SIRS-based dynamic network virus propagation construction method and simulation device

Abstract

The invention discloses an SIRS-based dynamic network virus propagation construction method and an SIRS-based dynamic network virus propagation simulation device. Firstly, information contained in nodes in a dynamic network virus propagation model is defined; constructing a square region with a periodic boundary condition, and initializing parameters of nodes; then updating the position information of the nodes, and updating the information of the aerosol propagation area; updating the state information of each node; key indexes serving as prediction results are calculated, wherein the key indexes comprise the infection number of the nodes, the death number of the nodes, the special nodes in the I state and the service nodes. According to the method, on the basis of a propagation model of an original dynamic network, infected crowds and propagation paths are refined, the influence of available medical resources and vaccines on virus propagation is considered, the influence of factors on virus propagation is quantified in numerical value, and therefore theoretical reference is provided for inhibiting virus propagation.

Description

Technical Field

[0001] The present invention belongs to the field of computer technology, and in particular relates to a dynamic network virus propagation model based on SIRS and a virus propagation simulation method and device using the model. Background Art

[0002] The spread of the virus is a complex process involving many factors, including the behavior of the infected, environmental conditions, and the characteristics of the virus. Among them, the behavior patterns of the crowd have an important impact on the spread of the virus. For example, special groups such as super spreaders and service groups such as doctors, police, and food delivery workers have different impacts on the spread of the virus due to their different behavior patterns and contact networks.

[0003] In addition, the way the virus spreads is also an important factor affecting the spread of the virus. In addition to direct interpersonal contact, aerosol transmission is also an important transmission route. Aerosol refers to tiny particles in the air that can carry viruses and spread to distant places through air flow, thereby infecting more people.

[0004] Over the years, simulation research on disease transmission has been developing continuously, but most of the existing models are based on some simplified assumptions. These models often ignore the differences in the transmission ability of different infected people or ignore the transmission routes of the virus. These assumptions may cause the model's prediction results to deviate from the actual situation. Summary of the invention

[0005] In summary, the existing dynamic propagation network based on mobile nodes is not completely consistent with the virus propagation in the real society. In order to be more in line with the actual virus propagation environment, the present invention provides a dynamic network virus propagation simulation method and device based on SIRS. By refining the infected population and propagation pathways on the basis of the original dynamic network propagation model, considering the impact of available medical resources and vaccines on virus propagation, the impact of various factors on virus propagation is numerically quantified, thereby providing a theoretical reference for inhibiting the spread of the virus.

[0006] In order to achieve the above object, the technical solution adopted by the present invention is:

[0007] In one aspect, the present invention provides a method for constructing a dynamic network virus propagation model based on SIRS, comprising the following steps:

[0008] Step 1: First, define the information contained in the nodes in the dynamic network virus propagation model;

[0009] Step 2: Construct a square area with periodic boundary conditions, whose side length is L. There are N nodes in this square area that can move freely. Initialize the node parameters, determine the number of special nodes, service nodes, and vaccination nodes, and set the initial number of infected nodes.

[0010] Step 3: Update the location information of the node and the information of the aerosol propagation area;

[0011] Step 4: Update the status information of each node based on the SIRS propagation algorithm;

[0012] Step 5: Calculate the total number of nodes in state I in time step T, the number of nodes in state I with special symptoms, the number of nodes in state I with service occupations, and the number of nodes that died without receiving effective medical treatment in this time step;

[0013] Step 6: Repeat steps 3 to 5 until the time step length T required to complete a simulation is T = T max , that is, a simulation is completed.

[0014] Furthermore, the specific method of step 1 is as follows:

[0015] The information contained in the nodes in the dynamic network virus propagation model includes:

[0016] x: the current horizontal coordinate of the node;

[0017] y: the current vertical coordinate of the node;

[0018] r: contact radius of the node;

[0019] Status: the status of the node, including: S status, I status, and R status;

[0020] Symptom: the symptom of the node, including: 0 for common symptoms and 1 for special symptoms;

[0021] Carrier: the node's movement mode, including: 0 for normal and 1 for fast;

[0022] Service: The occupation of the node, including: 0 for ordinary occupation, 1 for service occupation;

[0023] Vac: vaccination status of the node, including: 0 for not vaccinated, 1 for vaccinated;

[0024] Among them, the special symptoms of the nodes in the above Symptom include three categories: 1) Compared with ordinary infected people, they have stronger infectious ability, corresponding to people with high viral load in reality; 2) Compared with ordinary infected people, they have a larger contact range, corresponding to people with more interpersonal relationships in reality; 3) Compared with ordinary infected nodes, they have a faster movement speed, corresponding to people who leave the epidemic area to the non-epidemic area in reality.

