Method and apparatus for constructing multi-level population contact network

Abstract

The embodiment of the present disclosure discloses a multi-level crowd contact network construction method and device. The method comprises: obtaining an actual crowd size and a target crowd size of a target city, and constructing a virtual city space model based on the actual crowd size and the target crowd size; distributing and configuring individuals with different attributes in the virtual city space model based on the population structure of the target city and the target crowd size; obtaining the relative contact coefficients between various crowds in various social activity scenarios based on the attributes of the individuals and the distribution positions of the individuals in the virtual city space model; and constructing a multi-level crowd contact network based on the relative contact coefficients between various crowds in various social activity scenarios. The method can greatly improve the precision and credibility of crowd contact behavior simulation in scenarios such as infectious disease transmission by constructing a multi-level crowd contact network that conforms to the population and space laws of a real city.

Description

Technical Field

[0001] This disclosure relates to the technical fields of information technology and public health, and in particular to a method and apparatus for constructing a multi-level population contact network. Background Technology

[0002] With the accelerating pace of urbanization, high population density, frequent social activities, and well-developed transportation networks, respiratory and other infectious diseases in cities are characterized by rapid spread, complex transmission routes, and significant spatial heterogeneity. Particularly in large and medium-sized cities, the contact structure among people of different ages, occupations, and activity scenarios varies significantly, resulting in a highly heterogeneous transmission process for infectious diseases.

[0003] Most current infectious disease transmission simulation studies still employ population-based dynamic models (such as SEIR and its extended models). While these models can reflect the overall trend of the epidemic by averaging the population, they struggle to depict individual differences and real-world transmission behaviors in different social scenarios. When applied at the provincial, municipal, or larger scales, these models typically require significant simplification of population behavior, spatial structure, and contact processes, reducing their ability to support refined epidemic prevention and control decisions. Summary of the Invention

[0004] In view of this, the present disclosure provides a method and apparatus for constructing a multi-level crowd contact network, which can significantly improve the accuracy and reliability of crowd contact behavior simulation in scenarios such as the spread of infectious diseases by constructing a multi-level crowd contact network that conforms to the real urban population and spatial patterns.

[0005] In a first aspect, embodiments of this disclosure provide a method for constructing a multi-level crowd contact network, employing the following technical solution: Obtain the actual population size and target population size of the target city, and construct a virtual city spatial model based on the actual population size and the target population size; Based on the population structure of the target city and the size of the target population, individuals with different attributes are distributed and configured within the virtual city spatial model; Based on individual attributes and their distribution location within the virtual city space model, the relative contact coefficients between various groups of people in various social activity scenarios are obtained. Based on the relative contact coefficients between different groups of people in various social activity scenarios, a multi-level group contact network is constructed.

[0006] Optionally, constructing a virtual city spatial model based on the actual population size and the target population size includes: Based on the ratio of the target population size to the actual population size, the geographic space of the target city is scaled proportionally to construct a virtual city grid space; The virtual city grid space is divided into multiple living areas; Based on the size of the target population, determine the number of institutions and households; Families and institutions are allocated to each living area according to the capacity quota of each living area; Within each living area, families and institutions are randomly placed at coordinate points within that living area to form a virtual city space model.

[0007] Optionally, the attributes include age, gender, and occupation, and the distribution location of individuals within the virtual city spatial model includes family location and occupational location; the distribution configuration of individuals with different attributes within the virtual city spatial model based on the population structure of the target city and the size of the target population includes: Based on the family structure, set the age and gender of individuals within the family; Based on an individual's age and the occupational distribution characteristics of the target city, the individual's occupation is set; Based on the number of each type of institution and the average number of people of each occupation within the institution, the number of people of each occupation allowed to be accommodated in each institution is randomly determined in a normal distribution. Based on an individual's occupation, determine whether the individual has an applicable institution category, wherein the institution category includes regular work institutions and residential institutions; When an individual has an applicable institution category, the individual matched to the institution category is assigned to a suitable institution based on the number of people of various occupations that each institution under the institution category is allowed to accommodate; Based on whether an individual has an applicable institution category and the institution category to which they belong, their family location and occupational location are determined.

[0008] Optionally, determining an individual's family location and occupational location based on whether the individual has an applicable institution category and the institution category to which they belong includes: When no applicable institution category is available for an individual, the family coordinates are used as the individual's family location and occupational location. When the applicable institution category for an individual is a regular work institution, the family coordinates are used as the individual's family location, and the institution coordinates are used as the individual's occupational location. When an individual is assigned to a residential institution, the institution's coordinates are used as both the individual's home location and occupational location.

[0009] Optionally, obtaining the relative contact coefficients between various groups of people in various social activity scenarios based on individual attributes and their distribution locations within the virtual city spatial model includes: Based on an individual's occupation and location, we determine the various social activity scenarios in which the individual is situated, and classify the corresponding groups of people in each social activity scenario. Set total contact coefficients for different groups of people in various social activity scenarios; Based on the occupations and sizes of various groups of people in various social activity scenarios, obtain the corresponding average total; Based on the total contact coefficient and the average total number, the individual contact coefficients of various groups of people in various social activity scenarios are obtained; Using the total contact coefficient in the family setting as the benchmark, the ratio between the individual contact coefficients of various groups of people in various social activity scenarios and the benchmark is used as the corresponding relative contact coefficient.

[0010] Optionally, the total contact coefficient is set for various groups of people in various social activity scenarios, including: Identify all contact channels between various groups of people in various social activity scenarios, and obtain the frequency of occurrence, duration of contact, size of the contacted group, and probability of transmission for each contact channel per unit time. Based on the occurrence frequency, contact duration, contact population size, and propagation probability, obtain the channel contact coefficient for each contact channel among various groups of people in various social activity scenarios; The total contact coefficient of all contact channels among various groups of people in the same social activity scenario is obtained by summing up the contact coefficients of all contact channels among various groups of people in various social activity scenarios.

[0011] Optionally, the total contact coefficient is set for various groups of people in various social activity scenarios, including: Set the propagation probability of a home scenario within a unit of time and use it as a reference value; Identify all contact channels between various groups of people in various social activity scenarios, and obtain the frequency of occurrence, the size of the contacted group, and the probability of transmission per contact for each contact channel per unit time. Based on the occurrence frequency, the size of the contacted population, the single contact transmission probability, and the reference value, the channel contact coefficient of each contact channel between various groups of people in various social activity scenarios is obtained; The total contact coefficient of all contact channels among various groups of people in the same social activity scenario is obtained by summing up the contact coefficients of all contact channels among various groups of people in various social activity scenarios.

[0012] Optionally, obtaining the corresponding average total based on the occupations and sizes of various groups of people in various social activity scenarios includes: When the social activity scenario is a family scenario or a commuting scenario, and the types of people in the social activity scenario are the same, the average total number of people of the same type is set to 1. When the social activity scenario is a commercial service institution scenario, the population is divided into service recipients and operating personnel. The total shared amount among service recipients is set as the product of the number of service recipients within the service coverage area of ​​the commercial service institution, the square of the total number of similar institutions within the service coverage area, and the number of operating personnel. The total shared amount among operating personnel is set as the number of operating personnel minus 1. The total shared amount between service recipients and operating personnel is set as the product of the total number of similar institutions within the service coverage area and the number of operating personnel. When the social activity scenario is a production and operation institution scenario, the average total number of people of the same type is set to the size of the same type of people minus 1, and the average total number of people of different types who come into contact with each other is set to the size of the people who come into contact with each other. When the social activity scenario is a delivery or taxi scenario, the population is divided into service recipients and operators. The total number shared between service recipients and operators is set as the number of service recipients within the service coverage area of ​​the operators minus the number of operators. When the social activity scenario is an activity scenario or a gathering scenario, and the types of people in the social activity scenario are the same, the average total number of people of the same type is set to the total population size in the activity area or gathering area minus 1.

[0013] Optionally, the construction of a multi-level population contact network based on the relative contact coefficients between various groups of people in various social activity scenarios includes: Based on the relative contact coefficients between various groups of people in various social activity scenarios, sub-contact matrices for various social activity scenarios are constructed. Based on the power-law growth relationship between the contact intensity and population size in various social activity scenarios, the baseline correction coefficients for various social activity scenarios are obtained. Based on the actual population size, the target population size, and the baseline correction coefficient, obtain the scale correction coefficients for various social activity scenarios; Each sub-contact matrix is ​​calibrated based on the aforementioned scale correction coefficient, and all calibrated sub-contact matrices are merged to obtain a multi-level population contact network.

