In the physical contact layer, an individual in a conscious state A, based on an adaptive reconnection rate... Prioritize establishing or maintaining connections with individuals who have the same epidemic status; The mandatory regulation mechanism driven by official information specifically involves: dynamically calculating the intensity of behavioral responses based on the real-time proportion of infected individuals in the physical contact layer, and adjusting the core parameters of disease transmission accordingly.

[0029] Among them, the adaptive reconnection mechanism stipulates that conscious individuals use probability... Homomorphic connections simulate the behavior of individuals proactively optimizing their local network environment based on risk perception (driven by cognitive information), directly altering the potential path of disease transmission. The mandatory regulatory mechanism stipulates the global adjustment of core parameters based on the infection rate, simulating collective protective behavior at the societal level caused by official information and policies, thus reducing transmission efficiency. Furthermore, the abstract behavioral response is concretized into calculable and controllable model rules, thereby enabling the quantitative analysis of the independent and combined effects of these two behaviors.

[0030] Specifically, the mandatory adjustment mechanism dynamically calculates the behavioral response intensity and adjusts the core parameters, following the rules below: according to Proportion of individuals in state I at any given time Calculate the intensity of response to mandatory behavior The calculation formula is as follows: ,in, The preset maximum response strength; According to the response intensity Compared to the baseline infection rate Perform real-time corrections to obtain Effective infection rate at any time The calculation formula is as follows: .

[0031] Specifically, the construction and solution of the system dynamics model includes: Based on the micro-Markov chain method, a dynamic equation describing the state transition of an individual in the two-layer network is constructed. By performing matrix eigenvalue analysis on the aforementioned dynamic equations, the stability boundary of the system near the zero-infection state is solved, thus obtaining the critical threshold for epidemic transmission. ; The critical threshold Recovery rate Adaptive reconnection rate Proportion of conscious individuals in a steady state And functions that describe the physical contact layer network structure characteristics.

[0032] By employing the microscopic Markov chain method, a precise set of dynamic equations describing the macroscopic evolution of the system can be constructed based on the probabilistic rules of node state transitions. Through matrix eigenvalue analysis of this set of equations, the stability of the system's equilibrium state can be mathematically rigorously determined, and the critical conditions (transmission threshold) for an epidemic outbreak can be analytically identified. This technology elevates the model from a simulation tool to an analytical framework with theoretical predictive capabilities, establishing behavioral intervention parameters (such as...) and their functional relationships with each parameter. , The direct, quantitative relationship between the risk of an outbreak and the risk of an epidemic provides a basis for the theoretical evaluation of early warning and strategy effectiveness, overcoming the uncertainty of relying entirely on simulation.

[0033] Specifically, the simulation based on the quantized mapping relationship to evaluate the scale of epidemic transmission under different behavioral response strategies includes: Configure to include adaptive reconnection rate Maximum forced response strength The combination of behavioral parameters, including network parameters and disease transmission parameters; Initialize the network, and in each discrete time step, iteratively perform network topology updates, individual consciousness state updates, and individual epidemic state updates; Iterate until the system reaches a stable state, and then count the proportion of infected individuals at the stable state. ; Repeat the simulation multiple times to eliminate randomness and output the proportion of infected individuals. The average value is used as a quantitative indicator to assess the scale of epidemic spread.

[0034] By setting different combinations of behavioral parameters and repeatedly performing network and state updates after initialization, the dynamic trajectory of epidemic development under various preset intervention scenarios can be simulated. Through repeated simulations and statistical analysis of the average steady-state infection scale, randomness can be effectively assessed, thus reliably comparing the final effects of different intervention strategies.

