Multi-subject simulation method and system for community transmission of infectious diseases containing asymptomatic infectors

Abstract

The invention belongs to the field of infectious disease community transmission multi-subject simulation, and relates to an infectious disease community transmission multi-subject simulation method and system containing asymptomatic infectors. The method comprises the following steps: establishing a virtual community group which comprises a plurality of communities and a plurality of destinations; distinguishing subjects in the virtual community group by using the health state and the space state, wherein the subjects comprise a susceptible subject S, an exposed subject E, an asymptomatic infection subject A, an infection subject I and a rehabilitation subject R; a motion sequence of all subjects in the virtual community group is established, the infectious disease transmission process is simulated according to the motion sequence, and the motion sequence comprises movement, infection and state updating, infection detection and isolation and community sealing control; and obtaining a simulation result, and evaluating the effectiveness of the infectious disease prevention and control policy. According to the method, rapid evaluation of different prevention and control strategies on infectious disease transmission conditions, social and economic influences caused by infectious diseases and the like can be realized, and beneficial reference is provided for a macroscopic regulation and control policy for restraining infectious diseases.

Description

Technical Field

[0001] This invention relates to the field of multi-agent simulation of community transmission of infectious diseases, specifically to a multi-agent simulation method and system for community transmission of infectious diseases including asymptomatic carriers. Background Technology

[0002] Infectious diseases are a class of diseases caused by various pathogens and capable of spreading between humans, animals, or between humans and animals. For centuries, several epidemics, including avian influenza, H5N1 influenza, SARS, and COVID-19, have hindered economic development and threatened public safety and property. With increasing urbanization and the growing number of migrant populations, the probability of global infectious disease pandemics is also constantly increasing. Therefore, it is necessary to establish simulation models of epidemic transmission in urban communities and to seek feasible strategies to reduce the large-scale spread of infectious diseases and mitigate their harm.

[0003] Currently, research typically uses population classification models such as SIR and its extension SEIR to predict the spread of infectious diseases. These models assign different states to individuals, such as susceptible, exposed, infected, and recovered. Following the start of the novel coronavirus pandemic, the SEAIR model, which includes asymptomatic carriers, has also been explored. In this model, S represents susceptible individuals, E represents exposed individuals, I represents infected individuals, R represents recovered individuals, and A represents asymptomatic carriers. In these models, states are passed at a given rate as the disease evolves, depending on the specific model's parameter settings. While these models have played a role in analyzing the dynamic processes of epidemics, they assume a uniform and random mixture of individuals and use a continuous system of equations to describe the overall characteristics of infectious disease transmission, making it difficult to characterize the impact of individual behavioral patterns on the spread of infectious diseases.

[0004] Multi-agent simulation models, by modeling the micro-behaviors of heterogeneous individuals, simulate the impact of intervention strategies on the overall system in complex scenarios. They can better quantify how various different solutions influence individual behavior in complex scenarios and calculate potential social benefits or costs. Various models for infectious disease transmission have played a crucial role in developing epidemic preparedness plans. Existing multi-agent simulation models are generally used for simulating transmission in micro-areas such as buildings or parks, and are less commonly used for city-level transmission and control simulations. Furthermore, existing multi-agent simulation methods lack the ability to calculate the economic costs of epidemic prevention and control measures, failing to consider both the scale of epidemic control effectiveness and socio-economic costs. Summary of the Invention

[0005] The technical problem to be solved by this invention is to construct a multi-agent simulation method and system for the community transmission of infectious diseases, including asymptomatic carriers, so as to realize the rapid assessment of the spread of infectious diseases and the socio-economic impact of infectious diseases caused by different prevention and control strategies, and provide useful reference for macro-control policies to curb infectious diseases.

