CN114373542A - Behavior pattern-based SEIR infection risk simulation method - Google Patents

Behavior pattern-based SEIR infection risk simulation method Download PDF

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CN114373542A
CN114373542A CN202111523055.7A CN202111523055A CN114373542A CN 114373542 A CN114373542 A CN 114373542A CN 202111523055 A CN202111523055 A CN 202111523055A CN 114373542 A CN114373542 A CN 114373542A
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赵生捷
劉政杰
邓浩
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Tongji University
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    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
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Abstract

The invention relates to an SEIR infection risk simulation method based on a behavior pattern, which divides epidemic situation data into data with different scales through a time sequence network, takes families as network nodes, determines the connection relation of the network nodes based on the behavior pattern, constructs a crowd contact network, simulates each stage of epidemic situation propagation by adopting an SEIR model, and determines the daily epidemic situation propagation condition. Compared with the prior art, the method has the advantages of high simulation authenticity and high accuracy.

Description

Behavior pattern-based SEIR infection risk simulation method
Technical Field
The invention relates to the field of flexible cities, in particular to an SEIR infection risk simulation method based on a behavior pattern.
Background
A tough city (Urban resilience), which refers to the ability of a city to resist disasters, recover quickly after a disaster, and maintain the Urban function. In modern cities, the loss and influence caused by each disaster are huge, which highlights the importance of the tough cities. However, for a tough city, when various disasters including epidemic situations, floods, hurricanes and the like are faced, the control and prevention of the disasters are very important, and how to timely and effectively resist or pertinently avoid the disasters in advance is a problem to be solved urgently.
While many people have simulated the virus propagation process by using models such as SIR and SEIR and have a certain fitting effect in the process, the simulated numerical values are much lower than the actual numerical values when the contact between nodes is small in the initial simulation stage, which is not good for the control of the initial epidemic outbreak and may cause the spread of the epidemic by underestimating the numerical values in the initial stage.
Aiming at the problem of low epidemic propagation simulation precision, a high-precision epidemic infection risk simulation method needs to be designed.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide a high-accuracy SEIR infection risk simulation method based on a behavior pattern.
The purpose of the invention can be realized by the following technical scheme:
according to the first aspect of the invention, the SEIR infection risk simulation method based on the behavior pattern is provided, epidemic situation data are divided into data with different scales through a time sequence network, a family is taken as a network node, the network node connection relation is determined based on the behavior pattern, a crowd contact network is constructed, an SEIR model is adopted to simulate each stage of epidemic situation propagation, and the daily epidemic situation propagation condition is determined.
Preferably, the epidemic data comprises time data and space data; the time data is divided into long-time data and short-time data according to time, and the space data is divided into large space data and small space data according to space; and combining the long-time data and the large-space data into large-scale data, and combining the short-time data and the small-space data into small-scale data.
Preferably, the time-series network is a complex network in which node connection edges only move at a specific time, each connection edge carries information of when the node moves, and the connection edges in the time-series network appear or disappear along with the change of time.
Preferably, the determining the network node connection relationship based on the behavior pattern specifically includes: contact nodes and execution probabilities are determined based on the behavior patterns.
Preferably, the behavioral patterns include unleashed, learned, work, entertainment, and market; the learning and working is a fixed contact node; the entertainment and marketplace is a random contact node.
Preferably, the fixed contact node is specifically configured as: and when the number of the contacted different nodes reaches a set threshold value, stopping contacting other nodes which are not contacted yet.
Preferably, the stages of the spread of the epidemic include susceptible, latent, infected and convalescent.
Preferably, the simulation of each stage of epidemic propagation by using the SEIR model specifically includes:
the susceptible person has a latent period at the beginning, and symptoms appear after a period of time and become an infected person; once becoming a rehabilitative person, there is no re-infection, i.e. in the process of probability transmission, once becoming a rehabilitative person, there is no probability of re-transferring to an infected person or a susceptible person; the differential equation of the SEIR model is as follows:
Figure BDA0003408478010000021
Figure BDA0003408478010000022
Figure BDA0003408478010000023
Figure BDA0003408478010000024
wherein S, E, I, R represents the number of susceptible persons, latent persons, infected persons and convalescent persons, r represents the number of infected persons contacting susceptible persons, and t represents the propagation time; alpha is latent one converted into infected oneThe probability of infection by the contact of a susceptible person and an infected person, beta1The probability of the susceptible patient and the latent patient being infected, gamma is the probability of the infected patient recovering, and mu is the probability of the infected patient being isolated; n is the total number of nodes;