[0014] Secondly, this disclosure also provides a multi-level crowd contact network construction system, which adopts the following technical solution: The model building module is used to obtain the actual population size and target population size of the target city, and to build a virtual city spatial model based on the actual population size and the target population size. The individual distribution module is used to distribute individuals with different attributes within the virtual city spatial model based on the population structure of the target city and the size of the target population. The coefficient acquisition module is used to obtain the relative contact coefficients between various groups of people in various social activity scenarios based on the individual's attributes and the individual's distribution location within the virtual city space model. The network construction module is used to build a multi-level population contact network based on the relative contact coefficients between various groups of people in various social activity scenarios.

[0015] Thirdly, this disclosure also provides a computer device, which adopts the following technical solution: The computer device includes: At least one processor; and, A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, which enables the at least one processor to perform any of the multi-level population contact network construction methods described above.

[0016] Fourthly, embodiments of this disclosure also provide a computer-readable storage medium storing computer instructions for causing a computer to execute any of the multi-level crowd contact network construction methods described above.

[0017] Fifthly, embodiments of this disclosure also provide a computer program product, including a computer program / instructions that, when executed by a processor, implement the steps of any of the methods described above.

[0018] The multi-level population contact network construction method provided in this disclosure constructs a virtual city network based on the actual population size and a controllable target population size as a variable. This achieves a precise correspondence with the real city population size, solving the technical problem of existing simulation models being out of sync with the real city population size, leading to distorted contact intensity. Since the simulated population of the model can strictly map to the real city population proportionally, the population distribution in various social activity scenarios has geometric similarity to the real urban spatial structure, providing a simulation basis that conforms to real geographical spatial laws for subsequent contact coefficient calculations, ensuring the spatial authenticity of the contact network construction. Based on the precise configuration of the distribution of individuals with different attributes according to the population structure, and combined with individual attributes and spatial location to determine the relative contact coefficients in each scenario, a multi-level contact network is finally constructed. Compared to single-dimensional contact networks, this method can completely reproduce contact relationships in multiple scenarios such as families, institutions, and activities, achieving full-scenario coverage, attribute association, and spatial refinement in contact network construction. This significantly improves the simulation accuracy of the contact network for real population transmission behavior, providing highly reliable network data support for fields such as infectious disease prevention and control and urban planning.

[0019] The above description is merely an overview of the technical solution disclosed herein. In order to better understand the technical means of this disclosure and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this disclosure more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0020] To more clearly illustrate the technical solutions of the embodiments of this disclosure, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this disclosure. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 A flowchart illustrating the method for constructing a multi-level crowd contact network provided in this embodiment of the disclosure; Figure 2 A flowchart illustrating the virtual city spatial model construction method provided in this embodiment of the disclosure; Figure 3 A flowchart illustrating the individual distribution configuration method provided in this embodiment of the disclosure; Figure 4 A flowchart illustrating the method for obtaining the relative contact coefficient provided in this embodiment of the disclosure; Figure 5 A flowchart illustrating the method for obtaining the total contact coefficient provided in this embodiment of the disclosure; Figure 6 Another schematic flowchart of the method for obtaining the total contact coefficient provided in this embodiment of the disclosure; Figure 7 This is a schematic flowchart of a method for constructing a multi-level crowd contact network based on relative contact coefficients, provided in an embodiment of this disclosure. Figure 8 A schematic diagram of the commuting locations of two individuals provided in an embodiment of this disclosure; Figure 9 A schematic diagram of the multi-level crowd contact network construction system provided in the embodiments of this disclosure; Figure 10 This is a schematic diagram of the structure of a computer device provided in an embodiment of the present disclosure. Detailed Implementation

[0022] The embodiments of this disclosure will now be described in detail with reference to the accompanying drawings.

[0023] It should be understood that the following specific examples illustrate the implementation of this disclosure, and those skilled in the art can easily understand other advantages and effects of this disclosure from the content disclosed in this specification. Obviously, the described embodiments are only a part of the embodiments of this disclosure, and not all of them. This disclosure can also be implemented or applied through other different specific implementation methods, and the details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of this disclosure. It should be noted that, in the absence of conflict, the following embodiments and features in the embodiments can be combined with each other. Based on the embodiments in this disclosure, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this disclosure.

[0024] It should be noted that various aspects of embodiments within the scope of the appended claims are described below. It will be apparent that the aspects described herein can be embodied in a wide variety of forms, and any particular structure and / or function described herein is merely illustrative. Based on this disclosure, those skilled in the art will understand that one aspect described herein can be implemented independently of any other aspect, and two or more of these aspects can be combined in various ways. For example, any number of aspects set forth herein can be used to implement the device and / or practice the method. Additionally, this device and / or method can be implemented using structures and / or functionalities other than one or more of the aspects set forth herein.

[0025] It should also be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of this disclosure. The drawings only show the components related to this disclosure and are not drawn according to the number, shape and size of the components in actual implementation. In actual implementation, the form, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.

[0026] Furthermore, specific details are provided in the following description to facilitate a thorough understanding of the examples. However, those skilled in the art will understand that the described aspects can be practiced without these specific details.

[0027] Reference Figure 1 This disclosure provides a method for constructing a multi-level population contact network, including the following steps: S1: Obtain the actual population size and target population size of the target city, and construct a virtual city spatial model based on the actual population size and target population size; S2: Based on the population structure and target population size of the target city, distribute and configure individuals with different attributes within the virtual city spatial model; S3: Based on individual attributes and their distribution location within the virtual city space model, obtain the relative contact coefficients between various groups of people in various social activity scenarios; S4: Construct a multi-level population contact network based on the relative contact coefficients between various groups of people in various social activity scenarios.

[0028] The multi-level population contact network construction method disclosed herein constructs a virtual city network based on the actual population size and a controllable target population size as a variable. This achieves a precise correspondence with the real city population size, solving the technical problem of existing simulation models' population size being out of sync with the real city, leading to distortion of contact intensity. Because the simulated population of the model can strictly map to the real city population on a proportional basis, the population distribution in various social activity scenarios has geometric similarity to the real urban spatial structure, providing a simulation basis that conforms to the real geographical spatial laws for subsequent contact coefficient calculations, ensuring the spatial authenticity of the contact network construction.

[0029] By precisely configuring the distribution of individuals with different attributes based on population structure, and then combining individual attributes with spatial location to determine the relative contact coefficients in each scenario, a multi-level contact network is finally constructed. Compared with a single-dimensional contact network, this method can fully reproduce the contact relationships in multiple scenarios such as families, institutions, and activities, and achieve the construction of a contact network with full scenario coverage, attribute association, and spatial refinement. This significantly improves the simulation accuracy of the contact network for the transmission behavior of real populations, and provides highly reliable network data support for fields such as infectious disease prevention and control and urban planning.

[0030] In S1, refer to Figure 2 The flowchart illustrating the virtual city spatial model construction method shows that "constructing a virtual city spatial model based on the actual population size and the target population size" includes the following steps: S11: Based on the ratio of the target population size to the actual population size, the geographic space of the target city is scaled proportionally to construct a virtual city grid space; S12: Divide the virtual city grid space into multiple living areas; S13: Set the number of institutions and households according to the size of the target population; S14: Allocate families and institutions to each living area according to the capacity quota of each living area; S15: Within each living area, families and institutions are randomly placed at coordinate points within the living area to form a virtual city space model.

[0031] In S11, the target population size can be set based on the actual population size of the target city, computational resource conditions, or simulation requirements. The ratio of the target population size to the actual population size is used as the city scaling factor. Based on this, the actual geographical area of ​​the target city is scaled proportionally to obtain the theoretical area of ​​the virtual city. Combining the length-to-width ratio of the target city with the preset virtual space grid side length, the theoretical area is divided by the area of ​​a single grid (the square of the grid side length) to calculate the theoretical total number of grids in the virtual city. Then, using the theoretical total number of grids as a constraint, all positive integer values ​​for the vertical grid number are iterated. For each candidate vertical grid number, the corresponding horizontal grid number is calculated according to the length-to-width ratio of the target city and rounded to obtain the corresponding actual total number of grids. The vertical grid number with the smallest deviation from the theoretical total number of grids is selected as the actual vertical grid number. Based on this, the actual horizontal grid number and the actual total number of grids are derived. Finally, a virtual city grid space that meets the length-to-width ratio requirements and has a positive integer number of grids is generated, and each grid is numbered to complete the space construction.