[0006] The technical solution adopted by this invention to solve its technical problem is:

[0007] A multi-agent simulation method for community transmission of infectious diseases including asymptomatic carriers includes the following steps:

[0008] Establish a virtual community group, which includes several communities and several destinations;

[0009] The various subjects in the virtual community group are distinguished by their health status and spatial status, including susceptible subjects S, exposed subjects E, asymptomatic infected subjects A, infected subjects I, and recovered subjects R;

[0010] Establish movement sequences for various entities in a virtual community group, and simulate the spread of infectious diseases according to the movement sequences. The movement sequences include movement, transmission and status updates, infection detection and isolation, and community lockdown.

[0011] Obtain simulation results and evaluate the effectiveness of infectious disease prevention and control policies.

[0012] Furthermore, the movement includes four types of movement behaviors: (1) moving from the community to the destination; (2) moving from the destination back to the community; (3) moving from home to another community, or moving from another community back home; (4) moving freely within the current community or the destination; each type of movement behavior has a corresponding probability of occurrence.

[0013] Furthermore, the infection and status update include: during movement, if two people move into the same grid at the same time, their health status is checked; if a susceptible subject comes into contact with an asymptomatic infected subject or an infected subject, the susceptible subject may become infected with the virus, and its health status may change after the incubation period; other changes in health status change over time with a given probability.

[0014] Furthermore, the infection detection and isolation include:

[0015] An additional state P is introduced to simulate the detection of health status, where state P represents a positive test. It is assumed that only pathogens from exposed subject E, asymptomatic infected subject A, or infected subject I can be identified as positive by the test.

[0016] For large-scale testing, each person is tested at a predefined interval;

[0017] For individual testing, testing is conducted spontaneously at random times, and once a test is positive, it is assumed that the individual is isolated in a separate area.

[0018] Furthermore, the community lockdown includes overall lockdown and household lockdown; for overall lockdown, if the number of positive individuals in a community reaches a predefined threshold, the community is closed, and people in the community are not allowed to leave, but can only move within the community; for household lockdown, when symptoms related to the epidemic appear, the possibility of an individual leaving home is reduced to zero, and self-lockdown is carried out in this way.

[0019] Furthermore, the statistical indicators used to assess the effectiveness of infectious disease control policies include: duration, scale of infection, total number of tests, maximum number of isolations, and number of people lost due to relocation. Among these, duration and scale of infection measure the scale of the infectious disease; the smaller the scale, the greater the benefits of the infectious disease control policy. Total number of tests, maximum number of isolations, and number of people lost due to relocation measure the costs of the infectious disease control policy.

[0020] Furthermore, the total cost and total cost rate of infectious disease control policies are calculated using the following formulas:

[0021] Total cost=Test / 24+Isolation+(-LostN)×800

[0022] Total cost rate=Total cost / 800

[0023] Where Test represents the total number of tests, Isolation represents the maximum number of isolations, and LostN represents the number of people lost due to migration.

[0024] A multi-agent simulation system for community transmission of infectious diseases, including asymptomatic carriers, comprising:

[0025] The virtual community group creation module is used to create virtual community groups, which contain several communities and several destinations. It uses health status and spatial status to distinguish the various subjects in the virtual community group, including susceptible subject S, exposed subject E, asymptomatic infected subject A, infected subject I, and recovered subject R.

[0026] The infectious disease transmission process simulation module is used to establish the movement sequence of each subject in a virtual community group and simulate the infectious disease transmission process according to the movement sequence. The movement sequence includes movement, transmission and status update, infection detection and isolation, and community lockdown.

[0027] The simulation results observation and policy evaluation module is used to obtain simulation results and evaluate the effectiveness of infectious disease prevention and control policies.