and based on the Markov chain, defining the state of the next day to be only related to the state of the previous day, and combining the differential equation to obtain the epidemic situation spread condition of each day.
Preferably, the nth epidemic propagation iterative equation is as follows:
Figure BDA0003408478010000031
Figure BDA0003408478010000032
In＝In-1+αEn-1-γIn-1-μIn-1
Rn＝Rn-1+γIn-1-μRn-1
wherein S, E, I, R represents the number of susceptible persons, latent persons, infected persons and convalescent persons, r represents the number of infected persons contacting susceptible persons, and n represents the number of days; alpha is the probability of transferring from a latentiated person to an infected person, beta is the probability of contagious infection between a susceptible person and an infected person, and beta1The probability of the susceptible patient and the latent patient being infected, gamma is the probability of the infected patient recovering, and mu is the probability of the infected patient being isolated; n is the total number of nodes.
According to a second aspect of the present invention, there is provided a system based on the above behavior pattern-based SEIR infection risk simulation method, including:
the data acquisition and preprocessing module is used for acquiring epidemic situation data and carrying out scale division preprocessing on the epidemic situation data;
the time sequence network modeling module is used for establishing a time sequence network according to epidemic situation data;
the SEIR model epidemic situation simulation module is used for simulating epidemic situation propagation according to a time sequence network;
and the display module is used for visually displaying the simulated epidemic situation propagation data.
Compared with the prior art, the invention has the following advantages:
1) the invention redesigns the connection relation between the nodes on the basis of the time sequence network, constructs the whole crowd contact network through the human behavior mode, and finally simulates the infection situation of the infectious disease through the SEIR model, thereby improving the early-stage efficiency and accuracy of simulation and further mastering the initial stage of epidemic situation outbreak;
2) according to the method, the connection relation of the network nodes is redefined, the behavior mode of a person is simulated, the simulation result can be more fit with the actual situation of the epidemic situation, the accuracy rate at the initial stage is predicted to be increased, the control at the initial stage of the epidemic situation outbreak can be more accurate, and the resource loss is reduced;
3) aiming at the fact that the development of the disaster presents a consistency relationship, the method uses a time sequence network to combine with an SEIR model for simulation, improves the accuracy of disaster prediction, and can present long-time results;
4) the method can be used for rapidly modeling aiming at various scenes, rapidly evaluating the infection risk of each region and facilitating the subsequent epidemic situation source work.
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FIG. 1 is a flow chart of a method of the present invention;
FIG. 2 is a multi-scale dataflow framework;
FIG. 3a is a diagram of a general network;
FIG. 3b is a schematic diagram of a timing network;
FIG. 4 is a schematic diagram of a network node design;
FIG. 5 is a flow chart of node connection;
fig. 6 is a schematic diagram of the SEIR model.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, not all, embodiments of the present invention. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, shall fall within the scope of protection of the present invention.
As shown in fig. 1, this embodiment provides an sei infection risk simulation method based on a behavior pattern, which divides epidemic situation data into data of different scales through a time-series network, uses a family as a network node, determines a network node connection relationship based on the behavior pattern, simulates each stage of epidemic situation propagation by using an sei model, and determines an epidemic situation of each day. The method of the present invention is described in detail below.
1. Data pre-processing
The epidemic situation data has spatial information and time information, for example, the spatial information has disaster occurrence points, coverage, severity distribution and the like, and the time information has disaster occurrence time, duration, diffusion time and the like, so the epidemic situation data is separated into time data and space data by space and time; and then the division processing with different size scales is respectively carried out, as shown in fig. 2.
The time data is divided into long-time data and short-time data according to time, and the space data is divided into large space data and small space data according to space; combining long-time data and large-space data into large-scale data, and combining short-time data and small-space data into small-scale data; different information of disasters can be presented more effectively, and the method is convenient to have better effect on subsequent training and prediction.
2. Temporal networks for time-series networks
A time-series network is a complex network in which edges between nodes are active only at specific times, each edge carries information about when it is active, and in short, a connection edge in a time-series network appears or disappears with time. Fig. 3a and 3b show the difference between a normal network and a sequential network, respectively, where the edges of the sequential network change as time goes forward.
The development conditions of the disasters belong to a coherent relationship, so that when a time point is predicted, the influence of the time closer to the time point on the prediction result is more serious, and the accuracy of the prediction result can be effectively improved by adding different weight influences to the time information in the time sequence network.
3. Constructing a crowd contact network model