[0025] The service occupation node in the above-mentioned Service specifically refers to people in the service industry who need to move for a long time, such as couriers and food delivery personnel in real life.

[0026] For the special symptoms 3) above and the nodes whose occupation is service, the carrier is 1, and the carrier of other nodes is 0.

[0027] t: records the time the node is in a certain state;

[0028] The evolution rules of the S state, I state, and R state in virus propagation are defined as follows:

[0029] S→I: infection occurs;

[0030] I→R: after infection for τI time steps;

[0031] R→S: recover after τR time steps;

[0032] Where τI represents the time that the node is in the I state, and τR represents the time that the node is in the R state.

[0033] When a node in state I moves within the region, an aerosol propagation zone will be formed at the path location. The relevant parameters of the propagation zone are as follows:

[0034] x a : The coordinate of the propagation area in the horizontal direction;

[0035] y a : The coordinate of the propagation area in the vertical direction;

[0036] r a : The contact radius of the propagation zone, its default value is half of the contact radius of the corresponding node, that is, 0.5r;

[0037] Symptom_a: the nature of the transmission area, including: common transmission area and special transmission area, corresponding to the aerosol transmission area formed by the nodes of common symptoms and special symptoms respectively;

[0038] t a : Record the duration of the infection area;

[0039] When the aerosol propagation area exists for a period of time ta Greater than or equal to the upper limit of existence time t max When the aerosol propagation area disappears, the upper limit of the existence time t max is a positive integer greater than 0.

[0040] Furthermore, when initializing the parameters of the node in step 2, the method is as follows:

[0041] First, the initial position of the node is randomly generated, the state of the node is uniformly set to the S state, the symptoms of the node are uniformly set to the normal state, the occupation of the node is uniformly set to the normal occupation, the time of the node is uniformly set to 0, and the contact radius of the node is uniformly set to r, where r is an input value, and r is greater than or equal to 0 and less than or equal to 1; secondly, n nodes are randomly selected to set their symptoms to the special state, and m nodes are randomly selected to set their occupations to the service occupation. The symptoms of nodes with service occupations can only be in the normal state; finally, the vaccination state of a certain proportion φ of the nodes is set to vaccinated, and then i(0) nodes are randomly selected, their states are set to the I state and judged. If the occupation of the node is the normal occupation, the contact radius of the node becomes ar, where a is a floating point number greater than 0 and less than 1; if the occupation of the node is the service occupation, the contact radius of the node remains unchanged; if the symptom of the node is a special state, the contact radius of the node becomes γar, where γ is a positive integer greater than 0 and less than 5; for all nodes in the I state, the time of the node is uniformly set to 1.

[0042] Furthermore, step 3 is implemented as follows:

[0043] The position of the node is updated according to the following rules:

[0044] x i (t+Δt)=x i (t)+v i (t)Δt,

[0045] Among them, x i (t) represents the two-dimensional vector of the position of node i in the square area at time t, v i (t) represents the speed of node i at time t, which is calculated as follows:

[0046]

[0047] where v is the modulus of the base velocity of all nodes, θ i (t) is the direction of velocity, which is a random variable, selected between the interval [-π,π] with uniform probability at each time step, g is the rate parameter for the movement of special nodes, selected by a random function between the interval [2,5], δ is the rate parameter for the movement of non-service occupation nodes with state I, δ∈(0,1).

[0048] The updated rules for aerosol transmission areas are as follows:

[0049] x a ,y a Record the x and y values ​​of the node in state I at time T-1 respectively. Symptom_a is consistent with the Symptom of the node in state I at time T-1. a is 0.5 times r of the node in state I at time T-1. a Add 1, when t a ≥t max When the aerosol transmission area disappears.

[0050] Furthermore, the specific method of step 4 is as follows:

[0051] Each node has a contact radius r. If a susceptible node stays within the radius coverage of an infected node, the susceptible node has a probability of being infected. Similarly, if a susceptible node stays within the coverage of the aerosol transmission area, it also has a probability of being infected. The calculation rules of infection probability and contact radius change are as follows:

[0052] First, define the basic infection probability:

[0053]

[0054] Where p is the basic probability of being infected by the node, p a is the basic probability of infection in the aerosol transmission area, β is the infection rate of the virus, r ij and r ik represents the distance between susceptible node i and infected node j, and aerosol transmission area, respectively; m and n represent the number of infected nodes and aerosol transmission areas within the contact range of susceptible node i, respectively; if susceptible node i has not been vaccinated, the probability of being infected by a node with common symptoms and aerosol transmission area is p and p respectively. a The probability of being a node with special symptoms and a special aerosol transmission area is bp and bp respectively. a ;

[0055] If node i has been vaccinated, the probability of being infected by a node with common symptoms and a common aerosol transmission area is αp and αp respectively. a The probability of being infected by a node with special symptoms and a special aerosol transmission area is bαp and bαp respectively. a , b is the infection rate parameter of nodes with special symptoms and special aerosol transmission areas; α is the infection rate parameter of vaccinated nodes.