[0028] The beneficial effects of this invention are as follows:

[0029] This invention proposes SEAIR-TQ, a multi-agent simulation model that includes asymptomatic carriers in the SEIR model. This model uses virus testing and community lockdown as the main mitigation strategies. Within this model framework, the invention studies the spread of epidemics, mitigation strategies, and economic costs. Compared to the standard SEIR model, SEAIR-TQ offers the advantage of providing the possibility of explicitly considering complex behavioral simulations arising from heterogeneity at the individual level, such as individual decisions and preferences, cross-regional spatial movement, and random contact and transmission events. Due to the bottom-up mechanism of the multi-agent model, the model settings only manipulate the micro-level; the overall effect of macro-epidemic spread is presented by the simulation results of individual behaviors, emerging from the aggregation of individual behaviors and the interactions between individuals and the environment. The SEAIR-TQ model can also apply multiple different mitigation strategies in a single framework, enabling the measurement and comparison of epidemic prevention effectiveness and economic costs. Especially for the economic costs of different community lockdown strategies, this method measures the impact on socio-economic activities by measuring changes in individual activity levels, allowing decision-makers to more objectively assess the effectiveness, benefits, and costs of infectious disease control strategies. Attached Figure Description

[0030] Figure 1 This is a schematic diagram of the spatial structure setup of the simulation method of the present invention;

[0031] Figure 2 This is a schematic diagram of the health status of the simulation method of the present invention;

[0032] Figure 3 This is a schematic diagram of the spatial state of the simulation method of the present invention;

[0033] Figure 4 This is a flowchart of the simulation process of the simulation method of the present invention;

[0034] Figure 5 These are the simulation results of the simulation method of this invention. Detailed Implementation

[0035] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to specific embodiments and accompanying drawings.

[0036] I. Spatial Structure Setting of Multi-Agent Simulation System

[0037] This invention creates a virtual community cluster with two types of areas in a multi-agent simulation system, comprising several communities and several destinations (which can be schools, parks, companies, shopping malls, etc.). Figure 1As shown. Spatial points within each region can be set as open-air or enclosed areas according to the simulation task requirements. Initially, each agent (i.e., an individual with autonomous behavior) lives in their home in community A or B. When they need to travel to another region for reasons such as work, local travel, or visits from friends, they will walk, take the subway, or drive to one of their destinations according to their preferences. The distance between regions (communities or destinations) is incorporated into the system to simulate travel time. During movement, one person randomly comes into contact with another; when contact occurs, infectious diseases will spread randomly with a certain probability.

[0038] II. Subject Status Setting and Infectious Disease Transmission Mechanism

[0039] This invention distinguishes agents in a multi-agent simulation system using superimposed health and spatial states. The health state, in the traditional SEIR model, considers asymptomatic carriers; therefore, each agent possesses one and only one of the following health states: susceptible (S), exposed (E), asymptomatic infection (A), infected (I), and recovered (R). Health states are as follows: Figure 2 As shown. At the same time, the spatial state reflects whether each subject is unrestricted and freely movable, or is sealed off or isolated (B), such as... Figure 3 As shown, there are three states: free movement, restricted movement, and isolation.

[0040] The specific definitions of subjects in different states are as follows: Susceptible subject (S) is not infected with the disease and has no antibodies; they can move freely. Exposed subject (E) has been in contact with an infectious disease carrier but has not yet developed obvious symptoms and cannot transmit the virus. Asymptomatic infected subject (A) is asymptomatic after infection but is infectious. Both exposed subject (E) and asymptomatic infected subject (A) can move freely. Infected subject (I) has obvious symptoms. Although it will not be immediately locked down or isolated after the state change, it will tend to stay at home rather than go out. Both asymptomatic infected subject (A) and infected subject (I) can transmit the disease. Recovered subject (R) has recovered from infection and has developed antibodies against any future infection. This invention modifies the SEIR model by adding an asymptomatic infected subject (A) because the presence of symptomatic symptoms is associated with spontaneous testing and self-isolation. Asymptomatic infected subject (A) may move and act like a healthy person, which may increase the likelihood of infecting other individuals. Compared to symptomatic individuals, asymptomatic infected subject (A) has a relatively lower probability of testing positive for the virus.

