Data prediction method, device, electronic device and computer-readable medium

Abstract

The present disclosure relates to a method, device, electronic device and computer-readable medium for predicting infectious disease transmission data, and belongs to the field of artificial intelligence technology. The method includes: obtaining social demographic data, and constructing an underlying individual contact model based on the social demographic data and multiple preset contact scenarios; obtaining an individual transmission model of an infectious disease, and setting relevant infectious disease transmission parameters and infectious disease intervention parameters; based on the individual transmission model and the infectious disease transmission parameters and infectious disease intervention parameters, determining the state transition probability of individuals in each transmission state in the underlying individual contact model within each preset time period; and obtaining the predicted value of infectious disease transmission data within each preset time period based on the state transition probability of the individual within each preset time period. The present disclosure can more scientifically understand the laws of infectious disease transmission and improve the accuracy of infectious disease transmission data prediction by modeling and simulating the infectious disease transmission process at the micro level.

Description

Technical Field

[0001] The present disclosure relates to the field of artificial intelligence technology, and in particular to a method for predicting infectious disease transmission data, a device for predicting infectious disease transmission data, an electronic device, and a computer-readable medium. Background Art

[0002] Simulation and modeling involve digitizing physical laws abstracted from real-world objects to create system models that can describe or simulate their structure or behavior. These models are used for virtual testing and early verification of real-world applications, thereby reducing the cost of direct testing. By combining scientific research literature with real-world data, simulation and modeling technologies are playing an increasingly important role in the field of epidemics.

[0003] Currently, simulations and modeling of infectious disease transmission mostly employ infectious disease dynamics, employing a set of differential equations to model infectious diseases. However, because these models are too macroscopic, they simplify the real-world scenario by replacing the sum of individual effects with average effects. This coarse-grained representation of real-world scenarios makes it difficult to capture population heterogeneity and accurately simulate and analyze specific interventions, resulting in suboptimal predictive performance.

[0004] In view of this, this field urgently needs a method that can improve the prediction effect of the model.

[0005] It should be noted that the information disclosed in the above background technology section is only used to enhance the understanding of the background of the present disclosure, and therefore may include information that does not constitute prior art known to ordinary technicians in the field. Summary of the Invention

[0006] The purpose of the present disclosure is to provide a method for predicting infectious disease transmission data, a device for predicting infectious disease transmission data, an electronic device and a computer-readable medium, thereby improving the accuracy of model predictions at least to a certain extent.

[0007] According to a first aspect of the present disclosure, a method for predicting infectious disease transmission data is provided, comprising:

[0008] Obtaining sociodemographic data and constructing an underlying individual contact model based on the sociodemographic data and a plurality of preset contact scenarios;

[0009] Obtaining an individual transmission model of the infectious disease and setting relevant infectious disease transmission parameters and infectious disease intervention parameters;

[0010] Determining the state transition probability of individuals in each propagation state in the underlying individual contact model within each preset time period based on the individual propagation model, the infectious disease propagation parameter, and the infectious disease intervention parameter;

[0011] According to the state transition probability of the individual in each preset time period, a predicted value of the infectious disease spread data in each preset time period is obtained.

[0012] In an exemplary embodiment of the present disclosure, constructing an underlying individual contact model based on the socio-demographic data and a plurality of preset contact scenarios includes:

[0013] Dividing all individuals in the bottom-level individual contact model into different levels of social contact units based on the sociodemographic data, and determining the social types of the individuals;

[0014] Dividing each of the preset time periods into a first contact time period and a second contact time period;

[0015] Determining, according to the social type of the individual, a preset contact scenario for the individual during the first contact time period, wherein the preset contact scenario includes a community scenario, a school scenario, and a work scenario;

[0016] Construct a contact network of the individual in each preset contact scenario during the first contact time period, and a contact network of the individual in the community scenario during the second contact time period, and obtain the underlying individual contact model based on each of the contact networks.

[0017] In an exemplary embodiment of the present disclosure, determining the state transition probability of individuals in each transmission state in the underlying individual contact model within each preset time period based on the individual transmission model, the infectious disease transmission parameter, and the infectious disease intervention parameter includes:

[0018] Obtaining the transmission status of the individual at the current prediction time point, wherein the transmission status includes susceptible state, exposed state, infected state, confirmed state, and recovered state;

[0019] determining an intervention action type according to the infectious disease intervention parameter, and determining a target contact scenario in which the individual is located during the first contact time period according to the intervention action type and the social type of the individual;

[0020] Determining, based on the infectious disease intervention parameters and the infectious disease transmission parameters in the target contact scenario, a first state transition probability of an individual in each of the transmission states within the first contact time period;

[0021] Determining an intermediate predicted time point based on the current predicted time point and the first contact time period, and determining the propagation state of the individual at the intermediate predicted time point based on the first state transition probability;

[0022] Based on the transmission status of the individual at the intermediate prediction time point, as well as the infectious disease transmission parameters and the infectious disease intervention parameters in the community scenario, the second state transition probability of the individual in each of the transmission states within the second contact time period is determined.

[0023] In an exemplary embodiment of the present disclosure, determining the first state transition probability of an individual in each of the transmission states within the first contact time period based on the infectious disease intervention parameter and the infectious disease transmission parameter in the target contact scenario includes:

[0024] determining a first state transition probability of the individual in the susceptible state within the first contact time period based on the infectious disease intervention parameter and the infectious disease transmission parameter in the target contact scenario;

[0025] According to the infectious disease transmission parameters and the first state transition probabilities of individuals in the susceptible state, the first state transition probabilities of individuals in the exposed state, the infected state, the confirmed state and the recovered state within the first contact time period are determined in sequence.

[0026] In an exemplary embodiment of the present disclosure, determining the first state transition probability of the individual in the susceptible state within the first contact time period based on the infectious disease intervention parameter and the infectious disease transmission parameter in the target contact scenario includes:

[0027] Obtaining vaccine efficiency parameters and individual protection parameters for the susceptible individual according to the infectious disease intervention parameters;

[0028] Determining the individual contact weight under the target contact scenario according to the infectious disease transmission parameter and the individual protection parameter under the target contact scenario;

[0029] Determining the probability of infection of an individual in the infected state and the probability of infection of an individual in the susceptible state according to the vaccine efficiency parameter;

[0030] According to the individual contact weight in the target contact scenario, the infection probability of the individual in the infected state, and the infection probability of the individual in the susceptible state, the first state transition probability of the individual in the susceptible state within the first contact time period is obtained.

[0031] In an exemplary embodiment of the present disclosure, determining the individual contact weight in the target contact scenario according to the infectious disease transmission parameter and the individual protection parameter in the target contact scenario includes:

[0032] Obtaining a probability of contact between the individual and the individual in the infected state in the target contact scenario according to the social type of the individual;

[0033] The individual contact weight under the target contact scenario is determined according to the infectious disease transmission parameters, the individual protection parameters, and the contact probability under the target contact scenario.

[0034] In an exemplary embodiment of the present disclosure, obtaining a predicted value of infectious disease spread data in each preset time period based on the state transition probability of the individual in each preset time period includes:

[0035] Obtaining a change value of the number of individuals in each of the propagation states according to the state transition probability of the individuals in each of the preset time periods;

[0036] According to the change value of the number of individuals under each of the said propagation states, the predicted value of the infectious disease propagation data within each of the said preset time periods is obtained.