[0056] Next, define the contact radius:

[0057]

[0058] Where γ is the multiplier parameter of the contact radius of special nodes, and a is the multiplier parameter of the contact radius of nodes of non-service occupations with status I.

[0059] The status of a node is updated according to the following rules:

[0060] if i (t)=0,Ω i (t) = S,

[0061] if i (t)∈(1,τ I ),Ω i (t)=I,

[0062] if i (t)∈(τ I ,τ R ),Ω i (t) = R,

[0063] where τ i (t) represents the time count value of node i at time t, Ω i (t) represents the state of node i at time t; S, I, and R represent the susceptible state, infected state, and recovered state, respectively. I ,τ R Respectively represent the length of time the node is in the I state and the R state. Each node has a corresponding time counter. The time counter will be accumulated according to the current state of the node. If the node is in the S state, it will be increased by 0. If the node is in the I state or the R state, it will be increased by 1 each time until τ i (t) = τ R , at which time the node state is reset to S state and the time counter is reset to zero.

[0064] Furthermore, the calculation formula of the number of node deaths is as follows:

[0065] d(t)=(i sup +i ser +i nor )×[i(t)-h] / i(t),

[0066] Among them, the i sup is the number of special symptom infection nodes at time t, which only includes common occupations, corresponding to super spreaders in imported cases in reality; ser is the number of service occupation infection nodes at time t, and the service occupation infection nodes only contain common symptoms, corresponding to the local service industry personnel in reality;nor is the number of common symptom infection nodes at time t (common occupation), i(t) is the number of all infected nodes at time t, h is the available medical resources, and h is a floating point number greater than or equal to 0 and less than or equal to 1.

[0067] The calculation of the death number d(t) of the node is performed only when i(t) is greater than or equal to h, otherwise the value of d(t) at time t is 0.

[0068] On the other hand, the present invention also provides a dynamic network virus propagation simulation device based on SIRS, comprising the following modules:

[0069] 1) Parameter input module: used to receive input parameters, including: the side length L of the square area with periodic boundary conditions, the total number of nodes N, the modulus v of the basic velocity of the node, the contact radius r of the node, and the time parameter τ I , τ R , simulation times t', initial number of infected nodes i(0), number of special nodes n, number of service nodes m, vaccine coverage φ, available medical resources h, multiplier parameter α of susceptible nodes vaccinated to be infected, multiplier parameters a, b, δ, γ;

[0070] 2) Model construction module: used to construct a dynamic network virus propagation model according to the input parameters received by the parameter input module and complete the initialization of node information;

[0071] 3) Location update module: used to update the location information of each node in the dynamic network virus propagation model and the location information of the aerosol propagation area;

[0072] 4) Simulation infection module: used to simulate the dynamic network virus propagation model constructed according to SIRS, including direct transmission between nodes and aerosol transmission;

[0073] 5) Index calculation module: used to calculate the key indicators used as prediction results, including: the number of infected nodes, the number of dead nodes, special nodes in state I, and service nodes;

[0074] 6) Visualization module: used to visualize the data obtained by the indicator calculation module.

[0075] The beneficial effects of the present invention are as follows:

[0076] Based on the existing model, this invention refines the differences in the transmission population and the transmission mode of the virus, making the simulation more in line with the real environment. By refining the infected population and transmission pathways based on the original dynamic network transmission model, considering the impact of available medical resources and vaccines on virus transmission, and numerically quantifying the impact of various factors on virus transmission, it provides a theoretical reference for inhibiting the spread of the virus. BRIEF DESCRIPTION OF THE DRAWINGS

[0077] Figure 1 This is a dynamic virus propagation model diagram of the present invention.

[0078] Figure 2 The change in the initial number ratio of super-spreaders with different characteristics of the device of the present invention affects the infection diagram (a. the input super-spreader has strong infectivity; b. the input super-spreader has a large contact range; c. the input super-spreader has a high movement rate).

[0079] Figure 3 It is a comparison chart of the number of people infected by aerosol transmission of the device of the present invention.

[0080] Figure 4 It is a comparison chart of the number of infected persons of the device of the present invention with a service node.

[0081] Figure 5 It is a snapshot diagram of the model at each time step of the method of the present invention. DETAILED DESCRIPTION

[0082] The present invention is further described below with reference to the accompanying drawings and examples. The present invention includes but is not limited to the following examples.

