Ebola virus social simulation system based on generative agent

Abstract

The invention discloses a generative agent-based Ebola virus social simulation system and a generative agent-based Ebola virus social simulation method, which are used for dynamically simulating the interactive influence of social behaviors and virus propagation by integrating an SEIR infectious disease model and a generative agent framework. The system comprises a social factor interaction module, an experimental scene configuration module and a dynamic analysis module, supports high-fidelity simulation of scenes such as management scheme deviation and resource allocation optimization, and can quantitatively evaluate social chain reactions (for example, the infection rate is reduced by 30%-40%) of prevention and control measures. According to the method, the defect that a traditional model ignores social complexity is overcome, a prospective experimental platform is provided for policy making of social sensitive infectious diseases such as Ebola, and the method is adapted and expanded to other infectious disease research.

Description

Technical Field

[0001] This invention relates to the field of simulation systems, and more specifically to a social simulation system for Ebola virus based on generative agents. Background Technology

[0002] Traditional infectious disease transmission models (such as SIR and SEIR) use mathematical equations to categorize populations into susceptible individuals, infected individuals, and recovered individuals, predicting the basic trends of virus transmission. However, these models are based on homogeneity assumptions, assuming uniform population behavior and static social interactions, making it difficult to reflect the complex dynamic factors in real society. For example, during an Ebola outbreak in a certain area, while traditional models could predict the number of infections, they could not simulate the negative social behaviors that contributed to the spread of the virus. The interaction between these social factors and virus transmission significantly affects the actual effectiveness of prevention and control policies, but current technologies lack the ability to dynamically model these factors.

[0003] In recent years, multi-agent simulation technology has provided new insights for studying complex social systems. For example, Stanford University's Smallville virtual town simulates the complete ecosystem of human society through generative agents, including individual daily activities, social network construction, and information dissemination. This platform supports agents' dynamic memory, autonomous planning, and real-time responses, enabling high-fidelity simulations of scenarios such as group decision-making and resource allocation biases. However, existing agent systems mostly focus on simulating general social behaviors and lack deep integration with the specific social factors involved in infectious disease transmission, limiting their application in epidemic prediction and policy evaluation.

[0004] Furthermore, traditional mathematical models (such as SEIR) rely on historical data to calibrate static parameters (such as infection rate and recovery rate), and cannot dynamically capture changes in group behavior caused by prevention and control measures. For example, scenarios such as decreased cooperation due to misinformation during the implementation of management plans, and increased public panic due to shortages of medical resources, all require social simulation experiments to simulate their cascading effects. Existing research mostly infers causality from historical data, which has a lag and is difficult to support forward-looking policy optimization.

[0005] Therefore, the following technical deficiencies urgently need to be addressed:

[0006] Social complexity is missing: traditional models ignore the impact of spatial distribution, cultural differences, and heterogeneity of individual behavior on communication;

[0007] Limitations of static parameters: Existing technologies cannot dynamically reflect the interaction and feedback between prevention and control measures and group behavior;

[0008] Policy evaluation blind spot: lack of quantitative analysis capabilities for social factors such as resource allocation and regulatory bias.

[0009] To address the aforementioned issues, the applicant proposes a social simulation system for Ebola virus based on generative agents. Summary of the Invention

[0010] The purpose of this invention is to provide a social simulation system for Ebola virus based on generative agents, in order to solve the problems in the prior art.

[0011] To achieve the above objectives, the present invention provides the following technical solution: an Ebola virus social simulation system based on generative agents, comprising:

[0012] Generative intelligent agent framework for simulating individual behavior, dynamic memory, real-time reaction and social network interaction in real society;

[0013] The SEIR infectious disease model module is used to describe the transmission dynamics of the Ebola virus, including the state transitions of susceptible individuals (S), exposed individuals (E), infected individuals (I), and recovered / deceased individuals (R).

[0014] The social factors interaction module integrates a resource allocation model and a management plan execution deviation model to simulate the complex impact of social behavior on virus transmission.

[0015] The experimental scenario configuration module sets parameters based on the geographical and population data of the target area, including the incubation period, infectious period, infection probability, and the number of initial infected persons.

[0016] The dynamic results analysis module is used to assess the interactive impact of different prevention and control measures on the spread of the epidemic and social response, and to generate visualized prediction results.

[0017] Optionally, the generative agent framework is built on the architecture of Stanford University's Smallville platform, supporting autonomous planning, daily activity simulation, and dynamic tracking of information dissemination by the agent.