[0041] Furthermore, this system makes the following assumptions regarding the transmission mechanism of infectious diseases: When a susceptible subject (S) comes into contact with an asymptomatic infected subject (A) or an infected subject (I), the susceptible subject (S) can become infected and transform into an exposed subject (E) with a specified probability. An exposed subject (E) may transform into an asymptomatic infected subject (A) over a period of time, subsequently remaining in an infected state. Additionally, some exposed individuals may directly transform into infected subjects (I). All exposed, asymptomatic, and infected individuals have the potential to enter the recovery period daily, and the transitions between different states are as follows... Figure 2 and Figure 3 As shown.

[0042] III. Simulation Process and Subject Behavior Path

[0043] This invention establishes four motion sequences for each subject in a multi-subject simulation system: movement, infection and status update, infection detection and isolation, and community lockdown. Figure 4 As shown. Figure 4 In this context, "turtle" represents a freely moving subject, "Tick" represents the shortest time interval (i.e., the time step of the simulation), and "A" represents an asymptomatic infected person who is infectious but has not yet been diagnosed.

[0044] (1) Move

[0045] For each freely mobile entity, one of the following four types of movement behaviors will occur: (1) moving from the community to the destination, (2) moving from the destination back to the community, (3) moving from home to another community or from another community back home, and (4) moving freely within the current area (community or destination). Each type of movement behavior has a corresponding probability of occurrence.

[0046] (2) Infection and Status Update

[0047] During movement, if two people move to the same grid (community or destination) simultaneously, the model will check their health status. If a susceptible individual (susceptible subject) comes into contact with an asymptomatic or symptomatic infectious individual (i.e., an asymptomatic infected subject or an infected subject), the susceptible individual may become infected with the virus, and their health status may change after the incubation period. Furthermore, changes in other health statuses (exposed subjects and recovered subjects) occur over time with given probabilities.

[0048] (3) Infection detection and isolation

[0049] This invention introduces an additional state P (positive test) to simulate the detection of a healthy state. For simplicity, it is assumed that only the pathogen in an exposed subject (E), an asymptomatic infected subject (A), or an infected subject (I) can be identified as positive by testing. The model of this invention assumes both mass testing and individual testing mechanisms. Mass testing is considered a centralized strategy where everyone is tested at predefined intervals. For individual testing, the willingness to be tested increases if symptoms appear on an individual, which is a decentralized strategy. Therefore, it will be tested spontaneously at random times. Once a positive test is obtained, it is assumed that the individual is isolated in a separate area (assigned state B). The purpose of isolation is to reduce the potential negative externalities of infected individuals spreading the virus.

[0050] (4) Community lockdown

[0051] Community lockdowns increase physical social distancing between infected and exposed individuals and other members of the community by restricting their movement. For overall lockdowns, if the number of positive individuals in a community reaches a predefined threshold, the community will be closed, and residents will not be allowed to leave, only move within the community. For household lockdowns, individuals who develop epidemic-related symptoms are reduced to zero likelihood of leaving their homes, thus self-isolating in this way.

[0052] The above movement sequence will be repeated cyclically over time to simulate the dynamic changes in the development of infectious diseases and their socio-economic impact.

[0053] IV. Simulation Results Observation and Policy Evaluation

[0054] The simulation results mainly include the consequences of infectious disease transmission and socioeconomic costs. The consequences of infectious disease transmission are primarily calculated based on the duration and the total number of infected individuals (i.e., the scale of the infection). Socioeconomic costs are calculated using the total number of infection tests, the maximum number of people in quarantine, and the loss of individuals who leave their homes.

[0055] Without considering non-prevention and non-economic factors, policy evaluation aims to maximize the control effect of infectious disease transmission while minimizing socioeconomic costs. The feasible solution set of the optimal policy is obtained by taking policy combinations on the Pareto frontier from various simulation results. Within this feasible solution set, implementation aspects and other factors are considered, and strategy combinations that do not meet testing scale constraints or other conditions are eliminated to obtain the final optimal prevention and control policy combination. These other constraints may include personnel, funding, material, organizational, or policy-related constraints encountered in the actual implementation of epidemic control policies.