[0032] In S12, after the virtual city grid space is constructed, the entire virtual city grid space is divided into equal-scale regions based on the preset grid side length d. This divides the continuous grid space into multiple non-overlapping living areas. These living areas can be divided according to the actual administrative districts (administrative districts / streets), functional zones (residential areas, commercial areas, science and education areas, medical areas, etc.), population density zones, or land use plans of the target city. The area of ​​each area can vary, closely resembling the spatial structure of a real city, or they can be randomly divided. This division method, when the virtual city space is large, can constrain the subsequent layout of families and institutions through regional quotas, preventing randomly generated families and institutions from becoming overly concentrated in a particular area. This ensures the rationality and realism of the virtual city's spatial distribution, providing a spatial basis for the subsequent stratified allocation of families and institutions. Furthermore, using the actual population size as a constraint provides a unified population capacity benchmark for the subsequent generation of family structures and the configuration of social units.

[0033] In S13, construct a table showing the percentage of institutions by type and the percentage of family structures by type in the target city. The table showing the percentage of the population in each type of institution (i.e., the ratio of the number of each type of institution to the total population of the target city) includes the percentage of the population in each type of institution in the target city, such as kindergartens, primary schools, junior high schools, senior high schools, universities, hospitals, nursing homes, shops, and others. The table showing the percentage of the population in each type of family includes the probability of the generation of families with different generational structures in the target city, including the number of first-generation, second-generation, and third-generation members in each type of family and their corresponding generation probability. For example, for the first type of family, the number of first-generation members is 0, the number of second-generation members is 1, and the number of third-generation members is 0, and the generation probability of this type of family is 24.55%.

[0034] Using the target population size as a base, the number of institutions of each type generated in the virtual city is calculated by multiplying each type by the percentage parameter of the corresponding institution type in the institution type percentage table, ensuring that the institution size matches the target population size proportionally. With the target population size as a constraint, the corresponding number of families are generated according to the generation probability of each family structure in the family structure percentage table, and the population is allocated to each family according to the number of family members until the total number of family members meets the target population size requirement, thus completing the setting of the number and structure of families.

[0035] In S14, based on parameters such as the spatial area and population carrying capacity of each living area, corresponding family and institution capacity quotas are set for each living area. All existing families and institutions are allocated to the corresponding living areas according to the quota ratio of each living area to ensure that the number of families and institutions in each living area is consistent with the quota requirements and to avoid excessive clustering in local areas.

[0036] In S15, for each assigned household and institution's living area, an unoccupied coordinate point is randomly selected from all grid coordinate points within that area as its placement location for each household and institution. After completing the coordinate placement of all households and institutions, a complete virtual urban spatial model containing spatial grids, households, institutions, and population distribution is constructed, providing a basic carrier for the subsequent construction of population contact networks. Moreover, this spatial structure modeling method makes the process of disease transmission using population contact networks not only dependent on population size but also related to the individual's location and its spatial proximity, thereby improving the spatial realism of the transmission simulation.

[0037] In S2, an individual's attributes include age, gender, and occupation. An individual's location within the virtual city spatial model includes their home location and occupational location. (See reference...) Figure 3 The flowchart illustrating the individual distribution configuration method shows that "based on the population structure and target population size of the target city, the distribution configuration of individuals with different attributes within the virtual city spatial model" includes the following steps: S21: Set the age and gender of individuals within the family according to the family structure; S22: Set the individual's occupation based on the individual's age and the occupational distribution characteristics of the target city; S23: Based on the number of each type of institution and the average number of people of each occupation within the institution, randomly select the number of people of each occupation that each institution is allowed to accommodate in a normal distribution; S24: Based on an individual's occupation, determine whether the individual has an applicable institution category, where institution categories include regular work institutions and residential institutions; S25: When an individual has an applicable institution category, the individual matched to the institution category will be assigned to an appropriate institution based on the number of various occupations that each institution under the institution category is allowed to accommodate; S26: Determine an individual’s family and occupational positions based on whether the individual has an applicable institution category and the institution category to which they belong.

[0038] In S21, family members of the third, second and first generations are constructed in sequence according to the composition structure of each family. The age range of the third generation is 0 to 17 years old, the age range of the second generation is 18 to 64 years old, and the age range of the first generation is 65 to 100 years old. The generation probability of the age and gender of all members is executed with reference to the preset age-gender distribution table. This table contains the gender ratio at different ages and is used to constrain and standardize the random generation rules of the age and gender of each generation of family members.

[0039] For third-generation family members, within their corresponding age range, the age and gender of each member are generated completely randomly based on the relative proportion of gender in the age-gender distribution table. If there are multiple third-generation members in the same family, the generation process of each member's age and gender is independent and unconstrained by each other.

[0040] When configuring second-generation family members, it is determined whether there are third-generation members within the family. If there are third-generation members, in addition to meeting their own basic age range, the age of the second-generation members must also fall within the range of any third-generation member's age plus 23 to 46 years. Differential configuration is applied based on the number of second-generation members. When there is only one second-generation member, within the compliant age range, age and gender are randomly generated according to the corresponding ratio. When there are two second-generation members, the default combination is one male and one female. The female member is generated first, and her age is randomly determined. Then, the male member's age is constrained to be within the range of the female member's age minus 3 to plus 5 years, while also meeting the basic age requirements, and the age configuration is completed randomly according to the ratio.

[0041] When configuring first-generation family members, the criteria for determining whether a family is composed of two families are used, based on the requirement that there are at least three first-generation members. If it is a combined family with four members in the first generation, two first-generation members are generated for each family, using two second-generation members as references, following the generation rules for offspring. If it is a combined family with three members in the first generation, one first-generation member is randomly assigned to a single second-generation member, and the remaining two first-generation members are assigned to the other second-generation member, then configured according to the established generation rules. If it is not a combined family, the corresponding rules are applied based on the number of second-generation members. When there is only one second-generation member, two first-generation members are configured according to the general generation method. When there are two second-generation members, the affiliation is randomly assigned, and one of the second-generation members is selected to generate two corresponding first-generation members. If there are no second-generation members, the age of the first generation must be simultaneously constrained to the range of 46 to 92 years older than any third-generation member's age. After meeting the basic age range requirements, the age and gender of the first-generation members are configured according to the unified rules, based on the third-generation members.

[0042] In S22, an age-occupation table is pre-set based on the occupational distribution characteristics of the target city. This table contains the probability of individuals of different ages generating each occupation. For example, for individuals aged 0 and 1, the occupation is 100% home-based. The individual's age is matched with this table to generate the individual's occupation.

[0043] In S23, a pre-defined occupation-institution mapping table is used. This table establishes a mapping relationship between various occupations and institution categories, clearly defining the institution type (e.g., kindergarten, primary school) to which different occupational groups (e.g., kindergarten students, primary school students) belong. When assigning individuals to institutions, the table categorizes individuals by occupation into corresponding institution categories. Then, combining the total number of institutions in each category and the total number of people in the corresponding occupation, a normal distribution with a mean of the average capacity of a single institution in that category and a standard deviation of 10% of the mean is used to randomly generate the number of people in each occupation that a specific institution can accommodate. This achieves a differentiated and statistically consistent allocation of individuals among institutions. The formula for randomly assigning the number of people in each occupation that each institution can accommodate using a normal distribution is as follows: In the formula, Indicates the first Among the various types of institutions, the first The number of people of the corresponding occupation type that each institution is allowed to accommodate; Indicates the first The number of institutions in each type of institution ; Indicates the first The average number of people employed in each occupation within each type of institution; Indicated by The average is denoted by . It is a normal distribution with standard deviation.

[0044] In S24, institutions are divided into two categories based on their core functions (social activities / housing security): regular work institutions and residential institutions. Regular work institutions are physical institutions that provide daily work, education, and other social activity scenarios for people, such as kindergartens, schools, hospitals, shops, and businesses. They are the core carriers for building work / education contact networks. Residential institutions are physical institutions that provide long-term housing and care services for specific groups, such as nursing homes. They are the core carriers for building contact networks for people living within these institutions. When an individual's occupation is a delivery driver, taxi driver, or other work / education type without a fixed workplace, they are deemed not to qualify for the regular work institution category. When an individual's occupation is a kindergarten student, primary school student, company employee, medical staff, or other occupation with a fixed workplace / education location, they are deemed to qualify for the regular work institution category.