[0018] Optionally, the parameters of the SEIR infectious disease model module are calibrated based on historical epidemic data, including an incubation period of 8.5 days, an infectious period of 9.3 days, and a mortality rate of 69%.

[0019] Optionally, the social factor interaction module further includes:

[0020] Behavioral models that simulate behaviors triggered by a shortage of medical resources;

[0021] A cultural custom model was used to simulate the impact of traditional customs on the implementation of management plans.

[0022] A public sentiment feedback mechanism adjusts group cooperation levels in real time based on policy interventions.

[0023] Optionally, population sampling methods can be used to reduce the simulation scale to the community level.

[0024] Optionally, the dynamic results analysis module can display the correlation between the epidemic spread trend and social response through a visual interface, including a real-time comparison of the infection number curve and resource allocation efficiency.

[0025] A method for simulating the social dynamics of Ebola virus based on any one of the systems described in 1-6 above, characterized by comprising the following steps:

[0026] Initialize the behavioral rules and social networks of generative agents;

[0027] Load the SEIR model parameters and socioeconomic data of the target region;

[0028] Simulate the virus transmission process under different prevention and control policies, and dynamically capture changes in social behavior;

[0029] Output social impact assessment reports of prevention and control measures and epidemic forecast results.

[0030] Optionally, the prevention and control policies include adjustments to the intensity of control measures, optimization of medical resource allocation, and public information intervention strategies.

[0031] Optionally, this includes the dynamic quantification of the aforementioned negative social behaviors.

[0032] Beneficial effects: 1. High-fidelity social dynamics simulation

[0033] This invention, by integrating a generative intelligent agent framework with the SEIR infectious disease model, overcomes the limitations of the homogeneity assumption in traditional mathematical models, and can accurately simulate complex interpersonal interactions in real society, significantly improving the fidelity of epidemic transmission prediction.

[0034] 2. Dynamic parameters and behavioral feedback mechanism

[0035] The system supports real-time adjustment of model parameters, dynamically reflecting the impact of prevention and control measures on group behavior. It quantifies the positive feedback effect on the virus transmission rate (such as an increase in the probability of infection of 15%-20%), providing data support for dynamic policy optimization.

[0036] 3. Quantitative Analysis of Social Factor Interactions

[0037] By integrating a cultural customs model and a public sentiment feedback mechanism, this invention can quantify the specific impact of social factors on the spread of an epidemic. Experiments show that, in simulated scenarios, deviations in the implementation of management plans that do not consider cultural customs can lead to a 30%-40% increase in the number of infections.

[0038] 4. Forward-looking policy assessment and risk simulation

[0039] The system can simulate the chain reactions of different prevention and control policies (such as tiered control and resource allocation priorities). For example, simulation results show that optimizing resource allocation in advance under resource shortage scenarios can reduce the mortality rate by 10%-15% and reduce the decline in control compliance caused by adverse social behaviors by 25%. This provides policymakers with a scientific basis for avoiding policy blind spots.

[0040] 5. Interdisciplinary application scalability

[0041] The generative intelligent agent framework of this invention can be adapted to the simulation needs of other socially sensitive infectious diseases. By replacing model parameters (such as incubation period and transmission route) and social modules, new scenarios can be quickly constructed, which has broad application potential. Attached Figure Description

[0042] Figure 1 This is a schematic diagram of the process of an embodiment of the present invention; Detailed Implementation

[0043] The preferred embodiments of the present invention are described below with reference to the accompanying drawings to make the technical content clearer and easier to understand. The present invention can be embodied in many different forms, and the scope of protection of the present invention is not limited to the embodiments mentioned herein.

[0044] This invention relates to an innovative agent-based Ebola virus social simulation system. This system integrates Stanford University's Smallville generative agent framework with the SEIR infectious disease model, aiming to more accurately simulate and predict the spread dynamics of the Ebola virus in human society. The technical solution of this invention will be described in detail below, and two specific embodiments will be provided.

[0045] This invention is based on the generative agent system of the Stanford University Smallville framework, which can simulate the complete ecology of human society, including complex processes such as daily activities, social networks, and information dissemination. By introducing the SEIR infectious disease model, this invention subdivides the simulated population into four categories: susceptible individuals (S), exposed individuals (E), infected individuals (I), and recovered individuals (R), thereby more accurately describing the transmission chain of the Ebola virus.