[0037] According to a second aspect of the present disclosure, there is provided a device for predicting infectious disease spread data, comprising:

[0038] An underlying model building module, configured to obtain sociodemographic data and build an underlying individual contact model based on the sociodemographic data and a plurality of preset contact scenarios;

[0039] a propagation model acquisition module, configured to acquire the individual propagation model of the infectious disease and set relevant infectious disease propagation parameters and infectious disease intervention parameters;

[0040] a transition probability determination module, configured to determine, based on the individual transmission model, the infectious disease transmission parameter, and the infectious disease intervention parameter, the state transition probability of an individual in each transmission state in the underlying individual contact model within each preset time period;

[0041] The propagation data prediction module is used to obtain the predicted value of the infectious disease propagation data in each preset time period based on the state transition probability of the individual in each preset time period.

[0042] According to a third aspect of the present disclosure, an electronic device is provided, comprising: a processor; and a memory for storing executable instructions of the processor; wherein the processor is configured to execute any one of the above-mentioned methods for predicting infectious disease transmission data by executing the executable instructions.

[0043] According to a fourth aspect of the present disclosure, a computer-readable medium is provided, on which a computer program is stored. When the computer program is executed by a processor, it implements the method for predicting infectious disease transmission data described in any one of the above.

[0044] The exemplary embodiments of the present disclosure may have the following beneficial effects:

[0045] In the method for predicting infectious disease transmission data of the exemplary embodiment of the present disclosure, an underlying individual contact model is constructed based on social demographic data, and based on the individual transmission model of the infectious disease and related infectious disease transmission parameters and infectious disease intervention parameters, the state transition probability of individuals in each transmission state in the underlying individual contact model within each preset time period is determined, thereby obtaining the predicted value of infectious disease transmission data within each preset time period. The method for predicting infectious disease transmission data in the exemplary embodiment of the present disclosure, on the one hand, by tracking and analyzing the infection status of each individual based on the individual model at the micro level, and performing micro-level modeling and simulation of the transmission process of infectious diseases in a large-scale population, can more scientifically understand the laws of infectious disease transmission and improve the accuracy of infectious disease transmission data prediction; on the other hand, by combining relevant intervention actions for modeling, the infectious disease control effect of each intervention action can be analyzed from a micro level. By combining with actual infectious disease data, ongoing infectious diseases can be digitized, thereby testing the effectiveness of various infectious disease intervention actions, further guiding the implementation of relevant intervention actions, and achieving the purpose of making decision recommendations and effect simulations for infectious disease control.

[0046] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] The accompanying drawings are incorporated into and constitute a part of the specification, illustrate embodiments consistent with the present disclosure, and together with the specification, are used to explain the principles of the present disclosure. Obviously, the drawings described below are only some embodiments of the present disclosure, and those skilled in the art can derive other drawings based on these drawings without inventive effort.

[0048] Figure 1 A schematic flow chart showing a method for predicting infectious disease spread data according to an exemplary embodiment of the present disclosure;

[0049] Figure 2 A schematic diagram of a process for constructing an underlying individual contact model according to an exemplary embodiment of the present disclosure is shown;

[0050] Figure 3 A schematic diagram of an underlying individual contact model according to one embodiment of the present disclosure is shown;

[0051] Figure 4 A schematic diagram of an infectious disease transmission dynamics model according to a specific embodiment of the present disclosure is shown;

[0052] Figure 5A schematic diagram showing a flow chart of determining the state transition probability of individuals in various propagation states within each preset time period according to an exemplary embodiment of the present disclosure is shown;

[0053] Figure 6 A schematic diagram showing a flow chart of determining the first state transition probability of an individual in each propagation state according to an exemplary embodiment of the present disclosure is shown;

[0054] Figure 7 A schematic diagram showing a process of determining a first state transition probability of an individual in a susceptible state according to an exemplary embodiment of the present disclosure is shown;

[0055] Figure 8 A schematic flow chart of a method for predicting infectious disease spread data according to a specific embodiment of the present disclosure is shown;

[0056] Figure 9 A specific application scenario of the infectious disease individual simulation model according to a specific embodiment of the present disclosure is shown;

[0057] Figure 10 A block diagram showing an apparatus for predicting infectious disease spread data according to an exemplary embodiment of the present disclosure;

[0058] Figure 11 A schematic structural diagram of a computer system suitable for implementing the electronic device according to the embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0059] In order to enable ordinary persons in the art to better understand the technical solutions of the present disclosure, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below with reference to the accompanying drawings.

[0060] It should be noted that the terms "first," "second," and the like in the specification and claims of the present disclosure and the accompanying drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or precedence. It should be understood that the terms used in this manner are interchangeable where appropriate, such that the embodiments of the present disclosure described herein can be implemented in an order other than that illustrated or described herein.

[0061] The following example embodiments can be implemented in a variety of forms and should not be construed as being limited to the examples set forth herein; rather, these embodiments are provided so that the present disclosure will be more comprehensive and complete and will fully convey the concepts of the example embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, many specific details are provided to provide a full understanding of the embodiments of the present disclosure. However, those skilled in the art will appreciate that the technical solutions of the present disclosure may be practiced while omitting one or more of the specific details, or that other methods, components, devices, steps, etc. may be employed. In other cases, well-known technical solutions are not shown or described in detail to avoid obscuring various aspects of the present disclosure.

[0062] In addition, the accompanying drawings are merely schematic illustrations of the present disclosure and are not necessarily drawn to scale. Identical reference numerals in the figures denote identical or similar parts, and thus repetitive descriptions thereof will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities that do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different networks and / or processor devices and / or microcontroller devices.

[0063] Simulation and modeling refer to the process of digitizing physical laws abstracted from real-world objects to create a system model that can describe or simulate the structure or behavior of the object. This model is then used for virtual testing and early verification of the real world, thereby reducing the cost of direct testing.

[0064] By combining scientific research literature with real-world data, simulation and modeling technologies are playing an increasingly important role in the field of epidemiology. For example, by modeling a specific infectious disease, its spread patterns can be analyzed through simulation; by simulating interventions, the potential effects of various measures can be analyzed. Furthermore, by using actual infectious disease data for modeling and calibration, it is possible to predict and assess the trends of infectious diseases in advance, thereby reducing their impact on human production and life.

[0065] In some relevant embodiments, ABM (Agent-Based Modeling, a micro-model based on the individual level) can be used to represent each individual in the simulation environment using a multi-agent approach, thereby simulating the spread of infectious diseases at the micro level.

[0066] While the ABM approach can achieve micro-level modeling, its modeling approach limits the scale of simulation objects and the simulation of interventions. Because interventions such as isolating confirmed patients and their close contacts and recommending mask wearing are not simulated and implemented, it is not conducive to a comprehensive analysis of the effectiveness of various interventions.

[0067] This exemplary embodiment first provides a method for predicting infectious disease spread data. Figure 1 As shown, the above-mentioned method for predicting infectious disease spread data may include the following steps:

[0068] Step S110: Obtain sociodemographic data, and construct an underlying individual contact model based on the sociodemographic data and multiple preset contact scenarios.

[0069] Step S120: Obtain an individual infectious disease transmission model and set relevant infectious disease transmission parameters and infectious disease intervention parameters.

[0070] Step S130. Based on the individual transmission model, infectious disease transmission parameters and infectious disease intervention parameters, determine the state transition probability of individuals in each transmission state in the underlying individual contact model within each preset time period.