[0083] like Figure 1 As shown, the present invention provides a method for constructing a dynamic network virus transmission model based on SIRS, and the specific implementation process is as follows:

[0084] Step 1: Input the relevant data for model construction and the data of the virus transmission population, including the side length L of the square area with periodic boundary conditions, the total number of nodes N, the modulus v of the node's basic velocity, the contact radius r of the node, and the time parameter τ I , τ R , simulation times t', initial number of infected nodes i(0), number of special nodes n, number of service nodes m, vaccine coverage φ, available medical resources h, multiplier parameter α of susceptible nodes vaccinated to be infected, multiplier parameters a, b, δ, γ;

[0085] Step 2: Construct a dynamic network virus propagation model according to the parameters specified in step 1 and complete the initialization of node information. Since the present invention takes into account the propagation capabilities of different groups of people, it is necessary to calculate the corresponding contact radius r according to the node information when initializing the node. The contact radius of the node satisfies the following formula:

[0086]

[0087] Step 3: Update the location information of the node and record the location information and symptoms of the I state node before the update as the aerosol transmission area. The contact radius of the aerosol transmission area is 0.5r. When the time counter of the aerosol transmission area is greater than or equal to t max When , the record of the propagation area is deleted, and the position of node i at time t+1 is:

[0088] x i (t+Δt)=x i (t)+v i (t)Δt,

[0089] The velocity of node i at time t is:

[0090]

[0091] where v is the modulus of the base velocity of all nodes, θ i (t) is the direction of velocity, which is a random variable, selected between the interval [-π,π] with uniform probability at each time step, g is the rate parameter for the movement of special nodes, selected by a random function between the interval [2,5], δ is the rate parameter for the movement of non-service occupation nodes with state I, δ∈(0,1).

[0092] Step 4: Based on the node location information obtained in step 3, simulate the virus transmission that occurs during interpersonal contact. The basic infection probability of the node is calculated by the following formula:

[0093]

[0094] If node i has not been vaccinated, the probability of node i being infected is:

[0095]

[0096] If node i has been vaccinated, the probability of node i being infected is:

[0097]

[0098] Aerosol transmission simulation is performed based on the aerosol transmission area information recorded in step 3, and the basic infection probability of the node is calculated by the following formula:

[0099]

[0100] If node i has not been vaccinated, the probability of node i being infected is:

[0101]

[0102] If node i has been vaccinated, the probability of node i being infected is:

[0103]

[0104] Step 5: Draw a model snapshot diagram based on the node status updated in step 4, such as Figure 5 As shown, the total number of infections in this simulation, the number of infections in special states, the number of infections in service occupations, and the number of deaths are recorded at the same time. The number of deaths is calculated by the following formula:

[0105] d(t)=(i sup +i ser +i nor )×[i(t)-h] / i(t),

[0106] Among them, the i sup is the number of special symptom infection nodes at time t, which only includes common occupations, corresponding to super spreaders in imported cases in reality; ser is the number of service occupation infection nodes at time t, and the service occupation infection nodes only contain common symptoms, corresponding to the local service industry personnel in reality; nor is the number of common symptom infection nodes at time t (common occupation), i(t) is the number of all infected nodes at time t, h is the available medical resources, and h is a floating point number greater than or equal to 0 and less than or equal to 1.

[0107] Step 6: Repeat steps 3 to 5 until t' times, calculate the average number of total infections, number of infections in special states, number of infections in service occupations, and number of deaths in each time step in t' simulations, and draw a graph of the changes in the number of infections based on the value.

[0108] A dynamic network virus propagation simulation device based on SIRS, comprising the following modules:

[0109] 1) Parameter input module: used to receive input parameters, including: the side length L of the square area with periodic boundary conditions, the total number of nodes N, the modulus v of the basic velocity of the node, the contact radius r of the node, and the time parameter τ I , τ R, simulation times t', initial number of infected nodes i(0), number of special nodes n, number of service nodes m, vaccine coverage φ, available medical resources h, multiplier parameter α of susceptible nodes vaccinated to be infected, multiplier parameters a, b, δ, γ;

[0110] 2) Model construction module: used to construct a dynamic network virus propagation model according to the input parameters received by the parameter input module and complete the initialization of node information;

[0111] The information contained in the nodes in the dynamic network virus propagation model includes:

[0112] x: the current horizontal coordinate of the node;

[0113] y: the current vertical coordinate of the node;

[0114] r: contact radius of the node;

[0115] Status: the status of the node, including: S status, I status, and R status;

[0116] Symptom: the symptom of the node, including: 0 for common symptoms and 1 for special symptoms;

[0117] Carrier: the node's movement mode, including: 0 for normal and 1 for fast;

[0118] Service: The occupation of the node, including: 0 for ordinary occupation, 1 for service occupation;

[0119] Vac: vaccination status of the node, including: 0 for not vaccinated, 1 for vaccinated;

[0120] Among them, the special symptoms of the nodes in the above Symptom include three categories: 1) Compared with ordinary infected people, they have stronger infectious ability, corresponding to people with high viral load in reality; 2) Compared with ordinary infected people, they have a larger contact range, corresponding to people with more interpersonal relationships in reality; 3) Compared with ordinary infected nodes, they have a faster movement speed, corresponding to people who leave the epidemic area to the non-epidemic area in reality.