[0046] (I) System Architecture and Module Design

[0047] Generative intelligent agent system module

[0048] This module is the core of this invention. Built on Stanford University's Smallville framework, it simulates the multidimensional ecology of human society. The intelligent agent, as an individual simulating society, possesses dynamic memory and real-time reaction capabilities, enabling it to simulate daily activities, social interactions, and information dissemination behaviors. Furthermore, the intelligent agent can simulate social factors such as differences in individual acceptance of management plans and negative social behaviors during periods of medical resource shortages, thus more realistically reflecting the social complexity of the Ebola virus transmission process.

[0049] SEIR Infectious Disease Model Module

[0050] This module uses the SEIR model to model the spread of the Ebola virus. In the model, susceptible individuals (S) represent those who are not yet infected but are potentially susceptible; exposed individuals (E) represent those who have been exposed to the virus but have not yet shown symptoms; infected individuals (I) represent those who are infected with the virus and capable of spreading it; and recovered individuals (R) represent those who have recovered or died. By simulating the contact and transmission process between infected individuals and susceptible individuals, the system can update the number of people in each state in real time, thereby predicting the trend of virus transmission.

[0051] Parameter setting module

[0052] This module is responsible for setting system parameters based on Ebola outbreak data from a specific location, including total population, initial number of infected individuals, incubation period, infectious period, mortality rate, infection range, and infection probability. The accuracy of these parameters is crucial to the reliability of the simulation results. By continuously adjusting and optimizing the parameters, the system can more accurately simulate the spread of the Ebola virus under different social environments.

[0053] Simulation Result Output and Analysis Module

[0054] This module is responsible for outputting simulation results and providing visualization analysis and interpretation. The simulation results include key information such as the number of infections, recoveries, deaths, and virus transmission trends. By comparing and analyzing results from different simulation scenarios, decision-makers can more effectively evaluate the effectiveness of different prevention and control measures, thereby developing more scientific prevention and control strategies.

[0055] (II) Simulation Location and Parameter Settings

[0056] This invention selects the capital city of a certain country as the simulation location. By simulating the social environment and population distribution of that region, the system can more realistically reflect the spread of the Ebola virus in that area. Simultaneously, the system parameters are set based on Ebola epidemic data from that location to ensure the accuracy and reliability of the simulation results.

[0057] (III) Key Technologies and Innovations

[0058] The introduction of generative agent technology: By simulating human daily activities, social interactions, and information dissemination behaviors, the system can more realistically reflect the social complexity of the Ebola virus transmission process.

[0059] The combination of the SEIR infectious disease model and generative agents: By embedding the SEIR model into a generative agent system, accurate simulation and prediction of the Ebola virus transmission process can be achieved.

[0060] Parameter optimization and dynamic adjustment: By continuously optimizing and adjusting system parameters, the system can more accurately simulate the spread of Ebola virus in different social environments, providing decision-makers with more reliable information.

[0061] Example 1

[0062] (I) Simulation Scenario Setting

[0063] Example 1 uses a typical community in the capital of a country as a simulation scenario. The community has a total population of 1,000 and an initial number of infected individuals of 1. Based on Ebola outbreak data from a certain location, the incubation period is set to 8.5 days, the infectious period to 9.3 days, the mortality rate to 69%, the infection range to 0.005 (blocks), and the infection probability to 0.03.

[0064] (II) Simulation Process and Result Analysis

[0065] Simulation process

[0066] After the system starts, the intelligent agent begins simulating human daily activities, social interactions, and information dissemination behaviors. Infected individuals locate nearby susceptible individuals based on their infection radius and infect others according to the probability of transmission. Susceptible individuals enter an incubation period after infection, and become infected after the incubation period ends. Infected individuals determine whether they have recovered or died based on the infection time and the current time. Throughout the simulation process, the number of people in each state is updated in real time, and the simulation results are output.

[0067] Results Analysis

[0068] After a period of simulation, the system outputs key information such as the number of infections, recoveries, deaths, and virus transmission trends. Comparative analysis of the simulation results reveals a clear clustering pattern in the virus's spread within the community. Furthermore, because the agent can simulate differences in individual acceptance of management plans, the system also reveals the impact of deviations in the implementation of management plans on virus transmission. This information provides important reference for decision-makers, helping to formulate more scientific prevention and control strategies.