[0071] Step S140: Obtain the predicted value of the infectious disease spread data in each preset time period based on the state transition probability of the individual in each preset time period.

[0072] In the method for predicting infectious disease transmission data of the exemplary embodiment of the present disclosure, an underlying individual contact model is constructed based on social demographic data, and based on the individual transmission model of the infectious disease and related infectious disease transmission parameters and infectious disease intervention parameters, the state transition probability of individuals in each transmission state in the underlying individual contact model within each preset time period is determined, thereby obtaining the predicted value of infectious disease transmission data within each preset time period. The method for predicting infectious disease transmission data in the exemplary embodiment of the present disclosure, on the one hand, by tracking and analyzing the infection status of each individual based on the individual model at the micro level, and performing micro-level modeling and simulation of the transmission process of infectious diseases in a large-scale population, can more scientifically understand the laws of infectious disease transmission and improve the accuracy of infectious disease transmission data prediction; on the other hand, by combining relevant intervention actions for modeling, the infectious disease control effect of each intervention action can be analyzed from a micro level. By combining with actual infectious disease data, ongoing infectious diseases can be digitized, thereby testing the effectiveness of various infectious disease intervention actions, further guiding the implementation of relevant intervention actions, and achieving the purpose of making decision recommendations and effect simulations for infectious disease control.

[0073] Next, combine Figures 2 to 7The above steps of this exemplary embodiment are described in more detail.

[0074] In step S110 , sociodemographic data is obtained, and an underlying individual contact model is constructed based on the sociodemographic data and a plurality of preset contact scenarios.

[0075] In this example implementation, the underlying individual contact model is an individual simulation model based on the micro level, which can track and analyze the behavior and status of each individual in a preset contact scenario, and can be used to simulate and depict the activities of people in real scenarios.

[0076] Socio-demographic data can include age structure, household size, employment rate, work group distribution, school size distribution, commuting data, etc., which can be obtained through census results and population vector data.

[0077] In this example implementation, Figure 2 As shown in the figure, the underlying individual contact model is constructed based on socio-demographic data and multiple preset contact scenarios. Specifically, the following steps can be included:

[0078] Step S210: Divide all individuals in the bottom-level individual contact model into social contact units of different levels based on sociodemographic data, and determine the social type of the individuals.

[0079] Based on basic socio-demographic data, it can be assumed that the bottom individuals contact the common n individuals in the model. First, the population can be divided into D = 1, ..., k groups according to geographical administrative regions, and each region is divided into several communities C d =1, 2, ..., and then each community is divided into households of different sizes according to a certain ratio, and n dc represents the number of people in the cth community.

[0080] In this example implementation, the family can be used as the smallest and most intimate unit of social contact. The distance between individuals is divided according to the level of the social contact unit, and the contact rate decreases as the social contact unit increases. The specific order of social contact units from smallest to largest is: family, family cluster, community, administrative district, and city.

[0081] An individual's social type can mainly include employed people, students, unemployed people, etc. Different social types can determine the individual's main place of activity.

[0082] Step S220: Divide each preset time period into a first contact time period and a second contact time period.

[0083] In this example implementation, assuming that the model performs data statistics once a day, the preset time period can be set to one day, for example, from 8:00 in the morning to 8:00 the next morning is a complete time period.

[0084] Generally speaking, people's daily social patterns are usually divided into two types: daytime and nighttime. Therefore, each preset time period can be divided into a first contact time period and a second contact time period, wherein the first contact time period refers to the daytime period, such as 8 am to 6 pm, and the second contact time period refers to the nighttime period, such as 6 pm to 8 am the next day.

[0085] Step S230. Determine the preset contact scenarios of the individual within the first contact time period according to the individual's social type, wherein the preset contact scenarios include community scenarios, school scenarios, and work scenarios.

[0086] Because different types of individuals have different primary daytime locations during the first contact time period, it is necessary to determine the individual's pre-set contact scenario for the first contact time period based on their social type. Pre-set contact scenarios include community, school, and work. For example, if an individual's social type is a student, the pre-set contact scenario for the individual during the first contact time period is a school.

[0087] Step S240: Construct the contact network of the individual in each preset contact scenario during the first contact time period, and the contact network of the individual in the community scenario during the second contact time period, and obtain the underlying individual contact model based on each contact network.

[0088] like Figure 3 Figure 2 shows a schematic diagram of a bottom-level individual contact model according to a specific embodiment of the present disclosure. This model utilizes a composite structure of multiple layers of contact networks to simulate different types of social activities in real-world scenarios. Each layer simulates a different contact scenario, with the three social activity networks—community, school, and work—being independently and synchronously calculated.

[0089] The model divides individuals' daily activity patterns into daytime and nighttime. During the day, interpersonal interactions occur in three scenarios: community, school, and work. In the school scenario network layer, most students attend school or playgroups, and preschoolers typically form playgroups or community kindergartens. Each community has mixed groups representing two elementary schools, one middle school, and one high school. In the work scenario network layer, working-age individuals are assigned workplaces based on employment rates, and each employed individual is assigned a work destination based on actual commuting data. Workers are assigned to communities within their destination areas to simulate daytime social interactions, with work groups of approximately 20 people representing their close workplace contacts. Unemployed individuals remain in their home communities and have no daytime interactions in the work setting, except for interactions with other family members who are not working or attending school. In contrast, evening social interactions occur only within the community scenario contact network, where individuals can interact with their families, family clusters, home communities, and others within their home communities.

[0090] In the community activity network layer, the nodes in the macro graph represent communities, and each node in the micro graph represents an individual. The number of nodes per household is determined by the actual household size distribution. Based on the actual age distribution within the household, the association between age groups and each node is set. Based on the age distribution of education and employment rates, each node type determines whether it also participates in school or work activities. Similar to the household layer, the work layer is composed of n w The school layer consists of n disconnected components, each component represents a workplace, and the number of nodes in each workplace follows the actual workplace size distribution. The individuals in each workplace are further divided into several work groups, which serve as the smallest contact units in the workplace layer. The school layer consists of n s The number of nodes for each school is allocated based on the actual school size distribution and by school type, including preschool, elementary school, junior high school, and high school.

[0091] In step S120, an individual infectious disease transmission model is obtained, and relevant infectious disease transmission parameters and infectious disease intervention parameters are set.

[0092] In this exemplary embodiment, the individual transmission model of an infectious disease is a model established based on the dynamics of infectious disease transmission, and is used to simulate the transmission and transfer of states between individuals.

[0093] like Figure 4 FIG2 is a schematic diagram of an infectious disease transmission dynamics model according to a specific embodiment of the present disclosure. The infectious disease transmission process is represented by a discrete-time susceptible-exposed-infected-diagnosed-removed (SEIAR) model with a time step t.

[0094] In this example implementation, S can be defined i，t , E i，t , I i，t , A i，t , R i，t is the state of Bernoulli random variable individual i at time t, S i，t +E i，t +I i，t +A i，t +R i，t =1, the probability of each state occurring SEIAR disease dynamics can be described as a discrete Markov process in which transition probabilities vary over time.

[0095] In this example implementation, infectious disease transmission parameters may include viral load, contact probability between different groups, etc. Infectious disease transmission parameters and infectious disease intervention parameters can be used to calculate state transition probabilities during subsequent individual state transitions. Intervention measures may include setting vaccination backgrounds and designing intervention actions, as follows:

[0096] (1) Vaccine background setting.

