Community structure-based infectious disease prediction and intervention method, system, equipment and medium

Abstract

The invention discloses an infectious disease prediction and intervention method, system and device based on a community structure and a medium. The method comprises the following steps: constructing a contact network; performing community division on the contact network; constructing a propagation model according to the disease characteristics, determining a seed node, taking a community where the seed node is located as a community 0, and simulating a disease propagation process on the contact network according to the propagation model; calculating the average disease arrival time lt of each community in the contact network; tau igt; ; according to the average disease arrival time lt of each community in the contact network; tau igt; according to the method, the system, the equipment and the medium, the infectious disease can be accurately predicted and intervened.

Description

Technical Field

[0001] The present invention belongs to the field of complex networks and relates to a method, system, equipment and medium for predicting and intervening in infectious diseases based on community structure. Background Art

[0002] Traditional disease transmission dynamics models are built on the assumption that all individuals are evenly mixed. These models are essentially built on regular networks or random networks, but in reality, the disease transmission process is carried out on the population contact network, which depends on the network's topological structure and transmission mode. For a population contact network, individuals are abstracted into nodes of the network, and the contact process between individuals is abstracted into the edges of the network. However, the population density, contact frequency, and economic conditions of each region are different. If the same intervention measures are not optimal for each region. Summary of the invention

[0003] The purpose of the present invention is to overcome the shortcomings of the above-mentioned prior art and to provide a method, system, device and medium for predicting and intervening in infectious diseases based on community structure. The method, system, device and medium can predict and intervene in infectious diseases more accurately.

[0004] To achieve the above-mentioned purpose, the present invention discloses a method for predicting and intervening infectious diseases based on community structure, comprising:

[0005] Build a network of contacts;

[0006] Dividing the contact network into communities;

[0007] Construct a propagation model according to the disease characteristics, determine the seed node, take the community where the seed node is located as community 0, and simulate the disease propagation process on the contact network according to the propagation model;

[0008] Calculate the average disease arrival time <τ for each community in the contact network i >

[0009] According to the average disease arrival time of each community in the contact network <τ i > Intervene.

[0010] The process of dividing the contact network into communities is as follows:

[0011] The contact network is divided into communities using the Louvian algorithm.

[0012] The average disease arrival time of each community in the calculated contact network is <τ i >The process is:

[0013] Calculate the average disease arrival time <τ of each community in the contact network based on the Monte Carlo algorithm i>.

[0014] According to the average disease arrival time of each community in the contact network <τ i >The intervention process is:

[0015] In <τ i >, all connections between community 0 and community i are cut off, and the infected nodes in community i that are connected to community 0 are isolated. The nodes that are cut off but not isolated are <τ i > When +T, the connection is restored and the isolated node is released after being immunized.

[0016] The process of constructing the contact network is as follows:

[0017] Build a contact network through questionnaires, mobile phone Bluetooth signals or population mobility information.

[0018] The propagation dynamics equation of the propagation model is:

[0019]

[0020] Among them, i and j represent the community numbers, and m is the total number of communities.

[0021] The present invention discloses an infectious disease prediction and intervention system based on community structure, comprising:

[0022] Building blocks for constructing contact networks;

[0023] A partitioning module, used for partitioning the contact network into communities;

[0024] A simulation module is used to construct a propagation model according to disease characteristics, determine a seed node, take the community where the seed node is located as community 0, and simulate the disease propagation process on the contact network according to the propagation model;

[0025] Calculation module, used to calculate the average disease arrival time <τ for each community in the contact network i >

[0026] The intervention module is used to contact the average disease arrival time of each community in the network <τ i >Intervene.

[0027] According to the average disease arrival time of each community in the contact network <τ i >The intervention process is:

[0028] In <τ i >, all connections between community 0 and community i are cut off, and the infected nodes in community i that are connected to community 0 are isolated. The nodes that are cut off but not isolated are <τ i> When +T, the connection is restored and the isolated node is released after being immunized.

[0029] The present invention discloses a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the community structure-based infectious disease prediction and intervention method are implemented.

[0030] The present invention discloses a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the infectious disease prediction and intervention method based on community structure are implemented.

[0031] The present invention has the following beneficial effects:

[0032] The infectious disease prediction and intervention method, system, device and medium based on community structure of the present invention, when operating, divide the contact network into communities, calculate the average disease arrival time of each community in the contact network <τ i >, and use this to intervene, avoid contact between each community and the starting community at a specific time, achieve the prediction and intervention of infectious diseases, and provide better decision-making guidance for epidemic control and intervention. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 is a flow chart of the method of the present invention;

[0034] Figure 2 It is a schematic diagram of community propagation of the present invention;

[0035] Figure 3 Schematic diagram of the propagation dynamics of the present invention. DETAILED DESCRIPTION

[0036] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiment of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiment of the present invention. Obviously, the described embodiment is only an embodiment of a part of the present invention, not all embodiments, and is not intended to limit the scope of the present invention. In addition, in the following description, the description of well-known structures and technologies is omitted to avoid unnecessary confusion of the concepts disclosed in the present invention. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work should fall within the scope of protection of the present invention.