As shown in fig. 4 and 5, the connection between the network nodes is performed with a certain probability using the behavior pattern of the user to connect, defining the network nodes as homes. The behavioral patterns include unleashed, study, work, entertainment, and market;
different contact associations are generated according to different behavior modes, so that contact nodes and execution probability are determined.
People who are contacted with the learning and working in real life are relatively fixed, so the set condition is that the nodes are contacted with the fixed nodes, and when the number of the different contacted nodes reaches a set threshold value, the contact to other nodes which are not contacted is stopped; the crowd who is exposed to entertainment and the market is random, so that the whole result is simulated through the SEIR model by directly using random contact.
4. Simulation of epidemic propagation process by using SEIR model
The SEIR (safe-Exposed-Infected-Removed) model is used for modeling the probability of spreading infectious diseases, and divides each stage of epidemic spread into a Susceptible person, a latent period, an Infected person and a recovered person, as shown in FIG. 6.
The susceptible person has a latent period at the beginning, and symptoms appear after a period of time and become an infected person; once becoming a rehabilitative person, there is no re-infection, i.e. in the process of probability transmission, once becoming a rehabilitative person, there is no probability of re-transferring to an infected person or a susceptible person; the differential equation of the SEIR model is as follows:
Figure BDA0003408478010000051
Figure BDA0003408478010000052
Figure BDA0003408478010000053
Figure BDA0003408478010000054
wherein S, E, I, R represents the number of susceptible persons, latent persons, infected persons and convalescent persons, r represents the number of infected persons contacting susceptible persons, and t represents the propagation time; alpha is the probability of transferring from a latentiated person to an infected person, beta is the probability of contagious infection between a susceptible person and an infected person, and beta1The probability of the susceptible patient and the latent patient being infected, gamma is the probability of the infected patient recovering, and mu is the probability of the infected patient being isolated; n is the total number of nodes;
and based on the Markov chain, defining the state of the next day to be only related to the state of the previous day, and combining the differential equation to obtain the epidemic situation spread condition of each day.
The nth epidemic propagation iterative equation is as follows:
Figure BDA0003408478010000061
Figure BDA0003408478010000062
In＝In-1+αEn-1-γIn-1-μIn-1
Rn＝Rn-1+γIn-1-μRn-1
wherein S, E, I, R represents the number of susceptible persons, latent persons, infected persons and convalescent persons, r represents the number of infected persons contacting susceptible persons, and n represents the number of days; alpha is the probability of transferring from a latent person to an infected person, and beta is the probability of the infectious person contacting the susceptible person and the infected person to be infectedProbability of (a), beta1The probability of the susceptible patient and the latent patient being infected, gamma is the probability of the infected patient recovering, and mu is the probability of the infected patient being isolated; n is the total number of nodes.
The following provides an embodiment of the system of the present invention, and a system based on the above behavior pattern-based SEIR infection risk simulation method includes:
the data acquisition and preprocessing module is used for acquiring epidemic situation data and carrying out scale division preprocessing on the epidemic situation data;
the time sequence network modeling module is used for establishing a time sequence network according to epidemic situation data;
the SEIR model epidemic situation simulation module is used for simulating epidemic situation propagation according to a time sequence network;
and the display module is used for visually displaying the simulated epidemic situation propagation data.
The method can be used for quickly modeling aiming at various scenes, because attention is paid from a small-scale range, the interaction relation of the crowd in the small range is more regular compared with the large range, such as meeting between neighbors, carrying out the same entertainment activities and the like, the interaction is carried out with the large-scale data through the simulated result, and other fine conditions can be set according to different areas in the process, such as trans-regional movement on duty, resource interaction between trans-rural areas and other activities, so that the infection risks of other areas are presented, and the infection trend with larger scale is simulated;
taking the A city as an example, the infection origin is entertainment activities of chess and card rooms, the explosion point of the B city is from the beginning of the school, and both subsequent cases are infected mutually at the local explosion point, and then are transmitted to the family of the same residence through families, and then are transmitted in a larger scale, which further explains the importance of constructing crowd gathering simulation. The method can be used for rapidly modeling according to the scenes, rapidly evaluating the infection risk of each region and helping the follow-up epidemic news sources.
In addition, show the result of simulation through display module, can make the emergent personnel of calamity carry out quick control to the epidemic situation, promote the overall efficiency of epidemic prevention, also can carry out personnel's management and control to more serious area, can reduce the loss that the epidemic situation caused, improve the overall ability in toughness city.
While the invention has been described with reference to specific embodiments, the invention is not limited thereto, and various equivalent modifications and substitutions can be easily made by those skilled in the art within the technical scope of the invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims (10)