[0097] This example implementation takes vaccination into consideration. Preventive measures and reactive measures can be set based on the vaccination situation. The former refers to vaccination before an infectious disease outbreak, and the latter refers to vaccination during an infectious disease outbreak. When neither vaccination method is adopted, the model returns to the original situation without vaccination.

[0098] Regarding the characteristics of the vaccine, the relevant parameters set in the model may include the vaccine number (when multiple vaccines exist at the same time and need to be distinguished, this is achieved through numbering), the effectiveness of the vaccine for different groups, the vaccination rate of different types of people or whether they can be vaccinated (such as people over 65 years old, minors under 18 years old, pregnant women, etc.), the number of vaccine injections required, and the vaccination cycle.

[0099] For vaccine recipients, corresponding parameters are also set in the model, including the vaccination ratio among the eligible population, vaccination priorities for different types of personnel (such as people in high-risk industries need to be vaccinated first, and people with congenital diseases or pregnant women are not vaccinated), and the effectiveness of the vaccine in different age groups (the default effectiveness is consistent).

[0100] Vaccine-related parameters can be completed as model input during the simulation initialization process, and some parameters can be changed according to conditions during the simulation process.

[0101] (2) Setting and enabling various intervention actions.

[0102] a. Work stoppage

[0103] The work stoppage policy is implemented by requiring a certain percentage of employees to work from home, meaning that a certain percentage of employees do not participate in work-related activities during the day, but instead participate in community activities at home. The start and end times of working from home can be set.

[0104] b. Suspension from school

[0105] Two suspension plans are available: the first suspends classes in schools in medium- and high-risk areas, while other schools remain open. The second suspends classes for all schools in the region the day after a confirmed case appears. The start and duration of the suspension can be set.

[0106] c. Closed community management

[0107] By setting a certain percentage of communities to implement closed management policies, the probability of contact between people in the community is reduced. The start and end times of community closed management can be set.

[0108] d. Isolation policy

[0109] Two isolation policies are set: one is home isolation and the other is centralized isolation. The former allows for virus transmission between family members, potentially leading to intra-family transmission. The latter isolates close contacts in dedicated isolation facilities and provides them with professional medical care, thus preventing further intra-family transmission. The model defaults to centralized isolation for close contacts (such as family members of the infected person, colleagues in the same office, etc.) and home isolation for secondary close contacts. Isolation policies for different types of close contacts can be set before the simulation begins. The definitions of close contacts and secondary close contacts can also be changed to expand or narrow the population that needs to be isolated. Similarly, the isolation duration is adjustable, with the default setting being 14 days.

[0110] e. Mask wearing policy

[0111] The model includes a mask-wearing policy that requires masks to be worn in public places, meaning all places other than residences. After wearing a mask, the probability of virus transmission is multiplied by a factor less than 1, depending on the mask's protection rate. This indicates a decrease in the probability of virus transmission. Parameters include the mask-wearing rate, mask protection rate, the time to start wearing masks, and the time to relax the mask-wearing policy. The default setting for the mask-wearing policy is that it applies at all times during the simulation.

[0112] In this example implementation, the aforementioned intervention actions can be used to analyze the spread of infectious diseases after vaccination. This provides data analysis for how to normalize infectious disease prevention and control after a vaccine has been developed. By incorporating appropriate interventions, such as centralized quarantine policies and widespread mask wearing, the model can quantitatively assess the effectiveness of these interventions.

[0113] In step S130, based on the individual transmission model and the infectious disease transmission parameters and infectious disease intervention parameters, the state transition probability of individuals in each transmission state in the underlying individual contact model within each preset time period is determined.

[0114] In this example implementation, by embedding the individual communication model into a pre-established underlying individual contact model, the transition between individual states can be simulated through their social activities in multiple scenarios, and the state transition probability of individuals within each preset time period can be calculated. The state transition probability of individuals can be calculated separately for the first contact time period and the second contact time period, thereby obtaining the state transition probability for the entire preset time period.

[0115] In this example implementation, Figure 5 As shown, based on the individual transmission model, infectious disease transmission parameters, and infectious disease intervention parameters, the state transition probability of individuals in each transmission state in the underlying individual contact model within each preset time period is determined, which can specifically include the following steps:

[0116] Step S510: Obtain the transmission status of the individual at the current prediction time point, where the transmission status includes susceptible state, exposed state, infected state, confirmed state and recovered state.

[0117] The current prediction time point refers to the time point at the beginning of the preset time period. In the model, the number of individuals in each propagation state can be initialized at the beginning, and then iterative calculations can be performed through the model.

[0118] Step S520: Determine the intervention action type according to the infectious disease intervention parameter, and determine the target contact scenario in which the individual is located during the first contact time period according to the intervention action type and the individual's social type.

[0119] For different intervention action types, the target contact scenario of an individual during the first contact period may change. For example, for an individual whose social type is a student, if the intervention action type is suspension from school, then his or her daytime activity place will change from school to the community.

[0120] Step S530. Determine the first state transition probability of individuals in each transmission state within the first contact time period based on the infectious disease intervention parameters and the infectious disease transmission parameters in the target contact scenario.

[0121] In this example implementation, Figure 6 As shown, based on the infectious disease intervention parameters and the infectious disease transmission parameters in the target contact scenario, the first state transition probability of individuals in each transmission state within the first contact time period is determined, which can specifically include the following steps:

[0122] Step S610: Determine the first state transition probability of an individual in a susceptible state within a first contact time period based on the infectious disease intervention parameters and the infectious disease transmission parameters in the target contact scenario.

[0123] For the individual transmission model, it is first necessary to calculate the state transition of individuals in the susceptible state.

[0124] In this example implementation, Figure 7 As shown, based on the infectious disease intervention parameters and the infectious disease transmission parameters in the target contact scenario, determining the first state transition probability of an individual in a susceptible state within the first contact time period can specifically include the following steps:

[0125] Step S710: Obtain vaccine efficiency parameters and individual protection parameters of susceptible individuals based on infectious disease intervention parameters.

[0126] Among them, the individual protection parameters can represent the protection rate of an individual when wearing a mask.

[0127] Step S720: Determine the individual contact weight under the target contact scenario according to the infectious disease transmission parameters and individual protection parameters under the target contact scenario.

[0128] In this example implementation, the probability of an individual's contact with an infected individual in a target contact scenario can be obtained based on the individual's social type, and then the individual contact weight in the target contact scenario can be determined based on the infectious disease transmission parameters and individual protection parameters and contact probability in the target contact scenario.

[0129] Individual contact weight θ l The value of is related to the contact location, the age of the individual, and the intervention action performed within time t, l∈L=c,s,w, where L is the set of layers, c,s,w represent the community, school, and workplace respectively. The specific calculation method of individual contact weight is as follows:

[0130] If the individual's contact infection occurs at the community level, θ c It can be expressed as:

[0131]

[0132] Among them, M i ∈[0, 1] is the protection rate of an individual wearing a mask, that is, the individual protection parameter. The smaller the protection rate, the more effective it is. and are indicator variables. The former indicates whether individual i has contact with individual j at the community level, and the latter indicates whether the suspension of work and school policy has an impact on individual i. and is a Bernoulli random variable, This means that individual i has contact with individual j at the community level. This shows that the suspension of work and school policy affects individual i, and vice versa. CR represents the community contact reduction rate, γ i represents the age group to which individual i belongs, cpcm[γ i ] represents the probability that individual i will come into contact with an infected individual in the community. The probability of contact is age-dependent. When individual j in layer l infects individual i at time t, all stored information contributes to the calculation of the reproduction number and the ultimate size of the epidemic. ω = 2, representing the causal multiplier, implies that the risk of community-level infection increases when individual i is away from school (school closures) or working from home.