[0037] The accompanying drawings show schematic diagrams of structures according to embodiments disclosed in the present invention. These figures are not drawn to scale, and some details are magnified and some details may be omitted for the purpose of clear expression. The shapes of various regions and layers shown in the figures and the relative sizes and positional relationships therebetween are only exemplary, and may deviate in practice due to manufacturing tolerances or technical limitations, and those skilled in the art may additionally design regions / layers with different shapes, sizes, and relative positions according to actual needs.

[0038] Embodiment 1

[0039] refer to Figure 1 , Figure 2 and Figure 3 The infectious disease prediction and intervention method based on community structure of the present invention comprises the following steps:

[0040] 1) Obtain the original data of the contact network;

[0041] 2) When the contact network is non-regional, the contact network is divided into communities using the Louvian algorithm;

[0042] 3) Establish a propagation model based on disease characteristics, select seed nodes, record the community where the seed nodes are located as community 0, and simulate the disease propagation process on the contact network;

[0043] 4) Calculate the average disease arrival time of each community based on the Monte Carlo algorithm <τ i >

[0044] 5) In <τ i >, all connections between community 0 and community i are cut off, and the infected nodes in community i that are connected to community 0 are isolated. The nodes that are cut off but not isolated are <τ i > When +T, the connection is restored and the isolated node is released after being immunized.

[0045] The specific operations of step 1) are:

[0046] 11) Establish contact networks through questionnaires, mobile phone Bluetooth signals or population mobility information;

[0047] 12) When the contact network is in different regions, each region is regarded as a different community in the contact network.

[0048] The operation process of step 2) is:

[0049] 21) Treat each node in the contact network as an independent community;

[0050] 22) For the nodes in the community, assign the node to the community where each of its neighboring nodes is located in turn, calculate the modularity change ΔQ before and after the assignment, and record the neighboring node with the largest ΔQ. When ΔQ>0, assign the node to the community where the neighboring node with the largest ΔQ is located, otherwise, it remains unchanged, where the modularity change ΔQ is:

[0051]

[0052] 23) Repeat step 22) until the communities to which all nodes belong no longer change;

[0053] 24) All nodes in the same community are compressed into a new node, where the weight of the edge between nodes in the community is the weight of the ring of the new node, and the edge weight between communities is the edge weight between new nodes;

[0054] 25) Go to step 21) until the modularity of the entire contact network remains unchanged, and the community network is obtained.

[0055] The operation process of step 3) is:

[0056] 31) Taking the SIR model as an example, the propagation dynamics equation in the community network is:

[0057]

[0058] Among them, i and j represent the community numbers, m is the total number of communities, and the propagation dynamics is as follows Figure 3 shown.

[0059] 32) When the individual is in an infected state, γ probability is immune;

[0060] 33) When an individual is in a susceptible state, if there are n neighbors in an infected state, 1-(1-β) + probability of being infected;

[0061] 34) Loop through each node in the community network and execute step 32) and step 33).

[0062] The operation process of step 4) is:

[0063] 41) Loop through time t;

[0064] 42) Execute steps 32) and 33), when the i-th community has an infected node, record the time τ i ;

[0065] 43) Repeat step 42) 100 times to obtain 100 sets of τ i ;

[0066] 44) Calculate the 100 groups of τi The average value of <τ i >.

[0067] The operation process of step 5) is:

[0068] 51) Loop through time t;

[0069] 52) Execute step 32) and step 33)

[0070] 53) When the current When an individual in community i is in an infected state, isolate the individual;

[0071] 54) When individuals in community i are in isolation, they are immunized and released from isolation with probability γ;

[0072] 55) When the current When , disconnect all connections between community 0 and community i, and then <τ i >Restore the connection when +T;

[0073] 56) Loop through each node in the network and execute steps 52) to 54).

[0074] The present invention is based on voluntary control of mobility behavior, predicting the arrival time of the disease in each community, thereby avoiding contact with the starting community at a specific time. It can control the spread of the disease more accurately than traditional methods while ensuring the normal operation of society.