1. An SEIR infection risk simulation method based on a behavior pattern is characterized in that epidemic situation data are divided into data with different scales through a time sequence network, a family is used as a network node, the connection relation of the network node is determined based on the behavior pattern, a crowd contact network is constructed, an SEIR model is adopted to simulate each stage of epidemic situation propagation, and the daily epidemic situation propagation condition is determined.
2. The method of claim 1, wherein the epidemic data includes temporal data and spatial data; the time data is divided into long-time data and short-time data according to time, and the space data is divided into large space data and small space data according to space; and combining the long-time data and the large-space data into large-scale data, and combining the short-time data and the small-space data into small-scale data.
3. The method of claim 1, wherein the time-series network is a complex network in which node connection edges only act at specific time, each connection edge carries information of when the node is active, and connection edges in the time-series network appear or disappear with time.
4. The method according to claim 1, wherein the behavior pattern-based SEIR infection risk simulation method is specifically configured to determine a network node connection relationship based on a behavior pattern as follows: contact nodes and execution probabilities are determined based on the behavior patterns.
5. An SEIR infection risk simulation method according to claim 4 based on behaviour patterns including not going out, learning, working, entertainment and marketing; the learning and working is a fixed contact node; the entertainment and marketplace is a random contact node.
6. An SEIR infection risk simulation method based on behavioral patterns according to claim 5, characterized in that the fixed contact node settings are specifically: and when the number of the contacted different nodes reaches a set threshold value, stopping contacting other nodes which are not contacted yet.
7. The method of claim 1, wherein the stages of epidemic propagation include susceptible, latent, infected and convalescent individuals.
8. The method according to claim 7, wherein the SEIR model is used to simulate the stages of epidemic propagation, specifically:
the susceptible person has a latent period at the beginning, and symptoms appear after a period of time and become an infected person; once becoming a rehabilitative person, there is no re-infection, i.e. in the process of probability transmission, once becoming a rehabilitative person, there is no probability of re-transferring to an infected person or a susceptible person; the differential equation of the SEIR model is as follows:
Figure FDA0003408477000000021
Figure FDA0003408477000000022
Figure FDA0003408477000000023
Figure FDA0003408477000000024
wherein S, E, I, R represents the number of susceptible persons, latent persons, infected persons and convalescent persons, r represents the number of infected persons contacting susceptible persons, and t represents the propagation time; alpha is the probability of transferring from a latentiated person to an infected person, beta is the probability of contagious infection between a susceptible person and an infected person, and beta1The probability of the susceptible patient and the latent patient being infected, gamma is the probability of the infected patient recovering, and mu is the probability of the infected patient being isolated; n is the total number of nodes;
and based on the Markov chain, defining the state of the next day to be only related to the state of the previous day, and combining the differential equation to obtain the epidemic situation spread condition of each day.
9. The behavior pattern-based SEIR infection risk simulation method according to claim 8, wherein the iterative equation for spreading the epidemic situation on the nth day is:
Figure FDA0003408477000000025
Figure FDA0003408477000000026
In＝In-1+αEn-1-γIn-1-μIn-1
Rn＝Rn-1+γIn-1-μRn-1
wherein S, E, I, R are respectivelyThe number of susceptible persons, the number of latent persons, the number of infected persons and the number of recovered persons, r is the number of infected persons contacting susceptible persons, and the subscript n is the number of days; alpha is the probability of transferring from a latentiated person to an infected person, beta is the probability of contagious infection between a susceptible person and an infected person, and beta1The probability of the susceptible patient and the latent patient being infected, gamma is the probability of the infected patient recovering, and mu is the probability of the infected patient being isolated; n is the total number of nodes.
10. A system for a behavior pattern based SEIR infection risk simulation method according to claim 1, comprising:
the data acquisition and preprocessing module is used for acquiring epidemic situation data and carrying out scale division preprocessing on the epidemic situation data;
the time sequence network modeling module is used for establishing a time sequence network according to epidemic situation data;
the SEIR model epidemic situation simulation module is used for simulating epidemic situation propagation according to a time sequence network;
and the display module is used for visually displaying the simulated epidemic situation propagation data.
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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117095832A (en) * 2023-10-19 2023-11-21 泰州蕾灵百奥生物科技有限公司 Modeling method and system for animal epidemic disease infection risk

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117095832A (en) * 2023-10-19 2023-11-21 泰州蕾灵百奥生物科技有限公司 Modeling method and system for animal epidemic disease infection risk
CN117095832B (en) * 2023-10-19 2023-12-19 泰州蕾灵百奥生物科技有限公司 Modeling method and system for animal epidemic disease infection risk

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