[0056] Implementation Case:

[0057] Without loss of generality, this paper considers a partial urban simulation involving two communities and two destinations. Using the multi-agent simulation system for community transmission of infectious diseases, including asymptomatic carriers, constructed according to this invention, the effectiveness, benefits, and costs of different infectious disease control strategies are simulated. First, the required parameters must be set to calibrate the system.

[0058] I. Spatiotemporal parameters

[0059] The multi-agent simulation system for community transmission of infectious diseases, including asymptomatic carriers, sets the simulation scenario in discrete regions of a 300x300 spatial grid. Each spatial grid represents a 5m... 2 The local abstract space is equivalent to 0.5625 km in the real world. 2 The time step is set to 2 minutes. At each step, all subjects execute a series of motion sequences, and their states are updated. The simulation can cover a period of 150 days or longer. The system randomly places 800 subjects into the simulation. The system assumes that 25% of the subjects travel by car daily, and the remaining 75% use the subway. A one-way subway journey between the community and the destination takes 30 minutes, with trains running every 10 minutes. A one-way car journey takes 30 minutes to 1 hour. The system assumes that the distance between the community and the destination is too far to walk.

[0060] II. Behavioral Parameters

[0061] The system considers mobility strategies tailored to different health states to mitigate the spread of infectious diseases. The simulation divides each day into four time periods: morning (6:00-9:00), daytime (9:00-17:00), evening (17:00-20:00), and nighttime (20:00-6:00 the next day), corresponding to the morning peak, daytime peak, evening peak, and nighttime periods, respectively. As shown in Table 1, the movement characteristics of each individual differ across time periods, and the behaviors of symptomatic and asymptomatic individuals also vary. Here, it is assumed that in the absence of an epidemic, each individual will decide whether to undergo spatial transformation (e.g., from home to the community, or from the community to other areas, or vice versa) at different times of the day. Movement probabilities are adjusted based on relevant research data and daily experience. During the morning peak (morning), the probability of individuals leaving home and the community is relatively high, while the probability of returning to the community and home is relatively low; the opposite is true during the evening peak (evening). At night, the probability of individuals returning to the community and home is higher, while the probability of leaving home and the community is lower. When individuals do not move between locations, they either remain stationary at home or engage in random movement within their existing space. For symptomatic individuals, the probability of leaving home or community for a destination is relatively lower than that of asymptomatic individuals at any given time of day, while the probability of returning to the community from the destination or vice versa is relatively higher. Based on these parameters, symptomatic individuals will tend to reduce their outdoor activities, thereby mitigating the spread of infectious diseases.

[0062] Table 1

[0063]

[0064] III. Infection Parameters

[0065] Assume that susceptible individuals will be infected after contact with infected individuals (including symptomatic and asymptomatic individuals), and that the probability of infection differs between open-air and enclosed areas (such as subways). Furthermore, the probability of transitioning between different health states and the probability of testing positive for different health states are fixed, as shown in Table 2.

[0066] Table 2

[0067]

[0068]

[0069] IV. Setting of Statistical Indicators

[0070] In order to summarize the benefits and costs of infectious diseases and reflect the effectiveness of different prevention and control policies, this invention calculates the following statistical indicators.

[0071] (1) Duration: refers to the number of days from the first exposed individual to the recovery of the last infected individual.

[0072] (2) Size of infection: It is the total number of individuals who have been infected during the simulation process, that is, the total number of individuals who have transformed into asymptomatic infected subjects (A) or infected subjects (I).

[0073] (3) Total number of tests: This is the total number of infection tests the subject undergoes during the simulation. The more infection tests, the higher the labor and laboratory costs.

[0074] (4) Maximum isolation: This is the maximum number of entities isolated during the simulation. The larger the number, the greater the negative impact of the lockdown measures on the economy.

[0075] (5) Lost Numbers (LostN): The percentage change in the number of people leaving their residential community for other activities under current measures, compared to normal circumstances, over the same time period. The larger the number, the greater the potential side effects on the economy.