[0133] If the individual's contact infection occurs at the working level, θ w It can be expressed as:

[0134]

[0135] Among them, cpw[γ i ] represents the probability that individual i comes into contact with infected person j in the working layer.

[0136] If the individual's contact infection occurs at the school level, θ s It can be expressed as:

[0137]

[0138] Among them, cps|γ i ] represents the probability that individual i comes into contact with infected person j at the school level.

[0139] Step S730: Determine the infection probability of an individual in an infected state and the infection probability of an individual in a susceptible state according to the vaccine efficiency parameter.

[0140] The infection probability pri of an individual in the infected state represents the probability that the individual infects others when infected. The infection probability prs of an individual in the susceptible state represents the probability that the individual is infected by other infected persons. The expression of prs is as follows:

[0141] i.prs=(1-i.BVEs)*(1-VEs*EFF[γi])

[0142] Among them, BVEs represents the baseline vaccine efficiency of susceptible individuals, VEs represents the vaccine efficiency of susceptible individuals, and EFF[γi] represents the vaccine efficiency related to the age group. These three parameters are vaccine efficiency parameters.

[0143] Step S740: According to the individual contact weight in the target contact scenario, the infection probability of the individual in the infected state and the infection probability of the individual in the susceptible state, the first state transition probability of the individual in the susceptible state in the first contact time period is obtained.

[0144] In this example implementation, individual i in the susceptible state (S) can be infected by individual j in the infected state (I) and enter the exposed state (E) with a probability λ of:

[0145]

[0146] For individual i, the probability of its state changing from S to E can be expressed as: i =∑ j∈E λ i，j，t .

[0147] Step S620. Based on the infectious disease transmission parameters and the first state transition probabilities of individuals in the susceptible state, the first state transition probabilities of individuals in the exposed state, infected state, confirmed state and recovered state within the first contact time period are determined in sequence.

[0148] After determining the first-state transition probability of individuals in the susceptible state, the first-state transition probability of individuals in the exposed state, infected state, confirmed state, and recovered state during the first contact period can be determined in sequence using the following infectious disease transmission dynamics formula:

[0149]

[0150]

[0151]

[0152]

[0153]

[0154] where μ is the probability of the propagation state E→I, P is the contact probability, and T1 and T2 are the time delays of the propagation state I→A and the propagation state A→R, respectively.

[0155] Step S540. Determine an intermediate prediction time point based on the current prediction time point and the first contact time period, and determine the propagation state of the individual at the intermediate prediction time point based on the first state transition probability.

[0156] The intermediate prediction time point is the time point where the first contact time period intersects with the second contact time period. Based on the individual state transitions during the first contact time period, the propagation state of each individual at this intermediate prediction time point needs to be recalculated.

[0157] Step S550. Determine the second state transition probability of individuals in each transmission state within the second contact time period based on the transmission state of the individual at the intermediate prediction time point, as well as the infectious disease transmission parameters and infectious disease intervention parameters in the community scenario.

[0158] Based on the individual's transmission status at the intermediate prediction time point, the probability of transitioning to the second state during the second contact period, that is, at night, is calculated again. During this second contact period, it is assumed that the individual only interacts in the community setting. The calculation method for the second state transition probability is the same as the calculation method for the first state transition probability in the community setting and is not repeated here.

[0159] In step S140, a predicted value of the infectious disease spread data in each preset time period is obtained based on the state transition probability of the individual in each preset time period.

[0160] Finally, based on the state transition probabilities of individuals within each preset time period, the change in the number of individuals in each transmission state can be calculated. This change in the number of individuals in each transmission state can then be used to predict the spread of infectious diseases within each preset time period. For example, the number of newly exposed and infected people can be calculated each day.

[0161] Infectious diseases such as Figure 8 The figure shows a complete flow chart of a method for predicting infectious disease spread data in a specific embodiment of the present disclosure, which is an example of the above steps in this example embodiment. The specific steps of the flow chart are as follows:

[0162] Step S810: Building the underlying model.

[0163] The content of the underlying model construction mainly includes commuting conditions, location distribution and population structure.

[0164] Among them, the distribution of places mainly includes workplaces, schools and communities, especially the place settings in high-risk areas; the population structure is mainly obtained through the census results and population vector data. The census results include age structure, family size, employment rate, high-risk job employment ratio and other information. The population vector data can be fitted based on administrative district data.

[0165] Step S820. Setting and verifying infectious disease related parameters.

[0166] Infectious disease-related parameters mainly include relevant parameters of infectious disease transmission, viral load, contact probability of different groups, etc.

[0167] Among them, relevant parameters for the spread of infectious diseases may include the basic transmission number R0, the effective transmission rate beta, etc., which can be determined based on the historical infection situation in the local area; the viral load refers to the number of viruses in the body of an infected case, which can be set based on existing research; the probability of contact between different groups can be determined based on information such as age distribution, type of group to which they belong, and degree of contact.

[0168] Step S830. Vaccination background added.

[0169] Vaccination background may include vaccine-related parameters and vaccination status.

[0170] Among them, vaccine-related parameters may include vaccine type, effectiveness, number of injections required, vaccination cycle, etc.; vaccination status may include vaccine coverage, vaccination priority and vaccination time, such as before or during an infectious disease outbreak.

[0171] Step S840: Design and implement intervention actions.

[0172] Intervention actions may include work stoppages, school suspensions, community closures, quarantine policies, mask wearing, and more.

[0173] Among them, the suspension policy may include the suspension ratio and suspension time, and the specific parameters can be adjusted according to actual conditions; the school suspension policy may include the suspension method, such as suspension in medium and high-risk areas or suspension in the entire region; the community closure policy may include the closed community ratio and closure time, and the specific parameters can be adjusted according to actual conditions; the isolation policy may include centralized isolation and home isolation; the mask wearing policy may include the mask wearing ratio and wearing time, and the specific parameters can be adjusted according to actual conditions.

[0174] In this example implementation, by embedding the individual transmission model of an infectious disease into a pre-established underlying individual contact model, an overall individual infectious disease simulation model can be obtained. After the individual infectious disease simulation model is constructed, the parameters can be fitted and verified in combination with real-world infectious disease data, so as to calibrate the model and provide decision-making recommendations, thereby obtaining an individual infectious disease simulation model that can be applied to actual scenarios.

[0175] like Figure 9The following is a specific application scenario of an individual infectious disease simulation model in a specific embodiment of the present disclosure. By inputting the age structure data, family size data, employment data, workplace distribution data, school size distribution data and commuting data of the target area in the real world into the individual simulation model, the state parameters such as the daily number of newly confirmed cases, the cumulative number of confirmed cases, the number of vaccine injections and historical intervention actions in the target area can be obtained. After obtaining the above-mentioned state parameters, they can be input into the intervention action prediction model to obtain the predicted intervention level, and the current intervention action of the target area can be adjusted based on the predicted intervention level. Among them, the intervention action prediction model can be obtained based on the training of the reinforcement learning algorithm.