[0121] The service occupation node in the above-mentioned Service specifically refers to people in the service industry who need to move for a long time, such as couriers and food delivery personnel in real life.

[0122] For the special symptoms 3) above and the nodes whose occupation is service, the carrier is 1, and the carrier of other nodes is 0.

[0123] t: records the time the node is in a certain state;

[0124] The evolution rules of the S state, I state, and R state in virus propagation are defined as follows:

[0125] S→I: infection occurs;

[0126] I→R: after infection for τI time steps;

[0127] R→S: recover after τR time steps;

[0128] Where τI represents the time that the node is in the I state, and τR represents the time that the node is in the R state.

[0129] When a node in state I moves within the region, an aerosol propagation zone will be formed at the path location. The relevant parameters of the propagation zone are as follows:

[0130] x a : The coordinate of the propagation area in the horizontal direction;

[0131] y a : The coordinate of the propagation area in the vertical direction;

[0132] r a : The contact radius of the propagation zone, its default value is half of the contact radius of the corresponding node, that is, 0.5r;

[0133] Symptom_a: the nature of the transmission area, including: common transmission area and special transmission area, corresponding to the aerosol transmission area formed by the nodes of common symptoms and special symptoms respectively;

[0134] t a : Record the duration of the infection area;

[0135] When the aerosol propagation area exists for a period of time t a Greater than or equal to the upper limit of existence time t max When the aerosol propagation area disappears, the upper limit of the existence time t max is a positive integer greater than 0.

[0136] When initializing the parameters of the node, do the following:

[0137] First, the initial position of the node is randomly generated, the state of the node is uniformly set to the S state, the symptoms of the node are uniformly set to the normal state, the occupation of the node is uniformly set to the normal occupation, the time of the node is uniformly set to 0, and the contact radius of the node is uniformly set to r, where r is an input value, and r is greater than or equal to 0 and less than or equal to 1; secondly, n nodes are randomly selected to set their symptoms to the special state, and m nodes are randomly selected to set their occupations to the service occupation. The symptoms of nodes with service occupations can only be in the normal state; finally, the vaccination state of a certain proportion φ of the nodes is set to vaccinated, and then i(0) nodes are randomly selected, their states are set to the I state and judged. If the occupation of the node is the normal occupation, the contact radius of the node becomes ar, where a is a floating point number greater than 0 and less than 1; if the occupation of the node is the service occupation, the contact radius of the node remains unchanged; if the symptom of the node is a special state, the contact radius of the node becomes γar, where γ is a positive integer greater than 0 and less than 5; for all nodes in the I state, the time of the node is uniformly set to 1.

[0138] 3) Location update module: used to update the location information of each node in the dynamic network virus propagation model and the location information of the aerosol propagation area;

[0139] The position of the node is updated according to the following rules:

[0140] x i (t+Δt)=x i (t)+v i (t)Δt,

[0141] Among them, x i (t) represents the two-dimensional vector of the position of node i in the square area at time t, v i (t) represents the speed of node i at time t, which is calculated as follows:

[0142]

[0143] where v is the modulus of the base velocity of all nodes, θ i (t) is the direction of velocity, which is a random variable, selected between the interval [-π,π] with uniform probability at each time step, g is the rate parameter for the movement of special nodes, selected by a random function between the interval [2,5], δ is the rate parameter for the movement of non-service occupation nodes with state I, δ∈(0,1).

[0144] The updated rules for aerosol transmission areas are as follows:

[0145] x a ,y aRecord the x and y values ​​of the node in state I at time T-1 respectively. Symptom_a is consistent with the Symptom of the node in state I at time T-1. a is 0.5 times r of the node in state I at time T-1. a Add 1, when t a ≥t max When the aerosol transmission area disappears.