[0076] (6) Total Cost: The total cost takes into account the labor losses caused by large-scale testing, isolation, and lockdown strategies during the simulation. It is assumed that each test results in a loss of 1 hour of labor. The calculation is as follows:

[0077] Total cost=Test / 24+Isolation+(-LostN)×800

[0078] (7) Total cost ratio: The total cost ratio measures the percentage of labor loss relative to the total population.

[0079] Total cost rate=Total cost / 800

[0080] Duration and scale of infection measure the size of an infectious disease (the smaller the scale, the greater the benefits of infectious disease control policies), while total number of tests, maximum number of isolations, and number of people lost due to relocation measure the costs of infectious disease control policies.

[0081] V. Simulation Results

[0082] The baseline results of a multi-agent simulation system for community transmission of infectious diseases, including asymptomatic carriers, do not consider any infectious disease control policies. The results are as follows: Figure 5 As shown in the figure, the baseline results show an exponential increase in the number of infected individuals, followed by a disappearance among exposed, asymptomatic, and infected individuals. The number of recovered individuals increased exponentially, followed by a roughly constant long-term level, before the outbreak stopped spreading on day 108, with 1015 out of 1600 people eventually becoming infected.

[0083] In addition to the baseline results, the system simulated the benefits and costs of different types of infectious disease control policies. Specifically, the time intervals for large-scale infection testing were set to 7, 5, and 3 days, and the thresholds for triggering community lockdown measures were set to 10, 7, 5, and 3 people, respectively, resulting in 15 simulations. Furthermore, scenarios with both isolation and mandatory testing measures were compared with corresponding scenarios with only testing measures and no mandatory isolation measures, evaluating the cost of the strategies from different perspectives. The results are shown in Table 3.

[0084] Table 3

[0085]

[0086] Another embodiment of the present invention provides a multi-agent simulation system for community transmission of infectious diseases including asymptomatic carriers, comprising:

[0087] The virtual community group creation module is used to create virtual community groups, which contain several communities and several destinations. It uses health status and spatial status to distinguish the various subjects in the virtual community group, including susceptible subject S, exposed subject E, asymptomatic infected subject A, infected subject I, and recovered subject R.

[0088] The main motion simulation module is used to simulate the daily travel behavior of each individual. It allows different individuals to set their travel preferences to various modes such as walking, public transportation, and private car travel, and to set behaviors such as waiting at stations and taking detours, so as to better simulate the individual's motion characteristics.

[0089] The infectious disease transmission process simulation module is used to establish the movement sequence of each subject in a virtual community group and simulate the infectious disease transmission process according to the movement sequence. The movement sequence includes movement, transmission and status update, infection detection and isolation, and community lockdown.

[0090] The simulation results observation and policy evaluation module is used to obtain simulation results and evaluate the effectiveness of infectious disease prevention and control policies.

[0091] The above division of modules is merely illustrative. In practical applications, the functions described above can be assigned to different functional modules as needed to complete all or part of the functions described in the aforementioned method. The specific working process of each module can be found in the corresponding process in the aforementioned method embodiments, and will not be repeated here.

[0092] Another embodiment of the present invention provides a computer device (computer, server, smartphone, etc.) including a memory and a processor, the memory storing a computer program configured to be executed by the processor, the computer program including instructions for performing the steps of the method of the present invention.

[0093] Another embodiment of the present invention provides a computer-readable storage medium (such as ROM / RAM, disk, optical disk) storing a computer program that, when executed by a computer, implements the various steps of the method of the present invention.

[0094] The specific embodiments of the present invention disclosed above are intended to help understand the content of the present invention and to implement it accordingly. Those skilled in the art will understand that various substitutions, changes, and modifications are possible without departing from the spirit and scope of the present invention. The present invention should not be limited to the content disclosed in the embodiments of this specification; the scope of protection of the present invention is defined by the claims.