[0176] In this example implementation, the specific method of inputting the state parameters into the intervention action prediction model to obtain the predicted intervention level is as follows:

[0177] The state parameters are encoded based on the Transformer model in the intervention action prediction model to obtain a first encoding vector; wherein the dimension of the first encoding vector is consistent with the dimension of the first input layer of the intervention action prediction model, and the Transformer model includes a first linear fusion layer, an approximate sparse self-attention layer, a splicing layer and a second linear fusion layer.

[0178] In this example implementation, the structure of the Transformer model is first explained and illustrated. The Transformer model may include an input layer, a first linear fusion layer (Linear), a probable sparse self-attention layer (ProbSparse Self-Attention), a concatenation layer (Contact), a second linear fusion layer (Linear), and an output layer; wherein the input layer, the first linear fusion layer, the probable sparse self-attention layer, the concatenation layer, the second linear fusion layer, and the output layer are sequentially connected.

[0179] Secondly, the state parameters are encoded based on the Transformer model to obtain a first encoding vector, which may specifically include: first, using the first linear fusion layer to linearly fuse the daily number of newly confirmed cases, the cumulative number of confirmed cases, the number of vaccine injections, and historical intervention actions to obtain the current input vector; second, using the approximate sparse self-attention layer to calculate the current input vector to obtain the first importance, second importance, third importance, and fourth importance of the daily number of newly confirmed cases, the cumulative number of confirmed cases, the number of vaccine injections, and the historical intervention actions in the current input vector; then, using the connection layer to connect the daily number of newly confirmed cases, the cumulative number of confirmed cases, the number of vaccine injections, and the historical intervention actions as well as the first importance, second importance, third importance, and fourth importance to obtain the first sub-vector, second sub-vector, third sub-vector, and fourth sub-vector; finally, using the second linear fusion layer to linearly fuse the first sub-vector, the second sub-vector, the third sub-vector, and the fourth sub-vector to obtain the first encoding vector.

[0180] It should be noted here that the number of newly confirmed cases and the cumulative number of confirmed cases are quite different from the number of people injected with the vaccine; therefore, in order to facilitate the extraction and calculation of features, an approximate sparse self-attention layer is used here, which can improve the accuracy of the first importance, second importance, third importance and fourth importance, and ultimately achieve the purpose of improving the accuracy of the predicted intervention level; and, the dimension of the first coding vector is limited here to be consistent with the dimension of the first input layer of the intervention action prediction model, so that when the first coding vector is input into the intervention action prediction model, the model can accurately perform mapping processing according to the first coding vector, and thus obtain the corresponding output result.

[0181] The DQN (Deep Q-Network) model in the intervention action prediction model then maps the first encoded vector to produce an output. Specifically, the DQN model receives the first encoded vector through a three-layer perceptron network, each with 64 nodes in the hidden layer, and maps it to produce an output. This output includes three nodes, one corresponding to the intervention action adjustment method: maintaining the current intervention level unchanged, increasing it by one level, and decreasing it by one level. The level with the highest probability in the output is used as the predicted intervention level.

[0182] In addition, the intervention action prediction model can use other machine learning models to replace the Transformer model for encoding processing, and use other machine learning models to replace the DQN model for mapping processing, which is not specifically limited in this example implementation.

[0183] For example, the output of the intervention action prediction model can include three levels: maintaining the current intervention action unchanged, adjusting the current intervention action by one level, and adjusting the current intervention action by one level. Once the predicted intervention level is determined, the current intervention action for the target area can be adjusted based on the predicted intervention level.

[0184] Specifically, the specific adjustment method of the current intervention action of the target area may include: lowering the current intervention action of the target area by one level; or keeping the current intervention action of the target area unchanged; or raising the current intervention action of the target area by one level.

[0185] The intervention action may indicate whether to perform at least one of the following actions: case isolation, mask wearing, home office, school closure, and community lockdown. In some embodiments, the intervention action may include four levels: the first level is: no case isolation, no need to wear a mask, no need to work from home, no need to close a school, no need to close a community; the second level is: case isolation, mask wearing is required, no need to work from home, no need to close a school, no need to close a community; the third level is: case isolation, mask wearing is required, home office is required, school closure is required, and community lockdown is not required; the fourth level is: case isolation, mask wearing is required, home office is required, school closure is required, and community lockdown is required.

[0186] Based on the above intervention action settings and enable settings, the following four different levels of intervention actions can be set, as shown in Table 1 below:

[0187] Intervention level CI WM WH SC CL 0 × × × × × 1 √ √ × × × 2 √ √ √ √ × 3 √ √ √ √ √

[0188] Among them, in the above Table 1, CI: Case Isolation, case isolation; WM: Wearing Masks, wearing masks; WH: Working from Home, working from home; SC: School Closures, school closures; CL: Community Lockdown, community lockdown.

[0189] In the specific application scenario of the above-mentioned individual infectious disease simulation model, by combining the individual infectious disease simulation model with the intervention action prediction model and utilizing deep reinforcement learning technology, it is possible to automatically learn the optimal intervention action solution, thereby improving the accuracy of the predicted intervention level and solving the problem in the prior art of being unable to predict the intervention action based on the regional status data of a certain area, and thus being unable to adjust the current intervention action in a timely manner according to the predicted intervention action. In addition, since the current intervention action of the target area can be adjusted in a timely manner based on the predicted intervention level, the problem of hindering economic development due to the current intervention action being too strict can be avoided, and the problem of exacerbating infectious diseases due to the current intervention action being too loose can also be avoided. This can both control infectious diseases and protect economic development.

[0190] It should be noted that although the steps of the method of the present disclosure are described in a specific order in the drawings, this does not require or imply that the steps must be performed in this specific order, or that all steps must be performed to achieve the desired results. Additionally or alternatively, some steps may be omitted, multiple steps may be combined into one step, and / or one step may be decomposed into multiple steps.

[0191] Furthermore, the present disclosure also provides a device for predicting infectious disease spread data. Figure 10 As shown, the prediction device for infectious disease propagation data may include an underlying model building module 1010, a propagation model acquisition module 1020, a transition probability determination module 1030, and a propagation data prediction module 1040.

[0192] The underlying model building module 1010 may be used to obtain sociodemographic data and build an underlying individual contact model based on the sociodemographic data and a plurality of preset contact scenarios;

[0193] The transmission model acquisition module 1020 can be used to obtain an individual transmission model of an infectious disease and set relevant infectious disease transmission parameters and infectious disease intervention parameters;

[0194] The transition probability determination module 1030 may be configured to determine the state transition probability of individuals in each transmission state in the underlying individual contact model within each preset time period based on the individual transmission model, the infectious disease transmission parameters, and the infectious disease intervention parameters;

[0195] The propagation data prediction module 1040 can be used to obtain a predicted value of the infectious disease propagation data in each preset time period based on the state transition probability of an individual in each preset time period.

[0196] In some exemplary embodiments of the present disclosure, the underlying model building module 1010 may include a social individual division unit, a contact time period division unit, a contact scene determination unit, and a contact network construction unit.