[0035] In the first specific embodiment, the operation steps of the modeling method are as follows: Step S1: Construct a multi-layer adaptive temporal network evolution framework driven by multi-source information. By defining adaptive reconnection rules driven by cognitive information and mandatory regulation rules driven by official information, establish the dynamic evolution logic of behavior-epidemic co-evolution, specifically as follows: Step S1.1: Based on the multi-source characteristics of the information, establish a dual behavioral response mechanism: define an adaptive reconnection mechanism driven by cognitive information and a mandatory adjustment mechanism driven by official information, as follows: Adaptive reconnection mechanism: In the physical contact layer, based on the principle of "seeking benefits and avoiding harm," a dynamic evolution rule for network topology driven by individual consciousness is constructed. Specifically, the mechanism includes susceptible individuals disconnecting from infected individuals to avoid infection risk after obtaining epidemic-related information, and infected individuals actively reducing contact with susceptible individuals to block transmission paths after obtaining epidemic information. Furthermore, given the technical characteristics that individual consciousness states are difficult to quantify directly in real-world scenarios, the adaptive reconnection behavior is used as a proxy for individual consciousness states to achieve a quantitative characterization of the consciousness-driven topological cooperative evolution process in multi-layer temporal networks. Mandatory adjustment mechanism: Based on the driving effect of official information and prevention and control policies on the public, a dynamic feedback logic for public preventive behavior is constructed; specifically, the mechanism obtains... The proportion of individuals infected at any given time Calculate the corresponding response strength of the mandatory regulatory behavior. The implementation intensity The evolution logic follows the following calculation formula: (1) in, This represents the maximum intensity of the mandatory behavioral response; further, the calculated intensity of the mandatory regulatory behavioral response is used. Effective infection rate against epidemics The effective infection rate is dynamically adjusted. The calculation logic follows the following formula: (2) in, This represents the baseline infection rate when no mandatory regulatory measures are in place, and This formula establishes the effective infection rate. Intensity of response to mandatory regulatory behavior The mapping relationship, which increases and decreases linearly, allows for precise intervention in the dynamics of epidemic transmission through parameterized control of the effective infection rate. Step S1.2: Construct a multi-layered temporal network architecture and establish individual consciousness and epidemic status classifications; specifically as follows: A multi-layered temporal network consisting of a virtual social layer and a physical contact layer is constructed. The virtual social layer is used for the dissemination of cognitive information, and the physical contact layer is used to describe the spread of epidemics. The nodes between the two layers correspond one-to-one. An individual may be in one of the following four states: Unconsciously susceptible (US), Consciously susceptible (AS), Unconsciously infected (UI), and Consciously infected (AI). Step S1.3: Define the rules for the evolution of the consciousness state of individuals in the virtual social layer; specifically as follows: Establish a two-path mechanism for the transformation from unconscious state U to conscious state A: ① Social contact, i.e., unconscious individuals interact with conscious neighbors through diffusion. ① Obtaining epidemiological information; ② Self-awareness, that is, the rate of self-awareness among infected individuals in the physical contact layer. Spontaneous transformation into conscious state A; Establish the mechanism for the transition from a conscious state A to an unconscious state U: Assume that the individual in conscious state A has a forgetting rate... Forget the relevant information and transform it into an unconscious state U; Step S1.4: Define the rules for the evolution of the epidemic state of individuals in the physical contact layer; specifically as follows: The infection process of susceptible individual S was established: susceptible individuals at the physical layer infect each other through contact with infected neighbors, achieving an effective infection rate. The infected state is classified as infectious stage I. Establish the recovery process of infected individual I: Individuals in the infected state recover at a certain rate. Recover and transform into susceptible state S; Step S1.5: Establish the coupled intervention logic of the dual behavioral response mechanism on the propagation dynamics process; specifically as follows: Through the adaptive reconnection mechanism described in step S1.1, conscious individuals AS or AI prioritize establishing homomorphic connections and disconnecting heteromorphic connections based on the epidemic status of their neighbors, thereby achieving dynamic reorganization of the physical contact layer topology. Through the mandatory adjustment mechanism described in step S1.1, the adjustment factor is utilized. Baseline infection rate A decay mapping is performed to determine the effective infection rate described in step S1.4. ; By leveraging the diffusion of consciousness in the virtual social layer and the topological reorganization and parameter regulation in the physical contact layer, a closed-loop simulation of the dynamics of behavior-epidemic co-evolution driven by multi-source information is achieved.