[0075] Embodiment 2

[0076] Accordingly, the present invention discloses an infectious disease prediction and intervention system based on community structure, comprising:

[0077] Building blocks for constructing contact networks;

[0078] A partitioning module, used for partitioning the contact network into communities;

[0079] A simulation module is used to construct a propagation model according to disease characteristics, determine a seed node, take the community where the seed node is located as community 0, and simulate the disease propagation process on the contact network according to the propagation model;

[0080] Calculation module, used to calculate the average disease arrival time <τ for each community in the contact network i >

[0081] The intervention module is used to contact the average disease arrival time of each community in the network <τ i > Intervene.

[0082] According to the average disease arrival time of each community in the contact network <τ i>The intervention process is:

[0083] In <τ i >, all connections between community 0 and community i are cut off, and the infected nodes in community i that are connected to community 0 are isolated. The nodes that are cut off but not isolated are <τ i > When +T, the connection is restored and the isolated node is released after being immunized.

[0084] Embodiment 3

[0085] A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the community structure-based infectious disease prediction and intervention method are implemented. The memory may include a memory, such as a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk memory, etc. The processor, the network interface, and the memory are interconnected through an internal bus, which may be an industrial standard architecture bus, a peripheral component interconnection standard bus, an extended industrial standard architecture bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. The memory is used to store programs. Specifically, the program may include a program code, and the program code includes computer operation instructions. The memory may include a memory and a non-volatile memory, and provide instructions and data to the processor.

[0086] Embodiment 4

[0087] A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the infectious disease prediction and intervention method based on community structure are implemented. Specifically, the computer-readable storage medium includes, but is not limited to, for example, volatile memory and / or non-volatile memory. The volatile memory may include random access memory (RAM) and / or cache memory (cache), etc. The non-volatile memory may include read-only memory (ROM), hard disk, flash memory, optical disk, magnetic disk, etc.

[0088] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0089] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0090] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0091] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0092] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for predicting and intervening infectious diseases based on community structure, It is characterized in that include: Build a network of contacts; Dividing the contact network into communities; Construct a propagation model according to the disease characteristics, determine the seed node, take the community where the seed node is located as community 0, and simulate the disease propagation process on the contact network according to the propagation model; Calculate the average disease arrival time <τ for each community in the contact network i > According to the average disease arrival time of each community in the contact network <τ i > Intervene.

2. The infectious disease prediction and intervention method based on community structure according to claim 1, It is characterized in that The process of dividing the contact network into communities is as follows: The contact network is divided into communities using the Louvian algorithm.

3. The infectious disease prediction and intervention method based on community structure according to claim 1, It is characterized in that The average disease arrival time of each community in the calculated contact network is <τ i >The process is: Calculate the average disease arrival time <τ of each community in the contact network based on the Monte Carlo algorithm i >.

4. The infectious disease prediction and intervention method based on community structure according to claim 1, It is characterized in that According to the average disease arrival time of each community in the contact network <τ i >The intervention process is: In <τ i >, all connections between community 0 and community i are cut off, and the infected nodes in community i that are connected to community 0 are isolated. The nodes that are cut off but not isolated are <τ i > When +T, the connection is restored and the isolated node is released after being immunized.

5. The infectious disease prediction and intervention method based on community structure according to claim 1, It is characterized in that The process of constructing the contact network is as follows: Build a contact network through questionnaires, mobile phone Bluetooth signals or population mobility information.

6. The infectious disease prediction and intervention method based on community structure according to claim 1, It is characterized in that The propagation dynamics equation of the propagation model is: Among them, i and j represent the community numbers, and m is the total number of communities.

7. A method for predicting and intervening infectious diseases based on community structure, It is characterized in that include: Building blocks for constructing contact networks; A partitioning module, used for partitioning the contact network into communities; A simulation module is used to construct a propagation model according to disease characteristics, determine a seed node, take the community where the seed node is located as community 0, and simulate the disease propagation process on the contact network according to the propagation model; Calculation module, used to calculate the average disease arrival time <τ for each community in the contact network i > The intervention module is used to contact the average disease arrival time of each community in the network <τ i > Intervene.

8. The infectious disease prediction and intervention method based on community structure according to claim 7, It is characterized in that According to the average disease arrival time of each community in the contact network <τ i >The intervention process is: In <τ i >, all connections between community 0 and community i are cut off, and the infected nodes in community i that are connected to community 0 are isolated. The nodes that are cut off but not isolated are <τ i > When +T, the connection is restored and the isolated node is released after being immunized.

9. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, It is characterized in that When the processor executes the computer program, the steps of the infectious disease prediction and intervention method based on community structure as described in any one of claims 1 to 6 are implemented.

10. A computer-readable storage medium storing a computer program. It is characterized in that When the computer program is executed by a processor, the steps of the infectious disease prediction and intervention method based on community structure as described in any one of claims 1 to 6 are implemented.

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