[0197] The social individual division unit can be used to divide all individuals in the bottom-level individual contact model into different levels of social contact units based on sociodemographic data, and determine the social type of the individual;

[0198] The contact time period division unit may be configured to divide each preset time period into a first contact time period and a second contact time period;

[0199] The contact scene determination unit may be configured to determine a preset contact scene of the individual within the first contact time period according to the social type of the individual, wherein the preset contact scene includes a community scene, a school scene, and a work scene;

[0200] The contact network construction unit can be used to construct the contact network of the individual in each preset contact scenario within the first contact time period, and the contact network of the individual in the community scenario within the second contact time period, and obtain the underlying individual contact model based on each contact network.

[0201] In some exemplary embodiments of the present disclosure, the transition probability determination module 1030 may include a propagation state acquisition unit, a target contact scene determination unit, a first transition probability determination unit, an intermediate propagation state determination unit, and a second transition probability determination unit. Among them:

[0202] The transmission status acquisition unit can be used to obtain the transmission status of the individual at the current prediction time point, wherein the transmission status includes susceptible state, exposed state, infected state, confirmed state and recovered state;

[0203] The target contact scenario determination unit may be configured to determine an intervention action type according to the infectious disease intervention parameter, and determine a target contact scenario in which the individual is located during the first contact time period according to the intervention action type and the individual's social type;

[0204] The first transition probability determination unit may be configured to determine the first state transition probability of individuals in each transmission state within the first contact time period based on the infectious disease intervention parameter and the infectious disease transmission parameter in the target contact scenario;

[0205] The intermediate propagation state determination unit may be configured to determine an intermediate prediction time point based on the current prediction time point and the first contact time period, and determine the propagation state of the individual at the intermediate prediction time point based on the first state transition probability;

[0206] The second transition probability determination unit can be used to determine the second state transition probability of individuals in each transmission state within the second contact time period based on the individual's transmission state at the intermediate prediction time point, as well as the infectious disease transmission parameters and infectious disease intervention parameters in the community scenario.

[0207] In some exemplary embodiments of the present disclosure, the first transition probability determination unit may include a susceptible state transition probability determination unit and other state transition probability determination units.

[0208] The susceptible state transition probability determination unit may be configured to determine a first state transition probability of an individual in a susceptible state within a first contact time period based on infectious disease intervention parameters and infectious disease transmission parameters in a target contact scenario;

[0209] Other state transition probability determination units can be used to determine the first state transition probabilities of individuals in the exposed state, infected state, confirmed state and recovered state within the first contact time period in sequence based on the infectious disease transmission parameters and the first state transition probabilities of individuals in the susceptible state.

[0210] In some exemplary embodiments of the present disclosure, the susceptible state transition probability determination unit may include an infectious disease intervention parameter determination unit, an individual contact weight determination unit, an individual infection probability determination unit, and a first state transition probability determination unit. Among them:

[0211] The infectious disease intervention parameter determination unit may be used to obtain vaccine efficiency parameters and individual protection parameters of individuals in susceptible states according to the infectious disease intervention parameters;

[0212] The individual contact weight determination unit may be used to determine the individual contact weight under the target contact scenario according to the infectious disease transmission parameters and the individual protection parameters under the target contact scenario;

[0213] The individual infection probability determination unit can be used to determine the infection probability of an individual in an infected state and the infection probability of an individual in a susceptible state according to the vaccine efficiency parameter;

[0214] The first state transition probability determination unit can be used to obtain the first state transition probability of an individual in a susceptible state within the first contact time period based on the individual contact weight in the target contact scenario, the individual infection probability in the infected state, and the individual infection probability in the susceptible state.

[0215] In some exemplary embodiments of the present disclosure, the individual contact weight determination unit may include a contact probability determination unit and a contact weight determination unit.

[0216] The contact probability determination unit may be used to obtain the probability of the individual contacting an individual in an infected state in a target contact scenario according to the individual's social type;

[0217] The contact weight determination unit can be used to determine the individual contact weight in the target contact scenario according to the infectious disease transmission parameters and individual protection parameters and contact probability in the target contact scenario.

[0218] In some exemplary embodiments of the present disclosure, the propagation data prediction module 1040 may include an individual quantity change value determination unit and a propagation data prediction value determination unit.

[0219] The individual quantity change value determination unit may be used to obtain individual quantity change values ​​under various propagation states based on the state transition probability of the individual in each preset time period;

[0220] The propagation data prediction value determination unit can be used to obtain the prediction value of the infectious disease propagation data in each preset time period based on the change value of the number of individuals under each propagation state.

[0221] The specific details of each module / unit in the above-mentioned infectious disease spread data prediction device have been described in detail in the corresponding method embodiment part and will not be repeated here.

[0222] Figure 11 A schematic structural diagram of a computer system suitable for implementing an electronic device according to an embodiment of the present invention is shown.

[0223] It should be noted that Figure 11 The computer system 1100 of the electronic device shown is only an example and should not limit the functions and scope of use of the embodiments of the present invention.

[0224] like Figure 11 As shown, the computer system 1100 includes a central processing unit (CPU) 1101, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 1102 or the program loaded from the storage unit 1108 to the random access memory (RAM) 1103. Various programs and data required for system operation are also stored in the RAM 1103. The CPU 1101, ROM 1102, and RAM 1103 are connected to each other via a bus 1104. An input / output (I / O) interface 1105 is also connected to the bus 1104.

[0225] The following components are connected to the I / O interface 1105: an input section 1106 including a keyboard, a mouse, and the like; an output section 1107 including devices such as a cathode ray tube (CRT), a liquid crystal display (LCD), and a speaker; a storage section 1108 including a hard disk; and a communication section 1109 including a network interface card such as a LAN card or a modem. The communication section 1109 performs communication processing via a network such as the Internet. A drive 1110 is also connected to the I / O interface 1105 as needed. Removable media 1111, such as a magnetic disk, an optical disk, a magneto-optical disk, or a semiconductor memory, is installed in the drive 1110 as needed, so that computer programs read therefrom can be installed into the storage section 1108 as needed.

[0226] In particular, according to an embodiment of the present invention, the process described below with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present invention includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network via the communication section 1109, and / or installed from a removable medium 1111. When the computer program is executed by the central processing unit (CPU) 1101, the various functions defined in the system of the present application are performed.

[0227] It should be noted that the computer-readable medium described in the present disclosure may be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or component, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to, an electrical connection having one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, device, or component. In the present disclosure, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, which carries computer-readable program code. This propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium that can transmit, propagate, or transport a program for use by or in conjunction with an instruction execution system, apparatus, or device. Program code embodied on a computer-readable medium may be transmitted using any suitable medium, including but not limited to wireless, wireline, optical fiber cable, RF, or any suitable combination thereof.

[0228] The flowcharts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each box in the flowchart or block diagram can represent a module, program segment, or a part of code, and the above-mentioned module, program segment, or a part of code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in an order different from that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart, and the combination of boxes in the block diagram or flowchart, can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0229] As another aspect, the present application further provides a computer-readable medium, which may be included in the electronic device described in the above embodiments, or may exist independently without being incorporated into the electronic device. The computer-readable medium carries one or more programs, and when the one or more programs are executed by the electronic device, the electronic device implements the method described in the above embodiments.

[0230] It should be noted that although several modules of the device for action execution are mentioned in the detailed description above, this division is not mandatory. In fact, according to the embodiments of the present disclosure, the features and functions of two or more modules described above can be embodied in one module. Conversely, the features and functions of one module described above can be further divided into multiple modules to be embodied.