[0036] Step S1.6: Construct a multi-layer adaptive temporal network evolution framework and establish activity-driven instantaneous network generation and evolution rules; the specific process is as follows: Node attribute configuration and heterogeneity establishment: for the network Each node in the node Assign activity vector ,in and Representing nodes respectively Activity levels in the physical contact layer and the virtual social layer respectively follow an exponential pattern. and The power-law distribution: , ; Inter-step global initialization: at each discrete time step All nodes in the virtual social layer and the physical contact layer are initialized to an isolated state, and historical connection restrictions are cleared to reflect the instantaneous nature of the interaction. Intra-step adaptive connectivity of the physical contact layer: ① Define each node in the physical contact layer With probability Activated; ② If the node is activated If the user is in a conscious state A, then an adaptive reconnection behavior is implemented: Establish... The strip connects to nodes within the layer that have the same epidemic status and establishes... A link connects to a randomly selected node within the layer; where, Indicates the adaptive reconnection rate. This represents the number of edges sent by active nodes in the physical contact layer; ③ If the node is activated In an unconscious state U, then establish The link connects to a randomly selected node; 4) Intra-step random connections in the virtual social layer: This sets the parameters for each node in the virtual social layer. With probability Activated; and established The strip connects to randomly selected nodes within the layer; where This represents the number of edges generated by active nodes in the virtual social layer. 5) At the current time step After completing the state update, remove all established transient connections in the two-layer network, and then... Proceed to the next time step and repeat the above process until the system reaches a steady state; For ease of understanding, “nodes” are used in network topology evolution and “individuals” are used in behavior-epidemic co-evolutionary dynamics.