[0045] In S25, when an individual has a suitable institution category, based on the number of people allowed in each specific institution within that category for the corresponding occupation, a preset allocation mode is used to assign the individual matched to the institution category to a suitable specific institution. The allocation mode can be set to two types depending on the needs: one is the optimal allocation mode, which prioritizes assigning individuals to institutions of the corresponding category closest to their home, based on the individual's home location. If institution capacity exceeds the limit, the allocation is iteratively performed by randomly removing excess individuals and eliminating full institutions until all individuals are matched; the other is the random allocation mode, which does not consider spatial distance and only assigns individuals to any institution within the corresponding category based on individual identifier or random rules, until the institution capacity is full.

[0046] Taking the allocation of regular work / education institutions and hospital institutions as an example, for regular work / education institutions, the optimal allocation model assigns each occupational group to the corresponding category of institution closest to their home. When there is an oversubscription, individuals are removed and iterative matching is performed. In the random allocation model, individuals are randomly assigned to the corresponding category of institution according to their individual ID number until the quota is filled. For hospital institutions, the optimal allocation model assigns all individuals to the hospital closest to their home, while the random allocation model randomly assigns individuals to any hospital. The allocation logic for both types of institutions follows the above-mentioned higher-level rules, with differences only in the applicable objects and institution categories.

[0047] In S26, when an individual has no applicable institution category, the family coordinates are used as the individual's family location and occupational location, and the family location and occupational location are the same. When the applicable institution category is a regular work institution, the family coordinates are used as the individual's family location, and the institution coordinates are used as the individual's occupational location, and the family location and occupational location are not the same. When the applicable institution category is a residential institution, the institution coordinates are used as both the individual's family location and occupational location, and the family location and occupational location are also the same, but the specific location is essentially the coordinates of the nursing home.

[0048] Through the above methods, individuals are generated and organized based on family units, achieving a coordinated construction of family and social unit structures. This provides a stable and realistic foundation for the subsequent construction of multi-type contact networks. Each spatial unit can be set according to the city's area and spatial resolution, facilitating population distribution, unit layout, and the implementation of regional prevention and control measures. Furthermore, differentiated travel and activity characteristic parameters are set for individuals of different age groups, occupations, and residences / work locations, simplified into behavioral coordinates to describe their activity frequency and range over different time periods. This allows the virtual city spatial model to reflect the behavioral differences of different groups in the real city.

[0049] In S3, refer to Figure 4 The flowchart illustrating the method for obtaining relative contact coefficients shows that "based on individual attributes and their distribution within a virtual city spatial model, the method for obtaining relative contact coefficients between various groups of people in various social activity scenarios includes the following steps: S31: Based on an individual's occupation and location, determine the various social activity scenarios in which the individual is located, and classify the corresponding groups of people in each social activity scenario; S32: Set the total contact coefficient for different groups of people in various social activity scenarios; S33: Based on the occupations and scales of various groups of people in various social activity scenarios, obtain the corresponding average total; S34: Based on the total contact coefficient and the average total number, obtain the individual contact coefficients of various groups of people in various social activity scenarios; S35: Using the total contact coefficient in the family setting as the benchmark value, the ratio between the individual contact coefficients of various groups of people in various social activity scenarios and the benchmark value is used as the corresponding relative contact coefficient.

[0050] In S31, social activity scenarios refer to the spatial and behavioral scenarios corresponding to individuals' various daily activities in the virtual city space model, including types such as family, institution, activity, gathering, commuting, delivery, and taxi. Based on an individual's occupation and distribution location (including family location and occupational location), the various social activity scenarios in which the individual is located are determined. For example, individuals whose family location and occupational location are different are recorded as commuters, and they can be in commuting social activity scenarios. Then, corresponding populations are divided for different scenario types. Scenarios such as family, activity, and gathering usually contain only a single type of population, while scenarios such as institution, delivery, and taxi usually contain two or more types of population. For example, the hospital scenario contains two types of population: medical staff and patients, which can form three types of contact relationships: doctor-doctor, patient-doctor, and patient-patient. Finally, the population division of each social activity scenario is completed, providing a foundation for the subsequent construction of contact networks in different scenarios.

[0051] In S32, there are two methods for obtaining the total contact coefficient. In one specific implementation, refer to... Figure 5 The flowchart illustrating the method for obtaining the total contact coefficient shows that "setting the total contact coefficient for different groups of people in various social activity scenarios" includes the following steps: S321: Determine all contact channels between various groups of people in various social activity scenarios, and obtain the frequency of occurrence, duration of contact, size of the contacted group, and probability of transmission for each contact channel per unit time. S322: Based on the frequency of occurrence, duration of contact, size of the contacted population, and probability of transmission, obtain the channel contact coefficient for each contact channel among various groups of people in various social activity scenarios; S323: Add up the contact coefficients of all contact channels between different groups of people in the same social activity scenario to obtain the total contact coefficient of different groups of people in different social activity scenarios.

[0052] In sections S321-S323, based on epidemiological survey data, statistical patterns of population behavior, and infectious disease transmission characteristics of the target city, all contact channels between various groups of people in various social activity scenarios are identified. The frequency of occurrence, duration of contact, size of the contact population, and probability of transmission for each contact channel within a unit of time are obtained, or these data are output through a trained AI prediction model. For example, in a family setting, the contact channel is singular, including only contact between family members. For the contact channel between graduate students and their university staff, it could include contact within a class and contact during group meetings. Frequency of occurrence refers to the frequency of occurrence of the corresponding contact channel within a unit of time, reflecting the density of population contact; duration of contact refers to the length of time a single contact behavior lasts within a unit of time; size of the contact population refers to the size of the population participating in mutual contact within a unit of time in a single contact scenario; and probability of transmission refers to the probability that a pathogen will spread through this type of contact channel within a unit of time.

[0053] This method calculates the channel contact coefficient by multiplying the frequency, duration, size of the contact group, and probability of transmission of a single contact channel between similar or dissimilar groups in the current social activity scenario, and then dividing by the probability of transmission in a family setting. The method is calculated based on contact time and is used to handle situations where individuals come into contact with the same group of people over a period of time.

[0054] In another specific implementation, refer to Figure 6 Another flowchart illustrating the method for obtaining the total contact coefficient, which involves "setting the total contact coefficient for different groups of people in various social activity scenarios," includes the following steps: S324: Set the propagation probability of the home scene within a unit of time and use it as a reference value; S325: Identify all contact channels between various groups of people in various social activity scenarios, and obtain the frequency of occurrence, the size of the contacted group, and the probability of transmission per contact for each contact channel per unit time. S326: Based on the frequency of occurrence, the size of the contacted population, the probability of transmission in a single contact, and reference values, obtain the channel contact coefficient of each contact channel between various groups of people in various social activity scenarios; S327: Add up the contact coefficients of all contact channels between different groups of people in the same social activity scenario to obtain the total contact coefficient of different groups of people in different social activity scenarios.

[0055] In S324-S327, the single-contact transmission probability refers to the probability that a pathogen will spread when individuals have a single contact. The channel contact coefficient is obtained by multiplying the frequency of single-contact transmission between similar or different groups of people in the current social activity scenario, the size of the contact group, and the single-contact transmission probability, and then dividing by a reference value. This method is calculated based on contact frequency and is used to handle situations involving continuous short-term contact with different individuals.

[0056] In the two specific implementation methods mentioned above, a public transportation commuting parameter configuration table is preset. This table records core parameters such as the daily passenger volume of buses and subways in the target city, the time required to travel a unit distance (e.g., 1km), the probability of sharing a carriage, and the commuting working population. For commuting scenarios, the contact duration per unit time is obtained as follows: the daily contact duration for commuting is the reciprocal of the time required to travel a unit distance by bus / subway. The contact population size is calculated by dividing the daily bus / subway passenger volume by 2, then dividing by the commuting working population (i.e., the product of the proportion of individuals whose workplace is not their home and the total population of the target city), taking the square, and then multiplying by the probability of sharing a carriage. This quantifies the population contact characteristics in commuting scenarios, providing data support for subsequent contact coefficient calculations. The unit time is typically 24 hours.