Claims

1. A multi-agent simulation method for community transmission of infectious diseases including asymptomatic carriers, characterized in that, Includes the following steps: Establish a virtual community group, which includes several communities and several destinations; The various subjects in the virtual community group are distinguished by their health status and spatial status, including susceptible subjects S, exposed subjects E, asymptomatic infected subjects A, infected subjects I, and recovered subjects R; Establish movement sequences for various entities in a virtual community group, and simulate the spread of infectious diseases according to the movement sequences. The movement sequences include movement, transmission and status updates, infection detection and isolation, and community lockdown. Obtain simulation results and evaluate the effectiveness of infectious disease prevention and control policies.

2. The method according to claim 1, characterized in that, The movement includes four types of movement behaviors: (1) moving from the community to the destination; (2) moving from the destination back to the community; (3) moving from home to another community, or moving from another community back home; (4) moving freely within the current community or the destination; each type of movement behavior has a corresponding probability of occurrence.

3. The method according to claim 1, characterized in that, The infection and status update include: during movement, if two people move into the same grid at the same time, their health status is checked; if a susceptible subject comes into contact with an asymptomatic infected subject or an infected subject, the susceptible subject may become infected with the virus, and their health status may change after the incubation period; other changes in health status change over time with a given probability.

4. The method according to claim 1, characterized in that, The infection detection and isolation include: An additional state P is introduced to simulate the detection of health status, where state P represents a positive test. It is assumed that only pathogens from exposed subject E, asymptomatic infected subject A, or infected subject I can be identified as positive by the test. For large-scale testing, each person is tested at a predefined interval; For individual testing, testing is conducted spontaneously at random times, and once a test is positive, it is assumed that the individual is isolated in a separate area.

5. The method according to claim 1, characterized in that, The community lockdown includes overall lockdown and household lockdown. For overall lockdown, if the number of positive individuals in a community reaches a predefined threshold, the community is closed, and people in the community are not allowed to leave, but can only move within the community. For household lockdown, when symptoms related to the epidemic appear, the possibility of an individual leaving home is reduced to zero, and self-lockdown is carried out in this way.

6. The method according to claim 1, characterized in that, The statistical indicators used to assess the effectiveness of infectious disease control policies include: duration, scale of infection, total number of tests, maximum number of isolations, and number of people lost due to relocation. Among these, duration and scale of infection measure the size of the infectious disease; the smaller the size, the greater the benefits of the infectious disease control policy. Total number of tests, maximum number of isolations, and number of people lost due to relocation measure the costs of the infectious disease control policy.

7. The method according to claim 6, characterized in that, The total cost of infectious disease control policies is calculated using the following formula. Total cost and total cost rate: Total cost=Test / 24+Isolation+(-LostN)×800 Total cost rate=Total cost / 800 Where Test represents the total number of tests, Isolation represents the maximum number of isolations, and LostN represents the number of people lost due to migration.

8. A multi-agent simulation system for community transmission of infectious diseases including asymptomatic carriers, characterized in that, include: The virtual community group creation module is used to create virtual community groups, which contain several communities and several destinations. It uses health status and spatial status to distinguish the various subjects in the virtual community group, including susceptible subject S, exposed subject E, asymptomatic infected subject A, infected subject I, and recovered subject R. The main motion simulation module is used to simulate the daily travel behavior of each individual; The infectious disease transmission process simulation module is used to establish the movement sequence of each subject in a virtual community group and simulate the infectious disease transmission process according to the movement sequence. The movement sequence includes movement, transmission and status update, infection detection and isolation, and community lockdown. The simulation results observation and policy evaluation module is used to obtain simulation results and evaluate the effectiveness of infectious disease prevention and control policies.

9. A computer device, characterized in that, It includes a memory and a processor, the memory storing a computer program configured to be executed by the processor, the computer program including instructions for performing the method of any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, which, when executed by a computer, implements the method according to any one of claims 1 to 7.

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