[0037] Step S2: Construct a dynamic model describing the state evolution of the system using the microscopic Markov chain method, and numerically solve the dynamic model using matrix eigenvalue analysis to obtain the nonlinear mapping relationship between the epidemic transmission threshold and the dual behavioral response parameters, specifically: Step S2.1: At time... Use parameters respectively and Represents a node The probabilities of being in an unconscious susceptible state (US), a conscious susceptible state (AS), an unconscious infected state (UI), and a conscious infected state (AI). ; Step S2.2: Based on the activity-driven instantaneous network generation and evolution rules in step S1.6 above, calculate the probability that susceptible individuals S and infected individuals I in the physical contact layer remain in unconscious state U in the virtual layer. and The formula is as follows: (3) (4) Formula (3) represents the active (inactive) unconscious state of individual U. touch An inactive (active) conscious individual A The probability of not being informed; Formula (4) further introduces the self-awareness rate. Characterizing infected individuals in the physical contact layer They were neither informed of information by their conscious neighbors in the information layer, nor did they spontaneously realize the probability of an epidemic. Step S2.3: Based on the adaptive reconnection mechanism in step S1.1 and the activity-driven instantaneous network generation and evolution rules in step S1.6, calculate the unconscious state U and the conscious state A individuals in the information layer. The probability of maintaining susceptible state S in the physical contact layer and The formula is as follows: (5) (6) In formula (5), the first term represents the active, unconsciously susceptible US individual. touch An inactive infected individual The probability of not being infected, the second term represents the inactive, unconsciously susceptible state of US individuals. touch An active, unconsciously infected UI individual The probability of not being infected, the third item represents the inactive, unconsciously susceptible state of US individuals. touch An active, consciously infected AI individual The probability of not being infected; the first term in formula (6) represents an active, consciously susceptible AS individual. touch An inactive infected individual The probability of not being infected, the second item represents inactive, consciously susceptible AS individuals. touch An active, unconsciously infected UI individual The probability of not being infected, the third item represents the contact between an inactive, consciously susceptible AS individual and another individual. An active, consciously infected AI individual The probability of not being infected; Step S2.4: Based on the microscopic Markov chain method and combined with formulas (3)-(6), construct the system at time [time]. The dynamic evolution model: (7) Taking the first equation in formula (7) as an example, the first term represents the unconsciously susceptible US individual. With probability The probability of maintaining an unconscious US state; the second term represents an unconsciously infected UI individual. With probability The probability of transitioning to the unconscious state (US); the third term represents an individual in the conscious, susceptible state (AS). With probability The probability of transforming into an unconscious US state; the fourth term represents a consciously infected AI individual. With probability The probability of transforming into an unconscious state (US); Step S2.5: Establish the Jacobian matrix for the dynamic model, and use matrix eigenvalue analysis to solve for the stability boundary of the system near the zero-infection steady state, thereby calculating the critical threshold for epidemic transmission. for: (8) The epidemic threshold can be seen from formula (8). Determined by several key parameters: recovery rate Adaptive reconnection rate The proportion of conscious individuals in steady state And the network topology of the physical contact layer, i.e., the number of edges sent by active nodes. The first moment of activity distribution and second moment ; Step S3: By simulating the co-evolution of behavior and epidemic, assess the epidemic transmission threshold and scale under different combinations of behavioral response intensities, and output quantitative indicators to support the design of prevention and control strategies, specifically: Step S3.1: Set dual behavior response parameters, including adaptive reconnection rate. and the maximum intensity of the mandatory behavior response ; Step S3.2: Set the parameters of the multilayer temporal network model, including the network size. The number of edges connecting active nodes in the physical contact layer and the virtual social layer and Activity vector and and discrete time intervals ; Step S3.3: Set the parameters for the information diffusion and epidemic transmission model, including the baseline infection rate of the epidemic. recovery rate Information diffusion rate Forgetting rate Self-awareness rate And the total number of simulations, Ave; Step S3.4: Randomly select a preset proportion of individuals as infected state I and conscious state A; Step S3.5: Calculate the proportion of individuals in state I in the network at the current time. ; Step S3.6: Calculate using formulas (1) and (2) respectively. Intensity of mandatory behavioral response at any given moment and effective infection rate ; Step S3.7: Each At any given time, based on the activity-driven instantaneous network generation and evolution rules in step S1.6 above, an activity-driven multilayer adaptive temporal network evolution model is generated. Step S3.8: Each At any given moment, regarding the spread of information in the virtual social layer, according to the aforementioned step S1.3, the unconscious individual i updates whether it is in a conscious state and whether the conscious individual i has returned to an unconscious state based on the consciousness state of its neighbors in the virtual social layer and its own infection state in the physical contact layer. Step S3.9: Each At any given moment, regarding the spread of the epidemic in the physical contact layer, according to the aforementioned step S1.4, susceptible individual i updates whether it is infected and whether infected individual i has recovered to a susceptible state based on its own state of consciousness in the virtual social layer and the infection status of its neighbors in the physical contact layer. Step S3.10: Continuous evolution of multi-layer adaptive temporal network model, information diffusion, and epidemic transmission process. time; Step S3.11: Repeat steps S3.5-S3.10 until the fluctuation range of the system state parameters is less than the preset threshold, determine that the spread of the epidemic and the diffusion of information have reached a steady state, and end the iteration; Step S3.12: Statistical analysis of the infected state at steady state The proportion of individuals; Step S3.13: Repeat steps S3.4-S3.12 ave times to eliminate random errors; Step S3.14: Calculate the infected state under the simulation results of Ave. The average proportion of individuals is used to output the simulation results of co-evolutionary dynamics.

[0038] This embodiment analyzes the single or combined effects of adaptive reconnection behavior and mandatory regulation behavior on the spread of an epidemic, and further verifies the accuracy of the epidemic spread threshold.