[0057] A preset scenario contact parameter conversion benchmark table records the conversion benchmark between the contact population size in the target city and the benchmark city in social activity scenarios other than commuting. The conversion benchmark can be based on the density ratio (population density ratio or place density ratio) between the target city and the benchmark city, converting the scenario contact parameters obtained from the benchmark city survey into the contact population size in the target city, adapting to the differences in population contact characteristics in different cities.

[0058] In S33, institutions are divided into two categories based on their core functions and the attributes of their service recipients: commercial service institutions and production and operation institutions. Commercial service institutions are characterized by providing commercial services such as commodity transactions and catering to the public, and serve as the carriers for building consumer scenario contact networks, including restaurants and retail stores. Production and operation institutions are characterized by supporting people's daily work, schooling, medical treatment, and other social production and public service activities, and serve as the carriers for building work / school scenario contact networks, including schools, hospitals, and enterprises.

[0059] When the social activity scenario is a family scenario or a commuting scenario, the types of people in the social activity scenario are the same. The average total number of people of the same type is set to 1. This is because in the family scenario or commuting scenario, the contact between people does not need to distinguish between individual occupations. Therefore, they are all regarded as the same type of people, and the average total number of people of this type is set to the base value of 1.

[0060] When the social activity scenario is a commercial service institution scenario, the population is divided into service recipients and operational staff. The average total among service recipients is set as the product of the number of service recipients within the service coverage area of ​​the commercial service institution, the square of the total number of similar institutions within the service coverage area, and the number of operational staff. The average total among operational staff is set as the number of operational staff minus 1. The average total among service recipients and operational staff is set as the product of the total number of similar institutions within the service coverage area and the number of operational staff. For example, for store employees - employees, the average total is set as the number of employees in the catering retail store - 1; for store customers - employees, the average total is set as the number of employees in the catering retail store × the total number of catering retail stores within the store's coverage area; for store customers - customers, the average total is set as the number of individuals within the store's coverage area × the square of the total number of catering retail stores within the store's coverage area × the number of employees in the catering retail store. The service coverage area of ​​the commercial service institution is defined by a pre-defined area centered on the location of the operational staff's home and the location of the commercial service institution, jointly defining the radiation range (e.g., a square area) of the corresponding service recipients.

[0061] When the social activity scenario is a production and operation institution scenario, the average total number of people of the same type is set to the size of the same type of people minus 1, and the average total number of people of different types who come into contact is set to the size of the people who come into contact with each other. For example, for kindergarten students-kindergarten students, which is a contact between people of the same type, the average total number is set to the number of kindergarten students minus 1. However, for kindergarten students-kindergarten staff, which is a contact between people of different types, the average total number is set to the number of kindergarten staff.

[0062] When the social activity scenario is a delivery or rental scenario, the population is divided into service recipients and operators. The average total number of service recipients and operators is set as the number of service recipients within the operator's service coverage area minus the number of operators. The operator's service coverage area is a preset range area (such as a square area) centered on the operator's home location. When the social activity scenario is an event scenario or a gathering scenario, the population types in the social activity scenario are the same, and the average total number of people of the same type is set as the total population size in the event area or gathering area minus 1.

[0063] In S34, the ratio between the total contact coefficient of various groups of people in various social activity scenarios and the average total number is the corresponding individual contact coefficient.

[0064] In S35, the total contact coefficient in the family setting is used as the benchmark because the family, as the core setting with the most stable population composition and the most basic contact behavior, has objective and representative contact characteristics. Using this as a benchmark can achieve standardized quantification and horizontal comparability of relative contact coefficients in various social activity scenarios. All these calculated relative contact coefficients are recorded in the contact coefficient table to facilitate the subsequent construction of a multi-level population contact network.

[0065] In S4, refer to Figure 7 The illustrated flowchart demonstrates a method for constructing a multi-level population contact network based on relative contact coefficients. The process of "constructing a multi-level population contact network based on the relative contact coefficients between various groups of people in various social activity scenarios" includes the following steps: S41: Based on the relative contact coefficients between various groups of people in various social activity scenarios, construct sub-contact matrices for various social activity scenarios; S42: Based on the power-law growth relationship between the contact intensity and population size in various social activity scenarios, obtain the benchmark correction coefficients for various social activity scenarios; S43: Based on the actual population size, target population size, and baseline correction coefficient, obtain the scale correction coefficient for various social activity scenarios; S44: Based on the scale correction coefficient, each sub-contact matrix is ​​calibrated, and all calibrated sub-contact matrices are merged to obtain a multi-level population contact network.

[0066] In S41, production and operation institutions are further divided into medical service institutions and general social service institutions. Medical service institutions refer to professional medical institutions whose core functions are to provide medical diagnosis, treatment and health care services. As the core carrier of doctor-patient interaction, these institutions build a professional contact network between specific groups of people (medical staff and patients). General production and operation institutions refer to physical institutions other than medical service institutions, whose core functions are to support people's daily work, schooling and other production and operation activities. These include schools, enterprises, office buildings, etc., and are the core carriers for building contact networks of people in work / school scenarios.

[0067] Social activity scenarios include at least one of the following: family, general social service institutions, medical service institutions, activities, commercial service institutions, express delivery and food delivery, taxi, commuting, and gathering.

[0068] In the family scenario, the sub-contact matrix contains the relative contact coefficients of the corresponding families. The relative contact coefficients between individuals within the family are uniformly set to 1. This is because in the family scenario, the individual contact coefficient divided by the total contact coefficient is always 1. In this way, the sub-contact matrix of each family scenario is constructed. Since individuals belonging to the same family unit are considered to have stable and high-frequency contact relationships, this type of contact is continuous in time and mainly occurs in the living place in space.

[0069] For general social service institutions, the sub-contact matrix includes the relative contact coefficients of the corresponding social service institutions. Within the same general social service institution, between people of corresponding occupational combinations, the relative contact coefficients are assigned according to the "Occupation" item in the contact coefficient table. This method constructs a sub-contact matrix for each general social service institution. Since different types of units or schools can differ in member size, member composition, and contact intensity, the sub-contact matrix for general social service institutions can reflect the impact of different work or learning environments on the transmission process. Similarly, for medical service institutions, the sub-contact matrix includes the relative contact coefficients of the corresponding medical service institutions. Within the same medical service institution, between people of corresponding occupational combinations, the relative contact coefficients are assigned according to the "Hospital" item in the contact coefficient table. This describes the potential contact relationships between patients and medical staff, among medical staff, and among patients in the medical environment. These contacts typically have phased characteristics and are related to the individual's disease state.

[0070] For each activity scenario, the sub-contact matrix comprises the product of the relative contact coefficients between groups of people within a small area of ​​the activity and an adjustment parameter. These groups are geographically distributed; when their home locations are the same, the adjustment parameter is half the home-outing adjustment rate; when their occupational locations are the same, the adjustment parameter is half the work-outing adjustment rate. This sub-contact matrix describes the non-fixed contact relationships that individuals generate during their daily outdoor activities around their living or working locations. The contact frequencies can be set according to individual behavioral characteristics to reflect the differences in daily activity patterns among different groups.

[0071] For commercial service institutions, the service recipients within the service coverage area and the operational staff within each institution are recorded. The sub-contact matrix includes the product of the relative contact coefficients between service recipients and operational staff and the adjustment parameter, the product of the relative contact coefficients between service recipients and the adjustment parameter, and the relative contact coefficients between operational staff. Service recipients include surrounding permanent residents and surrounding transient populations. A first service area is defined centered on the home location of the operational staff within the commercial service institution. Individuals within this first service area are considered surrounding permanent residents. The adjustment parameter between surrounding permanent residents and operational staff is the home-based adjustment rate of surrounding permanent residents; the adjustment parameter between surrounding permanent residents is half the product of their home-based adjustment rates. A second service area is defined centered on the location of the commercial service institution itself. Individuals within this first service area are considered surrounding transient populations. The adjustment parameter between surrounding transient populations and operational staff is the work-based adjustment rate of surrounding transient populations; the adjustment parameter between surrounding transient populations is half the product of their work-based adjustment rates. By iterating through the operational staff, all service recipients with whom there are contact relationships can be systematically filtered out, and then values ​​can be assigned to each item in the sub-contact matrix, which starts with a value of 0. This sub-contact matrix for commercial service establishments can describe the contact relationships that individuals have with other customers or service personnel when engaging in dining or retail shopping activities around their living or working locations. These contact relationships can be configured with different contact frequencies based on the individual's consumption behavior characteristics.