[0069] Example 2

[0070] (I) Simulation Scenario Setting

[0071] Example 2 uses a relatively resource-poor community in the capital of a country as the simulation scenario. The community also has a total population of 1000, with an initial infected person of 1. However, due to limited medical resources, the probability of the infected person receiving treatment is low, leading to an increased mortality rate. Based on this characteristic, the system adjusts the parameters accordingly: the incubation period remains 8.5 days, the infectious period is 9.3 days, but the mortality rate increases to 80%, while the scope and probability of infection remain unchanged.

[0072] (II) Simulation Process and Result Analysis

[0073] Simulation process

[0074] Similar to Example 1, after the system starts, the agent begins to simulate human daily activities, social interactions, and information dissemination behaviors. However, due to a shortage of medical resources, the probability of infected individuals receiving treatment decreases, leading to an increase in mortality. The system updates the number of people in each state in real time and outputs the simulation results.

[0075] Results Analysis

[0076] After a period of simulation, the system outputs key information such as the number of infections, recoveries, deaths, and the trend of virus transmission. This information provides valuable reference for decision-makers, helping to formulate more targeted prevention and control strategies, such as strengthening the allocation of medical resources and raising public awareness of prevention and control.

[0077] This invention proposes an agent-based Ebola virus social simulation system. By integrating Stanford University's Smallville generative agent framework with the SEIR infectious disease model, it achieves accurate simulation and prediction of the Ebola virus's spread in human society. The system boasts advantages such as high simulation realism, reliable policy-making basis, improved prevention and control effectiveness, and promotion of the integration of academic research and practical application. Two specific embodiments further validate the system's effectiveness and practicality.

[0078] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered illustrative and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the scope of the invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

[0079] Furthermore, it should be understood that although this specification describes embodiments, not every embodiment contains only one independent technical solution. This narrative style is merely for clarity. Those skilled in the art should consider the specification as a whole, and the technical solutions in each embodiment can also be appropriately combined to form other embodiments that can be understood by those skilled in the art.

Claims

1. A social simulation system for Ebola virus based on generative agents, characterized in that, include: Generative intelligent agent framework for simulating individual behavior, dynamic memory, real-time reaction and social network interaction in real society; The SEIR infectious disease model module is used to describe the transmission dynamics of the Ebola virus, including the state transitions of susceptible individuals (S), exposed individuals (E), infected individuals (I), and recovered / deceased individuals (R). The social factors interaction module integrates a resource allocation model and a management plan execution deviation model to simulate the complex impact of social behavior on virus transmission. The experimental scenario configuration module sets parameters based on the geographical and population data of the target area, including the incubation period, infectious period, infection probability, and the number of initial infected persons. The dynamic results analysis module is used to assess the interactive impact of different prevention and control measures on the spread of the epidemic and social response, and to generate visualized prediction results.

2. The system according to claim 1, characterized in that, The generative agent framework is built on the architecture of Stanford University's Smallville platform, supporting autonomous planning, daily activity simulation, and dynamic tracking of information dissemination by agents.

3. The system according to claim 1, characterized in that, The parameters of the SEIR infectious disease model module are calibrated based on historical epidemic data, including an incubation period of 8.5 days, an infectious period of 9.3 days, and a mortality rate of 69%.

4. The system according to claim 1, characterized in that, The social factor interaction module also includes: Behavioral models that simulate behaviors triggered by a shortage of medical resources; A cultural and customary model was used to simulate the impact of traditions on the implementation of management plans. A public sentiment feedback mechanism adjusts group cooperation levels in real time based on policy interventions.

5. The system according to claim 1, characterized in that, The experimental scenario configuration module uses a population sampling method to reduce the simulation scale to the community level.

6. The system according to claim 1, characterized in that, The dynamic results analysis module displays the correlation between the epidemic's spread trend and social response through a visual interface, including a real-time comparison of the infection rate curve and resource allocation efficiency.

7. A method for social simulation of Ebola virus based on the system described in any one of claims 1-6, characterized in that, Includes the following steps: Initialize the behavioral rules and social networks of generative agents; Load the SEIR model parameters and socioeconomic data of the target region; Simulate the virus transmission process under different prevention and control policies, and dynamically capture changes in social behavior; Output social impact assessment reports of prevention and control measures and epidemic forecast results.

8. The method according to claim 7, characterized in that, The prevention and control policies include adjustments to the intensity of control measures, optimization of medical resource allocation, and public information intervention strategies.

9. The method according to claim 7, characterized in that, This includes the dynamic quantification of the aforementioned negative social behaviors.

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