[0039] In the second specific embodiment, this specific embodiment is basically the same as the first specific embodiment described above, with the following differences: Step S3: By simulating the co-evolution of behavior and epidemic, assess the epidemic transmission threshold and scale under different combinations of behavioral response intensities, and output quantitative indicators to support the design of prevention and control strategies, specifically: Step S3.1: Given the maximum intensity of the mandatory behavior response Set adaptive reconnection rate Change from 0 to 1; Step S3.2: Set the parameters of the multilayer temporal network model, including the network size. The number of edges connecting active nodes in the physical contact layer and the virtual social layer and Activity vector and and discrete time intervals ; Step S3.3: Set the parameters for the information diffusion and epidemic transmission model, including the baseline infection rate of the epidemic. recovery rate Information diffusion rate Forgetting rate Self-awareness rate And the total number of simulations, Ave; Step S3.4: Set the relative error analysis formula for numerical iteration and Monte Carlo simulation. ; Step S3.5: Set the susceptibility analysis formula To determine the critical point of an epidemic outbreak based on simulation results; Step S3.6: Randomly select a preset proportion of individuals as infected state I and conscious state A; Step S3.7: Calculate the individuals in state I in the network at the current time; Step S3.8: Calculate using formulas (1) and (2) respectively. Intensity of mandatory behavioral response at any given moment and effective infection rate ; Step S3.9: Each At any given time, based on the activity-driven instantaneous network generation and evolution rules in step S1.6 above, an activity-driven multilayer adaptive temporal network evolution model is generated. Step S3.10: Each At any given moment, regarding the dissemination of information within the virtual social layer, following the aforementioned step S1.3, unconscious individuals... The system updates its own conscious state and status based on the consciousness state of its neighbors in the virtual social layer and its own infection state in the physical contact layer. Whether to return to an unconscious state; Step S3.11: Each At that moment, regarding the spread of the epidemic within the physical contact layer, following the aforementioned step S1.4, susceptible individuals... The system updates its own state of infection status based on its own consciousness in the virtual social layer and the infection status of its neighbors in the physical contact layer, and identifies individuals in the infected state. Whether or not they have recovered to a susceptible state; Step S3.12: Continuous evolution of multi-layer adaptive temporal network model, information diffusion, and epidemic transmission process. time; Step S3.13: Repeat steps S3.7-S3.10 until the fluctuation range of the system state parameters is less than the preset threshold, determine that the spread of the epidemic and the diffusion of information have reached a steady state, and end the iteration; Step S3.14: Statistical analysis of the infected state at steady state The proportion of individuals, calculating the relative error ; Step S3.15: Repeat steps S3.6-S3.14 ave times to eliminate random errors; Step S3.16: Calculate the infected state under the simulation results of Ave. Average proportion and relative error of individuals Average and susceptibility Output the simulation results of the co-evolutionary dynamics.

[0040] This embodiment analyzes the adaptive reconnection rate when other parameters are fixed. For infection status Individual proportions, numerical iteration, and relative errors of Monte Carlo simulation and susceptibility The impact.

[0041] In the third specific embodiment, this specific embodiment is basically the same as the second specific embodiment described above, with the following differences: Step S3.1: Given the adaptive reconnection rate Set the maximum intensity of the mandatory behavior response. Change from 0 to 1; This embodiment analyzes the maximum forced behavior response strength when other parameters are fixed. For infection status Individual proportions, numerical iteration, and relative errors of Monte Carlo simulation and susceptibility The impact.

[0042] In the fourth embodiment, this embodiment is basically the same as the first embodiment described above, with the following differences: Step S3.1: Set adaptive reconnection rate and the maximum intensity of the mandatory behavior response Change from 0 to 1; Step S3.12: Statistical analysis of conscious states during steady state and infected state The proportion of individuals; Step S3.14: Calculate the conscious state under the simulation results of Ave. and infected state The average proportion of individuals is used to output the simulation results of co-evolutionary dynamics.

[0043] This embodiment analyzes the adaptive reconnection rate when other parameters are fixed. and the maximum intensity of the mandatory behavior response The combined impact on information dissemination and the spread of epidemics.

[0044] In the fifth embodiment, this embodiment is basically the same as the first embodiment described above, with the following differences: Step S3.1: Given the adaptive reconnection rate Set the maximum intensity of the mandatory behavior response. Change from 0 to 1; Step S3.2: Given the recovery rate Information diffusion rate Forgetting rate Self-awareness rate And the total number of simulations, Ave, to set the baseline infection rate for the epidemic. Change from 0 to 1; Step S3.12: Statistical analysis of conscious states during steady state and infected state The proportion of individuals; Step S3.14: Calculate the conscious state under the simulation results of Ave. and infected state The average proportion of individuals is used to output the simulation results of co-evolutionary dynamics.