[0072] For the express delivery and food delivery scenario, couriers and food delivery workers are considered as operational personnel, and customers who interact with them are considered as service recipients. The sub-contact matrix contains the product of the relative contact coefficient between service recipients and operational personnel in the express delivery and food delivery scenario and an adjustment parameter. When the service recipient's home location is within the service coverage area of ​​the operational personnel, the adjustment parameter between the service recipient and the operational personnel is half of the service recipient's home shopping adjustment rate; when the service recipient's occupational location is within the service coverage area of ​​the operational personnel, the adjustment parameter is half of the work shopping adjustment rate. By iterating through all operational personnel, all service recipients with contact relationships are selected, and values ​​are assigned to each item in the sub-contact matrix, which is initially set to 0. This sub-contact matrix for the express delivery and food delivery scenario can describe the contact relationships between individuals providing express delivery or food delivery services and individuals receiving services within their service area. These contact relationships can be set with different contact frequencies based on the behavioral characteristics of the service recipients.

[0073] For the taxi scenario, taxi drivers are considered as operators, and customers interacting with them are considered as service recipients. The sub-contact matrix contains the product of the relative contact coefficient between service recipients and operators in the taxi scenario and an adjustment parameter. When the service recipient's home location is within the operator's service coverage area, the adjustment parameter between the service recipient and the operator is half of the service recipient's home-outbound adjustment rate; when the service recipient's occupational location is within the operator's service coverage area, the adjustment parameter is half of the work-outbound adjustment rate. By iterating through all operators, all service recipients with contact relationships are selected, and values ​​are assigned to each item in the sub-contact matrix, which starts at 0. This sub-contact matrix for the taxi scenario can describe the contact relationships between individuals engaged in taxi operation and passengers during the travel process. These contact relationships can be set with different contact frequencies based on passenger travel behavior characteristics.

[0074] Optionally, preset adjustment coefficients for home-based outing behavior, work-based outing behavior, total outing behavior, home-based online shopping behavior, work-based online shopping behavior, and total online shopping behavior for different occupations are provided. Preset adjustment coefficients for outing behavior and online shopping behavior for different age groups are also provided.

[0075] The initial home-away adjustment rate is obtained by multiplying the home-away behavior adjustment coefficient, the total out-of-home behavior adjustment coefficient, and the out-of-home behavior adjustment coefficient corresponding to the individual's occupation, age, and work-away behavior adjustment coefficient. Similarly, the initial work-away adjustment rate is obtained by multiplying the online shopping behavior adjustment coefficient, the total online shopping behavior adjustment coefficient, and the online shopping adjustment coefficient corresponding to the individual's occupation, age, and work-once-shopping behavior adjustment coefficient. Finally, the initial work-once-shopping adjustment rate is obtained by multiplying the online shopping behavior adjustment coefficient, the total online shopping behavior adjustment coefficient, and the online shopping adjustment coefficient corresponding to the individual's occupation, age, and work-once-shopping behavior adjustment coefficient.

[0076] Normalizing the initial home-outing adjustment rate, work-outing adjustment rate, home-online shopping adjustment rate, and work-online shopping adjustment rate for each individual yields the final home-outing adjustment rate, work-outing adjustment rate, home-online shopping adjustment rate, and work-online shopping adjustment rate. First, the home-outing adjustment rate and work-outing adjustment rate for each individual are averaged separately. Then, these averages for all individuals are summed to obtain the total adjustment rate for outings. The initial home-outing adjustment rate and work-outing adjustment rate for each individual are then multiplied by this total adjustment rate to obtain the normalized home-outing adjustment rate and work-outing adjustment rate. Similarly, the home-online shopping adjustment rate and work-online shopping adjustment rate for each individual are averaged separately. Then, these averages for all individuals are summed to obtain the total online shopping adjustment rate. The initial home-online shopping adjustment rate and work-online shopping adjustment rate for each individual are then multiplied by this total online shopping adjustment rate to obtain the normalized home-online shopping adjustment rate and work-online shopping adjustment rate.

[0077] The express delivery and food delivery scenarios can be further divided into express delivery scenarios and food delivery scenarios, and each can construct its own sub-contact matrix. Similarly, online shopping can be further divided into express delivery online shopping and food delivery online shopping.

[0078] For commuting scenarios, the sub-contact matrix comprises the product of the relative contact coefficients between commuters and an adjustment parameter. The adjustment parameter is twice the product of the expected commuting overlap between commuters and the grid side length of the virtual city spatial model. This sub-contact matrix describes the potential contact relationships that individuals may have while commuting using public transportation. These contact relationships exhibit spatial path characteristics and are correlated with the individual's travel characteristic modeling results.

[0079] Regarding the expected commuting overlap, the commuting process of commuters is represented as a monotonic path on a two-dimensional grid. Let the commuter's journey start from their home location (S=(x...)). s ,y s ), x s The x-coordinate parameter representing the family's location, y s (The vertical axis parameter represents the home location) to the occupational location (T=(x t ,y t ), x t The x-axis parameter representing occupational position, y t The vertical axis parameter represents the occupational position. Under the constraint of the "no turning back" principle, each step can only move towards the destination, that is, monotonically moving left or right horizontally and up or down vertically. Thus, all feasible commuting paths from S to T can be constructed from the arrangement of horizontal and vertical steps, and the total number is calculated using the following formula: The commuter population includes multiple commuter individuals. For any two commuter individuals A and B, their commuting paths are S and S, respectively. A →T A and S B →T B During the path selection process, the total commuting paths of two commuting individuals can generate a set of potential common grid edge points, denoted as E. ∩ For any candidate common edge e, calculate the probabilities that A and B traverse that edge. Assuming that commuters have the same probability of choosing each monotonic path, let the starting point of the candidate common edge e be (x...). e y e The direction is determined by its type (horizontal or vertical).

[0080] For commuting individuals agent∈{A,B}, define the number of monotonic level steps required to travel from the starting point S to the starting point of the candidate common edge. The number of monotonic vertical steps required to reach the starting point of the candidate common edge from the starting point S. After passing through this candidate public edge, proceed to the destination T. agent The remaining monotonic horizontal steps After passing through this candidate public edge, proceed to the destination T. agent The remaining monotonically vertical steps If these steps are consistent with the commuting direction of the commuting agent, then the probability that the commuting agent traverses the candidate common edge is... as follows: in, , , The x-axis parameter represents the occupational location of the commuting agent. The x-axis parameter represents the home location of the commuting agent. The ordinate parameter represents the occupational location of the commuting agent. The ordinate parameter represents the home location of the commuting agent. This represents the total number of horizontal steps taken by a commuting agent. This represents the total vertical steps taken by the commuting agent. If a step count is negative or the direction is incorrect, then let . .

[0081] Within this framework, the expected degree of commuting overlap that two commuting individuals might share during their commute. The calculation formula is as follows: In the formula, and Let represent the probabilities that two commuting individuals pass through the candidate common edge.

[0082] Reference Figure 8 The diagram illustrating the commuting locations of two individuals serves as an example. Individual A's home location is a(0,0), and their workplace location is a'(2,2); individual B's home location is b(2,0), and their workplace location is b'(1,3). Without turning back, individual A has 6 different paths from a to a', and individual B has 4 different paths from b to b'. In this case, the potential overlapping paths are (2,0)→(2,1), (2,1)→(2,2), (1,0)→(1,1), and (1,1)→(1,2). Assuming each individual's path selection is completely random, the probabilities of individual A traversing the four overlapping paths are 1 / 6, 1 / 2, 1 / 3, and 1 / 3, respectively, while the probabilities of individual B traversing the four overlapping paths are 3 / 4, 1 / 2, 1 / 4, and 1 / 2, respectively. Therefore, the expected commuting overlap between the two individuals is: .

[0083] For clustered scenarios, the sub-contact matrix comprises the product of the relative contact coefficients between people within the clustered scenario and adjustment parameters. The final outing adjustment coefficients for two individuals in contact within the clustered scenario, after global calibration, are obtained by multiplying the work-related outing behavior adjustment coefficients or the total outing behavior adjustment coefficients by the total outing adjustment rate. Multiplying these two final outing adjustment coefficients yields the corresponding adjustment parameters. This sub-contact matrix for clustered scenarios can describe the contact relationships that individuals may have in clustered scenarios such as large commercial facilities, public activity venues, or tourist attractions. These contact relationships are not limited to specific social units and can occur between any individuals in the virtual city spatial model that meet the clustering conditions.