[0146] 4) Simulation infection module: used to simulate the dynamic network virus propagation model constructed according to SIRS, including direct transmission between nodes and aerosol transmission;

[0147] Each node has a contact radius r. If a susceptible node stays within the radius coverage of an infected node, the susceptible node has a probability of being infected. Similarly, if a susceptible node stays within the coverage of the aerosol transmission area, it also has a probability of being infected. The calculation rules of infection probability and contact radius change are as follows:

[0148] First, define the basic infection probability:

[0149]

[0150] Where p is the basic probability of being infected by the node, p a is the basic probability of infection in the aerosol transmission area, β is the infection rate of the virus, r ij and r ik represents the distance between susceptible node i and infected node j, and aerosol transmission area, respectively; m and n represent the number of infected nodes and aerosol transmission areas within the contact range of susceptible node i, respectively; if susceptible node i has not been vaccinated, the probability of being infected by a node with common symptoms and aerosol transmission area is p and p respectively. a The probability of being a node with special symptoms and a special aerosol transmission area is bp and bp respectively. a ;

[0151] If node i has been vaccinated, the probability of being infected by a node with common symptoms and a common aerosol transmission area is αp and αp respectively. a The probability of being infected by a node with special symptoms and a special aerosol transmission area is bαp and bαp respectively. a , b is the infection rate parameter of nodes with special symptoms and special aerosol transmission areas; α is the infection rate parameter of vaccinated nodes.

[0152] Next, define the contact radius:

[0153]

[0154] Where γ is the multiplier parameter of the contact radius of special nodes, and a is the multiplier parameter of the contact radius of nodes of non-service occupations with status I.

[0155] The status of a node is updated according to the following rules:

[0156] if i (t)=0,Ω i (t) = S,

[0157] if i (t)∈(1,τ I ),Ω i (t)=I,

[0158] if i (t)∈(τ I ,τ R ),Ω i (t) = R,

[0159] where τ i (t) represents the time count value of node i at time t, Ω i (t) represents the state of node i at time t; S, I, and R represent the susceptible state, infected state, and recovered state, respectively. I ,δ R Respectively represent the length of time the node is in the I state and the R state. Each node has a corresponding time counter. The time counter will be accumulated according to the current state of the node. If the node is in the S state, it will be increased by 0. If the node is in the I state or the R state, it will be increased by 1 each time until τ i (t) = τ R , at which time the node state is reset to S state and the time counter is reset to zero.

[0160] 5) Index calculation module: used to calculate the key indicators used as prediction results, including: the number of infected nodes, the number of dead nodes, special nodes in state I, and service nodes;

[0161] The calculation formula of the number of deaths of the node is as follows:

[0162] d(t)=(i sup +i ser +i nor )×[i(t)-h] / i(t),

[0163] Among them, the i sup is the number of special symptom infection nodes at time t, which only includes common occupations, corresponding to super spreaders in imported cases in reality; seris the number of service occupation infection nodes at time t, and the service occupation infection nodes only contain common symptoms, corresponding to the local service industry personnel in reality; nor is the number of common symptom infection nodes at time t (common occupation), i(t) is the number of all infected nodes at time t, h is the available medical resources, and h is a floating point number greater than or equal to 0 and less than or equal to 1.

[0164] The calculation of the death number d(t) of the node is performed only when i(t) is greater than or equal to h, otherwise the value of d(t) at time t is 0.

[0165] 6) Visualization module: used to visualize the data obtained by the indicator calculation module.

[0166] Repeat the simulation t' times, calculate the average number of total infections, number of infections in special states, number of infections in service occupations, and number of deaths at each time step in t' simulations, and draw a graph of the changes in the number of infections based on the value.

[0167] Example

[0168] In a square area with a side length of L and periodic boundary conditions, there are N = 1000 nodes that can be moved arbitrarily, of which there are n = 10 special nodes, m = 0 service nodes, and i(0) = 10 infected nodes at the initial moment. For example, there are i infected nodes at the initial moment. s (0) are special infection nodes, indicating super spreaders imported from outside. Figure 2 The three figures a, b, and c in the figure correspond to the three hypothetical cases i s (0) The number of infected people from 0 to 10, the three hypothetical situations correspond to the following: a. When the imported super-spreader has a stronger infectious ability; b. When the imported super-spreader has a larger contact range; c. When the super-spreader has a faster moving speed. The transmission mode of the three hypothetical situations is direct transmission, and aerosol transmission is not considered. When only the transmission mode is considered, that is, n=0, m=0, the number of infected people who can be transmitted by aerosols and the number of infected people who can only be transmitted by direct contact are as follows Figure 3 shown.

[0169] The number of service nodes is set to 10, and the number of external input super spreaders is 5, that is, i s (0) = 5 simulates the situation where there are both imported super-spreaders and local super-spreaders. The transmission modes consider direct transmission and aerosol transmission, where b = g = 1, γ = 3. The impact on virus transmission is as follows: Figure 4 shown.