[0045] This embodiment analyzes the maximum forced behavior response strength when other parameters are fixed. and the baseline infection rate of the epidemic The combined impact on information dissemination and the spread of epidemics.

[0046] In the sixth embodiment, this embodiment is basically the same as the first embodiment described above, with the following differences: Step S3.1: Given the maximum intensity of the mandatory behavior response Set adaptive reconnection rate Change from 0 to 1; Step S3.2: Given the recovery rate Information diffusion rate Forgetting rate Self-awareness rate And the total number of simulations, Ave, to set the baseline infection rate for the epidemic. Change from 0 to 1; Step S3.12: Statistical analysis of conscious states during steady state and infected state The proportion of individuals; Step S3.14: Calculate the conscious state under the simulation results of Ave. and infected state The average proportion of individuals is used to output the simulation results of co-evolutionary dynamics.

[0047] This embodiment analyzes the adaptive reconnection rate when other parameters are fixed. and the baseline infection rate of the epidemic The combined impact on information dissemination and the spread of epidemics.

[0048] In the seventh embodiment, this embodiment is basically the same as the first embodiment described above, with the following differences: Step S3.1: Given the maximum intensity of the mandatory behavior response Set adaptive reconnection rate Change from 0 to 1; Step S3.2: Baseline infection rate for a given epidemic recovery rate Forgetting rate Self-awareness rate And the total number of simulations, Ave, and the information diffusion rate are set. Change from 0 to 1; Step S3.12: Statistical analysis of conscious states during steady state and infected state The proportion of individuals; Step S3.14: Calculate the conscious state under the simulation results of Ave. and infected state The average proportion of individuals is used to output the simulation results of co-evolutionary dynamics.

[0049] This embodiment analyzes the adaptive reconnection rate when other parameters are fixed. and information diffusion rate The combined impact on information dissemination and the spread of epidemics.

[0050] In summary, the epidemic co-evolution modeling method based on multi-source information-driven integrated adaptive reconnection and mandatory adjustment mechanisms described in the above embodiments first constructs a multi-layer adaptive temporal network evolution framework driven by multi-source information. By defining adaptive reconnection rules driven by cognitive information and mandatory adjustment rules driven by official information, the dynamic evolution logic of behavior-epidemic co-evolution is established. Second, a dynamic model describing the system state evolution is constructed using the micro-Markov chain method. The dynamic model is numerically solved using matrix eigenvalue analysis to obtain the nonlinear mapping relationship between the epidemic transmission threshold and dual behavioral response parameters. Finally, the epidemic transmission threshold and scale under different combinations of behavioral response intensities are evaluated through simulation of the behavior-epidemic co-evolution process, and quantitative indicators for supporting the design of prevention and control strategies are output. The above embodiments not only achieve quantitative characterization of the synergistic intervention effect of network topology dynamic reorganization and transmission parameter regulation, but also realize the deep regulatory impact of individual cognitive differences on the epidemic evolution trajectory using the model. This can provide accurate risk assessment indicators for responding to potential future outbreaks of sudden epidemics and provide scientific decision-making support for public health departments to formulate efficient prevention and control policies.