[0231] Those skilled in the art will readily appreciate other embodiments of the present disclosure after considering the specification and practicing the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the present disclosure that follow the general principles of the present disclosure and include common knowledge or customary techniques in the art not disclosed herein.

[0232] It should be understood that the present disclosure is not limited to the exact structures that have been described above and shown in the drawings, and that various modifications and changes can be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.

Claims

1. A method for predicting infectious disease spread data, characterized in that: include: Obtaining sociodemographic data and constructing an underlying individual contact model based on the sociodemographic data and a plurality of preset contact scenarios; Obtaining an individual transmission model of the infectious disease and setting relevant infectious disease transmission parameters and infectious disease intervention parameters; Determining the state transition probability of individuals in each propagation state in the underlying individual contact model within each preset time period based on the individual propagation model, the infectious disease propagation parameter, and the infectious disease intervention parameter; Obtaining a predicted value of infectious disease spread data within each of the preset time periods based on the state transition probability of the individual within each of the preset time periods; Among them, before setting the relevant infectious disease intervention parameters, it also includes: embedding the individual transmission model of the infectious disease into the underlying individual contact model to obtain an infectious disease individual simulation model, inputting real-world infectious disease data into the infectious disease individual simulation model to obtain state parameters, and inputting the state parameters into the intervention action prediction model to obtain a predicted intervention level, wherein the infectious disease intervention parameters include the intervention action type, and the predicted intervention level is used to adjust the current intervention action type of the target area, and the predicted intervention level includes keeping the current intervention action type unchanged, raising the current intervention action type by one level, and lowering the current intervention action type by one level.

2. The method for predicting infectious disease spread data according to claim 1, characterized in that: The constructing of an underlying individual contact model based on the socio-demographic data and a plurality of preset contact scenarios includes: Dividing all individuals in the bottom-level individual contact model into different levels of social contact units based on the sociodemographic data, and determining the social types of the individuals; Dividing each of the preset time periods into a first contact time period and a second contact time period; Determining, according to the social type of the individual, a preset contact scenario for the individual during the first contact time period, wherein the preset contact scenario includes a community scenario, a school scenario, and a work scenario; Construct a contact network of the individual in each preset contact scenario during the first contact time period, and a contact network of the individual in the community scenario during the second contact time period, and obtain the underlying individual contact model based on each of the contact networks.

3. The method for predicting infectious disease spread data according to claim 2, characterized in that: The determining, based on the individual transmission model, the infectious disease transmission parameter, and the infectious disease intervention parameter, of the state transition probability of individuals in each transmission state in the underlying individual contact model within each preset time period includes: Obtaining the transmission status of the individual at the current prediction time point, wherein the transmission status includes susceptible state, exposed state, infected state, confirmed state, and recovered state; determining an intervention action type according to the infectious disease intervention parameter, and determining a target contact scenario in which the individual is located during the first contact time period according to the intervention action type and the social type of the individual; Determining, based on the infectious disease intervention parameters and the infectious disease transmission parameters in the target contact scenario, a first state transition probability of an individual in each of the transmission states within the first contact time period; Determining an intermediate predicted time point based on the current predicted time point and the first contact time period, and determining the propagation state of the individual at the intermediate predicted time point based on the first state transition probability; Based on the transmission status of the individual at the intermediate prediction time point, as well as the infectious disease transmission parameters and the infectious disease intervention parameters in the community scenario, the second state transition probability of the individual in each of the transmission states within the second contact time period is determined.

4. The method for predicting infectious disease spread data according to claim 3, characterized in that: The determining, based on the infectious disease intervention parameter and the infectious disease transmission parameter in the target contact scenario, a first state transition probability of an individual in each of the transmission states within the first contact time period includes: determining a first state transition probability of the individual in the susceptible state within the first contact time period based on the infectious disease intervention parameter and the infectious disease transmission parameter in the target contact scenario; According to the infectious disease transmission parameters and the first state transition probabilities of individuals in the susceptible state, the first state transition probabilities of individuals in the exposed state, the infected state, the confirmed state and the recovered state within the first contact time period are determined in sequence.

5. The method for predicting infectious disease spread data according to claim 4, characterized in that: The determining, based on the infectious disease intervention parameter and the infectious disease transmission parameter in the target contact scenario, a first state transition probability of the individual in the susceptible state within the first contact time period includes: Obtaining vaccine efficiency parameters and individual protection parameters for the susceptible individual according to the infectious disease intervention parameters; Determining the individual contact weight under the target contact scenario according to the infectious disease transmission parameter and the individual protection parameter under the target contact scenario; Determining the probability of infection of an individual in the infected state and the probability of infection of an individual in the susceptible state according to the vaccine efficiency parameter; According to the individual contact weight in the target contact scenario, the infection probability of the individual in the infected state, and the infection probability of the individual in the susceptible state, the first state transition probability of the individual in the susceptible state within the first contact time period is obtained.

6. The method for predicting infectious disease spread data according to claim 5, characterized in that: The determining of the individual contact weight in the target contact scenario according to the infectious disease transmission parameter and the individual protection parameter in the target contact scenario includes: Obtaining a probability of contact between the individual and the individual in the infected state in the target contact scenario according to the social type of the individual; The individual contact weight under the target contact scenario is determined according to the infectious disease transmission parameters, the individual protection parameters, and the contact probability under the target contact scenario.

7. The method for predicting infectious disease spread data according to claim 1, characterized in that: Obtaining a predicted value of infectious disease spread data within each preset time period based on the state transition probability of the individual within each preset time period includes: Obtaining a change value of the number of individuals in each of the propagation states according to the state transition probability of the individuals in each of the preset time periods; According to the change value of the number of individuals under each of the said propagation states, the predicted value of the infectious disease propagation data within each of the said preset time periods is obtained.

8. A device for predicting infectious disease spread data, characterized in that: include: An underlying model building module, configured to obtain sociodemographic data and build an underlying individual contact model based on the sociodemographic data and a plurality of preset contact scenarios; a propagation model acquisition module, configured to acquire the individual propagation model of the infectious disease and set relevant infectious disease propagation parameters and infectious disease intervention parameters; a transition probability determination module, configured to determine, based on the individual transmission model, the infectious disease transmission parameter, and the infectious disease intervention parameter, the state transition probability of an individual in each transmission state in the underlying individual contact model within each preset time period; a propagation data prediction module, configured to obtain a predicted value of the infectious disease propagation data within each of the preset time periods based on the state transition probability of the individual within each of the preset time periods; Among them, before setting the relevant infectious disease intervention parameters, it also includes: embedding the individual transmission model of the infectious disease into the underlying individual contact model to obtain an infectious disease individual simulation model, inputting real-world infectious disease data into the infectious disease individual simulation model to obtain state parameters, and inputting the state parameters into the intervention action prediction model to obtain a predicted intervention level, wherein the infectious disease intervention parameters include the intervention action type, and the predicted intervention level is used to adjust the current intervention action type of the target area, and the predicted intervention level includes keeping the current intervention action type unchanged, raising the current intervention action type by one level, and lowering the current intervention action type by one level.

9. An electronic device, characterized in that: include: processor; as well as A memory for storing one or more programs, which, when executed by the processor, enables the processor to implement the method for predicting infectious disease spread data as described in any one of claims 1 to 7.

10. A computer-readable medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method for predicting infectious disease spread data according to any one of claims 1 to 7 is implemented.

Images