[0170] In summary, the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to set limitations. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that the technical solutions described in the above embodiments may still be modified, or some of the technical features may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for constructing a dynamic network virus propagation model based on SIRS, characterized in that: The steps include: Step 1: First, define the information contained in the nodes in the dynamic network virus propagation model; Step 2: Construct a square area with periodic boundary conditions, whose side length is L. In this square area, there are N nodes that can move freely at will. Initialize the parameters of the nodes, determine the number of special nodes, service nodes, and vaccination nodes, and set the initial number of infected nodes; Step 3: Update the location information of the node and the information of the aerosol propagation area; Step 4: Update the status information of each node based on the SIRS propagation algorithm; Step 5: Calculate the total number of nodes in state I in time step T, the number of nodes in state I with special symptoms, the number of nodes in state I with service occupations, and the number of nodes that died without receiving effective medical treatment in this time step; Step 6: Repeat steps 3 to 5 until the time step length T required to complete a simulation is T = T max , that is, a simulation is completed.

2. The method for constructing a dynamic network virus propagation model based on SIRS according to claim 1, characterized in that: Step 1: The information contained in the nodes in the dynamic network virus propagation model includes: x: the current horizontal coordinate of the node; y: the current vertical coordinate of the node; r: contact radius of the node; Status: the status of the node, including: S status, I status, and R status; Symptom: the symptom of the node, including: 0 for common symptoms and 1 for special symptoms; Carrier: the node's movement mode, including: 0 for normal and 1 for fast; Service: The occupation of the node, including: 0 for ordinary occupation, 1 for service occupation; Vac: vaccination status of the node, including: 0 for not vaccinated, 1 for vaccinated; Among them, the special symptoms of the nodes in the above Symptom include three categories: 1) Compared with ordinary infected people, they have stronger infectious ability, corresponding to people with high viral load in reality; 2) Compared with ordinary infected people, they have a larger contact range, corresponding to people with more interpersonal relationships in reality; 3) Compared with ordinary infected nodes, they have a faster movement speed, corresponding to people who leave the epidemic area to the non-epidemic area in reality; Regarding the service occupation node in the above Service, it specifically refers to people in the service industry who need to move for a long time; For the special symptoms 3) above and the nodes whose occupation is service, the carrier is 1, and the carrier of other nodes is 0. t: records the time the node is in a certain state; The evolution rules of the S state, I state, and R state in virus propagation are defined as follows: S→I: infection occurs; I→R: after infection for τI time steps; R→S: recover after τR time steps; Where τI represents the duration that the node is in the I state, and τR represents the duration that the node is in the R state; When a node in state I moves within the region, an aerosol propagation zone will be formed at the path location. The relevant parameters of the propagation zone are as follows: x a : The coordinate of the propagation area in the horizontal direction; y a : The coordinate of the propagation area in the vertical direction; r a : The contact radius of the propagation zone, its default value is half of the contact radius of the corresponding node, that is, 0.5r; Symptom_a: the nature of the transmission area, including: common transmission area and special transmission area, corresponding to the aerosol transmission area formed by the nodes of common symptoms and special symptoms respectively; t a : Record the duration of the infection area; When the aerosol propagation area exists for a period of time t a Greater than or equal to the upper limit of existence time t max When the aerosol propagation area disappears, the upper limit of the existence time t max is a positive integer greater than 0.

3. The method for constructing a dynamic network virus propagation model based on SIRS according to claim 2, characterized in that: In step 2, when initializing the node parameters, do the following: First, the initial position of the node is randomly generated, the state of the node is uniformly set to the S state, the symptoms of the node are uniformly set to the normal state, the occupation of the node is uniformly set to the normal occupation, the time of the node is uniformly set to 0, and the contact radius of the node is uniformly set to r, where r is an input value, and r is greater than or equal to 0 and less than or equal to 1; secondly, n nodes are randomly selected to set their symptoms to the special state, and m nodes are randomly selected to set their occupations to the service occupation. The symptoms of nodes with service occupations can only be in the normal state; finally, the vaccination state of a certain proportion φ of the nodes is set to vaccinated, and then i(0) nodes are randomly selected, their states are set to the I state and judged. If the occupation of the node is the normal occupation, the contact radius of the node becomes ar, where a is a floating point number greater than 0 and less than 1; if the occupation of the node is the service occupation, the contact radius of the node remains unchanged; if the symptom of the node is a special state, the contact radius of the node becomes γar, where γ is a positive integer greater than 0 and less than 5; for all nodes in the I state, the time of the node is uniformly set to 1.