[0051] Example 2 like Figure 3 As shown, this embodiment provides a multi-source information-driven epidemiological co-evolution modeling system, including: The information acquisition module is used to acquire multi-source information input, including cognitive information disseminated in virtual social networks and official information released by authoritative institutions that triggers widespread preventive behavior; The network construction module is used to construct a two-layer adaptive temporal network evolution framework, which includes a virtual social layer and a physical contact layer, based on the cognitive information and official information. The virtual social layer is used to simulate the evolution of an individual's state of consciousness, and the physical contact layer is used to simulate the evolution of an individual's epidemic state. The nodes of the two layers correspond one-to-one. The mechanism integration module is used to integrate, within the two-layer adaptive temporal network evolution framework, an adaptive reconnection mechanism driven by cognitive information to dynamically reorganize the network topology of the physical contact layer, and a mandatory regulation mechanism driven by official information to dynamically regulate the disease transmission parameters of the physical contact layer. The model solving module is used to construct and solve the system dynamics model based on a network framework with an integrated dual behavioral response mechanism, and obtain the quantitative mapping relationship between the epidemic transmission threshold and behavioral response parameters. The simulation evaluation module is used to perform simulations based on the quantified mapping relationship, evaluate the scale of epidemic spread under different behavioral response strategies, and output optimization indicators for prevention and control strategies.

[0052] Example 3 Embodiment 3 of the present invention provides an electronic device.

[0053] An electronic device includes a memory, a processor, and a program stored in the memory and running on the processor, wherein the processor executes the program to implement the steps in the multi-source information-driven epidemiological co-evolution modeling method as described in Embodiment 1 of the present invention.

[0054] The detailed steps are the same as those of the multi-source information-driven epidemiological co-evolution modeling method provided in Example 1, and will not be repeated here.

[0055] Example 4 Embodiment 4 of the present invention provides a computer-readable storage medium.

[0056] A computer-readable storage medium having a program stored thereon, which, when executed by a processor, implements the steps in the multi-source information-driven epidemiological co-evolution modeling method as described in Embodiment 1 of the present invention.

[0057] The detailed steps are the same as those of the multi-source information-driven epidemiological co-evolution modeling method provided in Example 1, and will not be repeated here.

[0058] Example 5 Embodiment 5 of the present invention provides a computer program product.

[0059] A computer program product includes software code, wherein the program in the software code performs the steps of the multi-source information-driven epidemiological co-evolution modeling method as described in Embodiment 1 of the present invention.

[0060] The detailed steps are the same as those of the multi-source information-driven epidemiological co-evolution modeling method provided in Example 1, and will not be repeated here.

[0061] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The solutions in the embodiments of the present invention can be implemented using various computer languages, such as the object-oriented programming language Java and the interpreted scripting language JavaScript.

[0062] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, as well as combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0063] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0064] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0065] The above description is merely a preferred embodiment of this practice and is not intended to limit the scope of this practice. Various modifications and variations can be made to this practice by those skilled in the art. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of this practice should be included within the protection scope of this practice.

Claims

1. A multi-source information-driven method for modeling the co-evolution of epidemics, characterized in that, include: Acquire multi-source information input, including cognitive information disseminated in virtual social networks and official information released by authoritative institutions that triggers widespread preventative behavior; Based on the cognitive and official information, a two-layer adaptive temporal network evolution framework is constructed, which includes a virtual social layer and a physical contact layer. The virtual social layer is used to simulate the evolution of an individual's state of consciousness, and the physical contact layer is used to simulate the evolution of an individual's epidemic state. The nodes of the two layers correspond one-to-one. In the two-layer adaptive temporal network evolution framework, an adaptive reconnection mechanism driven by cognitive information is integrated to dynamically reorganize the network topology of the physical contact layer, and a mandatory adjustment mechanism driven by official information is integrated to dynamically regulate the disease transmission parameters of the physical contact layer. Based on a network framework integrating dual behavioral response mechanisms, a system dynamics model is constructed and solved to obtain a quantitative mapping relationship between epidemic transmission thresholds and behavioral response parameters. Simulations are performed based on the quantified mapping relationship to evaluate the scale of epidemic spread under different behavioral response strategies and output optimization indicators for prevention and control strategies.

2. The method as described in claim 1, characterized in that, The construction of a two-layer adaptive temporal network evolution framework, comprising a virtual social layer and a physical contact layer, also includes: Each individual is defined to have a composite state, which is a combination of a conscious state and an epidemic state, wherein the conscious state includes an unconscious state U and a conscious state A, and the epidemic state includes a susceptible state S and an infected state I; The evolution of the state of consciousness is influenced by information diffusion and individual self-awareness within the virtual social layer; The evolution of the epidemic state is influenced by disease transmission and individual recovery within the physical contact layer.