[0084] In S42, since the population simulated by the model is usually much smaller than the real urban population, there is a power-law relationship between the contact intensity of different social activity scenarios and the population size. Therefore, it is necessary to adjust the scale of the coefficients of each contact matrix.

[0085] Since the sum of the coefficients of the clustered contact matrix is ​​always proportional to the square of the population size, i.e., C∝N 2 Therefore, a benchmark correction coefficient (also known as a power exponent) Z is set for different social activity scenarios. track These Z track The value represents the power-law exponent of contact intensity with population size growth in various scenarios, when Z track When Z = 2, the contact intensity is proportional to the square of the population (pure clustering scenario). track When Z = 1, the contact intensity grows linearly with the population (purely random contact). trackWhen Z < 1, the increase in contact intensity is slower than the increase in population (e.g., delivery is 0.9). track When the contact intensity is greater than 1, the growth rate of contact intensity is faster than that of population growth (e.g., 1.4 in commuting scenarios).

[0086] The specific values ​​of the baseline correction coefficients are not set arbitrarily, but are obtained through calibration using multiple sets of simulation experiments with different population sizes. For each type of social activity scenario, multiple agent models with different target population sizes are constructed. The sum of the population contact matrix coefficients for each type of social activity scenario is calculated, and a power-law fit is performed, as shown in the following formula: in, and This represents two individuals; For the fitting term, represents the proportionality coefficient; Indicates the size of the target audience; The formula represents the contact matrix coefficients between two individuals under different transmission pathways, with the left side representing the sum of the contact matrix coefficients for each transmission pathway. By fitting a power-law relationship between the sum of the contact matrix coefficients and the population size for different population sizes, the unique characteristics of each social activity scenario are determined. The value ensures that the baseline correction factor can accurately reflect the true growth pattern of contact intensity with population size in various scenarios.

[0087] In S43, the ratio between the actual population size and the target population size is obtained. For various social activity scenarios, this ratio is raised to the power of the corresponding scenario's baseline correction coefficient minus 1 to obtain the scale correction coefficient for each social activity scenario.

[0088] In S44, the scale correction coefficients for various social activity scenarios are multiplied by the corresponding sub-contact matrices to calibrate each sub-contact matrix. The calibrated sub-contact matrices are then superimposed to obtain a multi-level population contact network, output as an n×n heatmap. This network characterizes the potential contact relationships of individuals in different social scenarios, reflecting the diversity and complexity of population contact behavior in real cities. When simulating disease transmission, this multi-level population contact network can act simultaneously on the transmission process at the same time scale. Individuals can participate in multiple contact networks at different time periods, forming complex transmission paths. It can also be equipped with a population contact matrix visualization output function, which can output the number of different occupational groups in each specific institution across all institutional categories in the virtual city.

[0089] Reference Figure 9 This disclosure provides a multi-level population contact network construction system, including: The model building module 101 is used to obtain the actual population size and target population size of the target city, and to build a virtual city spatial model based on the actual population size and target population size. The individual distribution module 102 is used to configure the distribution of individuals with different attributes within the virtual city spatial model based on the population structure and target population size of the target city. The coefficient acquisition module 103 is used to obtain the relative contact coefficients between various groups of people in various social activity scenarios based on the individual's attributes and the individual's distribution location in the virtual city space model. Network construction module 104 is used to construct a multi-level population contact network based on the relative contact coefficients between various groups of people in various social activity scenarios.

[0090] The various variations and specific examples of the multi-level population contact network construction method provided above are also applicable to the multi-level population contact network construction system provided in this disclosure. Through the foregoing detailed description of the multi-level population contact network construction method, those skilled in the art can clearly understand the implementation method of the multi-level population contact network construction system. For the sake of brevity, they will not be described in detail here.

[0091] A computer device according to embodiments of the present disclosure includes a memory and a processor. The memory is used to store non-transitory computer-readable instructions. Specifically, the memory may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may, for example, include random access memory (RAM) and / or cache memory. The non-volatile memory may, for example, include read-only memory (ROM), hard disk, flash memory, etc.

[0092] The processor may be a central processing unit (CPU) or other form of processing unit with data processing capabilities and / or instruction execution capabilities, and may control other components in the computer device to perform desired functions. In one embodiment of this disclosure, the processor is used to execute computer-readable instructions stored in the memory, causing the computer device to perform all or part of the steps of the multi-level crowd contact network construction method of the foregoing embodiments of this disclosure.

[0093] Those skilled in the art will understand that, in order to solve the technical problem of how to achieve a good user experience, this embodiment may also include well-known structures such as communication buses and interfaces, and these well-known structures should also be included within the protection scope of this disclosure.

[0094] like Figure 10This is a schematic diagram of a computer device provided for an embodiment of the present disclosure. It illustrates a structural schematic diagram suitable for implementing the computer device in the embodiments of the present disclosure. Figure 10 The computer device shown is merely an example and should not be construed as limiting the functionality and scope of the embodiments disclosed herein.

[0095] like Figure 10 As shown, a computer device may include a processor (such as a central processing unit, graphics processing unit, etc.), which can perform various appropriate actions and processes based on programs stored in read-only memory (ROM) or programs loaded from storage devices into random access memory (RAM). The RAM also stores various programs and data required for the operation of the computer device. The processor, ROM, and RAM are interconnected via a bus. Input / output (I / O) interfaces are also connected to the bus.

[0096] Typically, the following devices can be connected to the I / O interface: input devices, such as sensors or visual information acquisition devices; output devices, such as displays; storage devices, such as magnetic tapes or hard drives; and communication devices. Communication devices allow the computer device to communicate wirelessly or wiredly with other devices (such as edge computing devices) to exchange data. Although Figure 10 A computer apparatus with various devices is shown, but it should be understood that it is not required to implement or have all of the devices shown. More or fewer devices may be implemented or included alternatively.

[0097] In particular, according to embodiments of this disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this disclosure include a computer program product comprising a computer program carried on a non-transitory computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from a storage device, or installed from a ROM. When the computer program is executed by a processor, all or part of the steps of the multi-level crowd contact network construction method of embodiments of this disclosure are performed.

[0098] For a detailed description of this embodiment, please refer to the corresponding descriptions in the foregoing embodiments, which will not be repeated here.

[0099] A computer-readable storage medium according to embodiments of the present disclosure stores non-transitory computer-readable instructions. When these non-transitory computer-readable instructions are executed by a processor, all or part of the steps of the multi-level crowd contact network construction method described in the foregoing embodiments of the present disclosure are performed.

[0100] The aforementioned computer-readable storage media include, but are not limited to: optical storage media (e.g., CD-ROM and DVD), magneto-optical storage media (e.g., MO), magnetic storage media (e.g., magnetic tape or portable hard drive), media with built-in rewritable non-volatile memory (e.g., memory card), and media with built-in ROM (e.g., ROM cartridge).

[0101] For a detailed description of this embodiment, please refer to the corresponding descriptions in the foregoing embodiments, which will not be repeated here.

[0102] The basic principles of this disclosure have been described above with reference to specific embodiments. However, it should be noted that the advantages, benefits, and effects mentioned in this disclosure are merely examples and not limitations, and should not be considered as essential features of each embodiment of this disclosure. Furthermore, the specific details disclosed above are for illustrative and facilitative purposes only, and are not limitations. These details do not limit the scope of this disclosure to the necessity of employing the aforementioned specific details for implementation.

[0103] In this disclosure, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. The block diagrams of devices, apparatuses, devices, and systems involved in this disclosure are merely illustrative examples and are not intended to require or imply that they must be connected, arranged, or configured in the manner shown in the block diagrams. As those skilled in the art will recognize, these devices, apparatuses, devices, and systems can be connected, arranged, and configured in any manner. Words such as "comprising," "including," "having," etc., are open-ended terms meaning "including but not limited to," and are used interchangeably with them. The terms "or" and "and" as used herein refer to the terms "and / or," and are used interchangeably with them unless the context clearly indicates otherwise. The term "such as" as used herein refers to the phrase "such as but not limited to," and is used interchangeably with it.