4. A method for constructing a dynamic network virus propagation model based on SIRS according to claim 2 or 3, characterized in that: Step 3 is implemented as follows: The position of the node is updated according to the following rules: x i (t+Δt)=x i (t)+v i (t)Δt, Among them, x i (t) represents the two-dimensional vector of the position of node i in the square area at time t, v i (t) represents the speed of node i at time t, which is calculated as follows: where v is the modulus of the base velocity of all nodes, θ i (t) is the direction of the speed, which is a random variable, selected between the interval [-π,π] with uniform probability at each time step, g is the rate parameter when the special node moves, selected by a random function between the interval [2,5], δ is the rate parameter when the node of the non-service occupation with state I moves, δ∈(0,1); The updated rules for aerosol transmission areas are as follows: x a ,y a Record the x and y values ​​of the node in state I at time T-1 respectively. Symptom_a is consistent with the Symptom of the node in state I at time T-1. a is 0.5 times r of the node in state I at time T-1; after each time step, the time counter t of the aerosol propagation area a Add 1, when t a ≥t max When the aerosol transmission area disappears.

5. The method for constructing a dynamic network virus propagation model based on SIRS according to claim 4, characterized in that: Step 4: Each node has a contact radius r. If a susceptible node stays within the radius coverage of an infected node, the susceptible node has a probability of being infected. Similarly, if a susceptible node stays within the coverage of the aerosol transmission area, it also has a probability of being infected. The calculation rules of infection probability and contact radius change are as follows: First, define the basic infection probability: Where p is the basic probability of being infected by the node, p a is the basic probability of infection in the aerosol transmission area, β is the infection rate of the virus, r ij and r ik represents the distance between susceptible node i and infected node j, and aerosol transmission area, respectively; m and n represent the number of infected nodes and aerosol transmission areas within the contact range of susceptible node i, respectively; if susceptible node i has not been vaccinated, the probability of being infected by a node with common symptoms and aerosol transmission area is p and p respectively. a The probability of being a node with special symptoms and a special aerosol transmission area is bp and bp respectively. a ; If node i has been vaccinated, the probability of being infected by a node with common symptoms and a common aerosol transmission area is αp and αp respectively. a The probability of being infected by a node with special symptoms and a special aerosol transmission area is bαp and bαp respectively. a , b is the infection rate parameter of nodes with special symptoms and special aerosol transmission areas; α is the infection rate parameter of nodes that have been vaccinated; Next, define the contact radius: Where γ is the multiplier parameter of the contact radius of special nodes, and a is the multiplier parameter of the contact radius of nodes of non-service occupations with status I; The status of a node is updated according to the following rules: ifτ i (t)=0,Ω i (t)=S, if t i (t)∈(1,τ I ),Oh i (t)=I, if t i (t)∈(τ I ,t R ),Oh i (t)=R, where τ i (t) represents the time count value of node i at time t, Ω i (t) represents the state of node i at time t; S, I, and R represent the susceptible state, infected state, and recovered state, respectively. I ,τ R Respectively represent the length of time the node is in the I state and the R state. Each node has a corresponding time counter. The time counter will be accumulated according to the current state of the node. If the node is in the S state, it will be increased by 0. If the node is in the I state or the R state, it will be increased by 1 each time until τ i (t) = τ R , at which time the node state is reset to S state and the time counter is reset to zero.

6. A method for constructing a dynamic network virus propagation model based on SIRS according to claim 1 or 5, characterized in that: The calculation formula of the number of deaths of the node is as follows: d(t)=(i sup +i ser +i nor )×[i(t)-h] / i(t), Among them, the i sup is the number of nodes infected with special symptoms at time t; ser is the number of service occupation infection nodes at time t; nor is the number of common symptom infection nodes of common occupations at time t, i(t) is the number of all infection nodes at time t, h is the available medical resources, and h is a floating point number greater than or equal to 0 and less than or equal to 1; The calculation of the death number d(t) of the node is performed only when i(t) is greater than or equal to h, otherwise the value of d(t) at time t is 0.

7. The SIRS-based dynamic network virus propagation simulation device according to claim 6, characterized in that: Includes the following modules: 1) Parameter input module: used to receive input parameters, including: the side length L of the square area with periodic boundary conditions, the total number of nodes N, the modulus v of the basic velocity of the node, the contact radius r of the node, and the time parameter τ I , τ R , simulation times t', initial number of infected nodes i(0), number of special nodes n, number of service nodes m, vaccine coverage φ, available medical resources h, multiplier parameter α of susceptible nodes vaccinated to be infected, multiplier parameters a, b, δ, γ; 2) Model construction module: used to construct a dynamic network virus propagation model according to the input parameters received by the parameter input module and complete the initialization of node information; 3) Location update module: used to update the location information of each node in the dynamic network virus propagation model and the location information of the aerosol propagation area; 4) Simulation infection module: used to simulate the dynamic network virus propagation model constructed according to SIRS, including direct transmission between nodes and aerosol transmission; 5) Index calculation module: used to calculate the key indicators used as prediction results, including: the number of infected nodes, the number of dead nodes, special nodes in state I, and service nodes; 6) Visualization module: used to visualize the data obtained by the indicator calculation module.

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