3. The method as described in claim 2, characterized in that, The adaptive reconnection mechanism driven by cognitive information specifically involves: in the physical contact layer, an individual in a conscious state A, based on an adaptive reconnection rate... Prioritize establishing or maintaining connections with individuals who have the same epidemic status; The mandatory regulation mechanism driven by official information specifically involves: dynamically calculating the intensity of behavioral responses based on the real-time proportion of infected individuals in the physical contact layer, and adjusting the core parameters of disease transmission accordingly.

4. The method as described in claim 3, characterized in that, The mandatory adjustment mechanism dynamically calculates the behavioral response intensity and adjusts the core parameters, following the rules below: according to Proportion of individuals in state I at any given time Calculate the intensity of response to mandatory behavior The calculation formula is as follows: ,in, The preset maximum response strength; According to the response intensity Compared to the baseline infection rate Perform real-time corrections to obtain Effective infection rate at any time The calculation formula is as follows: .

5. The method as described in claim 1, characterized in that, The construction and solution of the system dynamics model specifically includes: Based on the micro-Markov chain method, a dynamic equation describing the state transition of an individual in the two-layer network is constructed. By performing matrix eigenvalue analysis on the aforementioned dynamic equations, the stability boundary of the system near the zero-infection state is solved, thus obtaining the critical threshold for epidemic transmission. ; The critical threshold Recovery rate Adaptive reconnection rate Proportion of conscious individuals in a steady state And functions that describe the physical contact layer network structure characteristics.

6. The method as described in claim 1, characterized in that, The simulation based on the quantized mapping relationship, evaluating the scale of epidemic transmission under different behavioral response strategies, specifically includes: Configure to include adaptive reconnection rate Maximum forced response strength The combination of behavioral parameters, including network parameters and disease transmission parameters; Initialize the network, and in each discrete time step, iteratively perform network topology updates, individual consciousness state updates, and individual epidemic state updates; Iterate until the system reaches a stable state, and then count the proportion of infected individuals at the stable state. ; Repeat the simulation multiple times to eliminate randomness and output the proportion of infected individuals. The average value is used as a quantitative indicator to assess the scale of epidemic spread.

7. A multi-source information-driven epidemiological co-evolution modeling system, characterized in that, include: The information acquisition module is used to acquire multi-source information input, including cognitive information disseminated in virtual social networks and official information released by authoritative institutions that triggers widespread preventive behavior; The network construction module is used to construct a two-layer adaptive temporal network evolution framework, which includes a virtual social layer and a physical contact layer, based on the cognitive information and official information. The virtual social layer is used to simulate the evolution of an individual's state of consciousness, and the physical contact layer is used to simulate the evolution of an individual's epidemic state. The nodes of the two layers correspond one-to-one. The mechanism integration module is used to integrate, within the two-layer adaptive temporal network evolution framework, an adaptive reconnection mechanism driven by cognitive information to dynamically reorganize the network topology of the physical contact layer, and a mandatory regulation mechanism driven by official information to dynamically regulate the disease transmission parameters of the physical contact layer. The model solving module is used to construct and solve the system dynamics model based on a network framework with an integrated dual behavioral response mechanism, and obtain the quantitative mapping relationship between the epidemic transmission threshold and behavioral response parameters. The simulation evaluation module is used to perform simulations based on the quantified mapping relationship, evaluate the scale of epidemic spread under different behavioral response strategies, and output optimization indicators for prevention and control strategies.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the program, it implements the steps of the multi-source information-driven epidemic co-evolution modeling method as described in any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the steps of the multi-source information-driven epidemic co-evolution modeling method as described in any one of claims 1 to 6.

10. A computer program product, comprising software code, characterized in that, The program in the software code performs the steps of the multi-source information-driven epidemiological co-evolution modeling method as described in any one of claims 1 to 6.

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