[0104] Additionally, as used herein, the "or" used in a list of items beginning with "at least one" indicates a separate list, such that a list of, for example, "at least one of A, B, or C" means A or B or C, or AB or AC or BC, or ABC (i.e., A and B and C). Furthermore, the word "exemplary" does not imply that the described example is preferred or better than other examples.

[0105] It should also be noted that in the systems and methods of this disclosure, the components or steps can be decomposed and / or recombined. These decompositions and / or recombinations should be considered as equivalent solutions to this disclosure.

[0106] Various changes, substitutions, and modifications can be made to the technology described herein without departing from the teachings defined by the appended claims. Furthermore, the scope of the claims of this disclosure is not limited to the specific aspects of the processes, machines, manufactures, events, means, methods, and actions described above. Currently existing or later-developed processes, machines, manufactures, events, means, methods, or actions that perform substantially the same function or achieve substantially the same result as the corresponding aspects described herein can be utilized. Therefore, the appended claims include such processes, machines, manufactures, events, means, methods, or actions within their scope.

[0107] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use this disclosure. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to other aspects without departing from the scope of this disclosure. Therefore, this disclosure is not intended to be limited to the aspects shown herein, but rather to be carried out within the widest scope consistent with the principles and novel features disclosed herein.

[0108] The above description has been given for purposes of illustration and description. Furthermore, this description is not intended to limit the embodiments of this disclosure to the forms disclosed herein. Although numerous exemplary aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, alterations, additions, and sub-combinations therein.

Claims

1. A method for constructing a multi-level population contact network, characterized in that, include: Obtain the actual population size and target population size of the target city, and construct a virtual city spatial model based on the actual population size and the target population size; Based on the population structure of the target city and the size of the target population, individuals with different attributes are distributed and configured within the virtual city spatial model; Based on individual attributes and their distribution location within the virtual city space model, the relative contact coefficients between various groups of people in various social activity scenarios are obtained. Based on the relative contact coefficients between different groups of people in various social activity scenarios, a multi-level group contact network is constructed.

2. The method for constructing a multi-level population contact network according to claim 1, characterized in that, The construction of a virtual city spatial model based on the actual population size and the target population size includes: Based on the ratio of the target population size to the actual population size, the geographic space of the target city is scaled proportionally to construct a virtual city grid space; The virtual city grid space is divided into multiple living areas; Based on the size of the target population, determine the number of institutions and households; Families and institutions are allocated to each living area according to the capacity quota of each living area; Within each living area, families and institutions are randomly placed at coordinate points within that living area to form a virtual city space model.

3. The method for constructing a multi-level population contact network according to claim 2, characterized in that, The attributes include age, gender, and occupation; the distribution location of individuals within the virtual city spatial model includes family location and occupational location; the distribution configuration of individuals with different attributes within the virtual city spatial model based on the population structure of the target city and the size of the target population includes: Based on the family structure, set the age and gender of individuals within the family; Based on an individual's age and the occupational distribution characteristics of the target city, the individual's occupation is set; Based on the number of each type of institution and the average number of people of each occupation within the institution, the number of people of each occupation allowed to be accommodated in each institution is randomly determined in a normal distribution. Based on an individual's occupation, determine whether the individual has an applicable institution category, wherein the institution category includes regular work institutions and residential institutions; When an individual has an applicable institution category, the individual matched to the institution category is assigned to a suitable institution based on the number of people of various occupations that each institution under the institution category is allowed to accommodate; Based on whether an individual has an applicable institution category and the institution category to which they belong, their family location and occupational location are determined.

4. The method for constructing a multi-level population contact network according to claim 3, characterized in that, The determination of an individual's family and occupational location based on whether the individual has an applicable institutional category and the institutional category to which they belong includes: When no applicable institution category is available for an individual, the family coordinates are used as the individual's family location and occupational location. When the applicable institution category for an individual is a regular work institution, the family coordinates are used as the individual's family location, and the institution coordinates are used as the individual's occupational location. When an individual is assigned to a residential institution, the institution's coordinates are used as both the individual's home location and occupational location.

5. The method for constructing a multi-level population contact network according to claim 4, characterized in that, The method of obtaining relative contact coefficients between various groups of people in various social activity scenarios based on individual attributes and their distribution location within the virtual city spatial model includes: Based on an individual's occupation and location, we determine the various social activity scenarios in which the individual is situated, and classify the corresponding groups of people in each social activity scenario. Set total contact coefficients for different groups of people in various social activity scenarios; Based on the occupations and sizes of various groups of people in various social activity scenarios, obtain the corresponding average total; Based on the total contact coefficient and the average total number, the individual contact coefficients of various groups of people in various social activity scenarios are obtained; Using the total contact coefficient in the family setting as the benchmark, the ratio between the individual contact coefficients of various groups of people in various social activity scenarios and the benchmark is used as the corresponding relative contact coefficient.

6. The method for constructing a multi-level population contact network according to claim 5, characterized in that, The above refers to setting total contact coefficients for various groups of people in various social activity scenarios, including: Identify all contact channels between various groups of people in various social activity scenarios, and obtain the frequency of occurrence, duration of contact, size of the contacted group, and probability of transmission for each contact channel per unit time. Based on the occurrence frequency, contact duration, contact population size, and propagation probability, obtain the channel contact coefficient for each contact channel among various groups of people in various social activity scenarios; The total contact coefficient of all contact channels among various groups of people in the same social activity scenario is obtained by summing up the contact coefficients of all contact channels among various groups of people in various social activity scenarios.

7. The method for constructing a multi-level population contact network according to claim 5, characterized in that, The above refers to setting total contact coefficients for various groups of people in various social activity scenarios, including: Set the propagation probability of a home scenario within a unit of time and use it as a reference value; Identify all contact channels between various groups of people in various social activity scenarios, and obtain the frequency of occurrence, the size of the contacted group, and the probability of transmission per contact for each contact channel per unit time. Based on the occurrence frequency, the size of the contacted population, the single contact transmission probability, and the reference value, the channel contact coefficient of each contact channel between various groups of people in various social activity scenarios is obtained; The total contact coefficient of all contact channels among various groups of people in the same social activity scenario is obtained by summing up the contact coefficients of all contact channels among various groups of people in various social activity scenarios.

8. The method for constructing a multi-level population contact network according to claim 5, characterized in that, The method of obtaining the corresponding average total based on the occupations and scales of various groups of people in various social activity scenarios includes: When the social activity scenario is a family scenario or a commuting scenario, and the types of people in the social activity scenario are the same, the average total number of people of the same type is set to 1. When the social activity scenario is a commercial service institution scenario, the population is divided into service recipients and operating personnel. The total shared amount among service recipients is set as the product of the number of service recipients within the service coverage area of ​​the commercial service institution, the square of the total number of similar institutions within the service coverage area, and the number of operating personnel. The total shared amount among operating personnel is set as the number of operating personnel minus 1. The total shared amount between service recipients and operating personnel is set as the product of the total number of similar institutions within the service coverage area and the number of operating personnel. When the social activity scenario is a production and operation institution scenario, the average total number of people of the same type is set to the size of the same type of people minus 1, and the average total number of people of different types who come into contact with each other is set to the size of the people who come into contact with each other. When the social activity scenario is a delivery or taxi scenario, the population is divided into service recipients and operators. The total number shared between service recipients and operators is set as the number of service recipients within the service coverage area of ​​the operators minus the number of operators. When the social activity scenario is an activity scenario or a gathering scenario, and the types of people in the social activity scenario are the same, the average total number of people of the same type is set to the total population size in the activity area or gathering area minus 1.

9. The method for constructing a multi-level population contact network according to claim 1, characterized in that, The construction of a multi-level population contact network based on the relative contact coefficients between various groups of people in various social activity scenarios includes: Based on the relative contact coefficients between various groups of people in various social activity scenarios, sub-contact matrices for various social activity scenarios are constructed. Based on the power-law growth relationship between the contact intensity and population size in various social activity scenarios, the baseline correction coefficients for various social activity scenarios are obtained. Based on the actual population size, the target population size, and the baseline correction coefficient, obtain the scale correction coefficients for various social activity scenarios; Each sub-contact matrix is ​​calibrated based on the aforementioned scale correction coefficient, and all calibrated sub-contact matrices are merged to obtain a multi-level population contact network.

10. A computer device, characterized in that, The computer device includes: At least one processor; and, A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the multi-level population contact network construction method according to any one of claims 1-9.

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