CN112365996A - Disease propagation prevention and control method and system - Google Patents
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Abstract
本发明提供了一种疾病传播防控方法及系统，将待监测人员作为节点，采集每一个节点的基础数据以及该节点的相邻节点，作为节点数据；将基础数据超出设定阈值的节点作为初始感染者节点；采用SIR模型，以初始感染者节点为顶点，基于该顶点以及与该顶点相邻的其他节点信息，构建感染图网络，其中将感染图网络中的若干传播中心状态设为感染态I，将其他所有节点均设成易感态S，模拟疾病传播过程；采用溯源算法，计算得到一个源节点；按照其他节点的重要程度，生成需要隔离的节点数量，同时输出源节点和其他节点的标签，对疾病传播进行防控。本发明疾病筛查效率高，能够有效防止疾病传播，为后续的诊断和治疗提供有效的辅助工作。
The present invention provides a method and system for disease transmission prevention and control. The person to be monitored is used as a node, and basic data of each node and the adjacent nodes of the node are collected as node data; the node whose basic data exceeds the set threshold is used as the node data. The initial infected node; using the SIR model, with the initial infected node as the vertex, based on the information of the vertex and other nodes adjacent to the vertex, construct an infection graph network, in which the state of several transmission centers in the infection graph network is set as infection In state I, all other nodes are set to susceptible state S to simulate the disease transmission process; a source node is calculated by using the traceability algorithm; according to the importance of other nodes, the number of nodes that need to be isolated is generated, and the source node and other nodes are output at the same time. The label of the node to prevent and control the spread of the disease. The present invention has high disease screening efficiency, can effectively prevent disease spread, and provides effective auxiliary work for subsequent diagnosis and treatment.
Description
Technical Field
The invention relates to the technical field of epidemic situation prevention and control, in particular to a disease propagation prevention and control method and system.
Background
The health epidemic prevention is the construction of the health cause which is always maintained in China from the country construction, achieves great results, particularly controls and monitors various infectious diseases, and gradually eliminates the development and the popularity of the various infectious diseases. The narrow-sense of epidemic prevention refers to a series of measures taken to prevent and control the spread of diseases, and prevent the spread and epidemic of infectious diseases. The broad term epidemic prevention refers to the work of epidemic prevention in epidemic prevention stations, including two major parts of health supervision and disease control. Epidemic prevention has gradually been divided into two separate parts, health supervision and disease control. The health and epidemic prevention ensure the health of people in China and provide guarantee for normal social activities and economic activities in China.
For the places with dense people such as middle and primary schools, epidemic situation prevention and control are very important. The existing epidemic prevention measures are mainly used for detecting the body temperature of people one by one and investigating people who are possibly infected, so that the mode cannot control the close contact of the possibly infected people in time, further cannot effectively prevent and control the spread of diseases and evaluate the spread risk.
Through search, the following results are found:
the Chinese patent application with the publication number of CN111653368A and the publication date of 2020, 09 and 11 days provides an artificial intelligent epidemic situation big data prevention and control early warning system, which breaks through the traditional single infrared temperature measurement mode by carrying out face + body temperature + cardiopulmonary multi-mode identification and intelligent analysis on a person, realizes non-contact accurate temperature measurement, cardiopulmonary signal measurement and abnormal warning, and mainly aims at screening, detecting and warning the current new coronary pneumonia patients, especially asymptomatic patients, so that the screening working pressure of key public places can be greatly reduced, and the detection probability of suspected persons is improved. In addition, the system adopts an AI (artificial intelligence) mathematical model algorithm, has the capability of deep data mining, can utilize the neural convolution network to carry out deep learning, can predict the development trend of the epidemic situation and can generate the monitoring and troubleshooting standard of the epidemic situation in real time. However, the system is only used for screening the infected person, and a related technical scheme capable of timely grasping the person who may be infected and closely contacted with the infected person is not provided, so that the disease transmission can not be effectively prevented and controlled, and the disease transmission risk can not be evaluated.
The invention discloses a Chinese patent application with publication number CN111639191A, publication date 2020, 09 and 08 entitled "a prediction method for simulating epidemic situation development trend by novel coronavirus knowledge map", which comprises the following steps: acquiring and preprocessing a knowledge graph of the novel coronavirus; extracting the relational subgraphs; analysis of the transmission path of the new coronavirus epidemic. The invention introduces collective operation based on the characteristics of the data of the novel coronavirus knowledge graph and the correlation analysis task, divides the overall novel coronavirus knowledge graph into small-scale relation subgraphs according to the relation between novel coronavirus entities, so as to reduce the calculation amount and complexity of relation reasoning and improve the calculation efficiency. The method still has the following problems:
1. there is no data acquisition approach involved in establishing the profile.
2. Even if the method illustrates an acquisition path, without real propagation data as support, the knowledge-graph approach is difficult to work with.
3. There are deficiencies in the algorithm function: no traceability algorithm is indicated, and the source of infection and the person to be isolated cannot be indicated.
A Chinese patent invention, namely a disease prevention and control method and system, with an authorization notice number of CN 102043895B and an authorization notice date of 2013, 08 and 14, collects characteristic physiological data of a disease required to be prevented and controlled by a user through a user side device worn on the user and sends the data to a remote prevention and control server; the remote prevention and control server analyzes and processes the characteristic physiological data, and can ensure the timeliness of epidemic situation information reporting or publishing while improving the objective accuracy and reporting speed of the epidemic situation information. However, the method and the system still have the following problems:
1. the infection chain structure adopted can not accurately judge the close contact of the confirmed personnel.
2. No traceability algorithm is indicated, and the source of infection and the person to be isolated cannot be indicated.
In summary, the existing epidemic situation prevention and control technology cannot really meet the requirements of epidemic situation transmission prevention and control, no explanation or report similar to the technology of the invention is found at present, and similar data at home and abroad are not collected.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a disease propagation prevention and control method and a disease propagation prevention and control system.
The invention is realized by the following technical scheme.
According to an aspect of the present invention, there is provided a disease propagation prevention and control method including:
taking a person to be monitored as a node and setting a label, and acquiring body basic data of each node and other node information adjacent to the node as node data;
processing the obtained node data, comparing the basic data of the body with a set threshold value, and taking the node of which the basic data of the body exceeds the set threshold value as an initial infected person node;
based on an SIR model, taking an initial infected node as a vertex, and based on the vertex and other node information adjacent to the vertex, constructing an infection graph network; setting a plurality of top states in the infection graph network as infection states I, setting all other nodes as susceptible states S, and simulating a disease propagation process;
calculating to obtain a predicted source node in the constructed infection graph network by adopting a tracing algorithm;
and generating the number of nodes needing to be isolated according to the importance degree of other nodes related to the predicted source node, and simultaneously outputting labels of the source node and other nodes to prevent and control disease propagation.
Preferably, adopt intelligent bracelet, gather the basic data of the health and the contact personnel of each node, wherein, the basic data of health includes: body temperature, electrocardiogram, and blood pressure data.
Preferably, the smart band includes: the temperature sensor, the electrocardio sensor and the photoelectric sensor are arranged on the inner side wall of the bracelet body, and the Bluetooth sensor is arranged on the outer side wall of the bracelet body; wherein:
the temperature sensor is used for acquiring body temperature data of the nodes;
the electrocardio sensor is used for acquiring heart rate and/or heart rate data of the nodes to obtain electrocardio data;
the blood pressure sensor is used for acquiring diastolic pressure and systolic pressure of the nodes to obtain blood pressure data;
the Bluetooth sensor is used for acquiring the number of Bluetooth gateways and labels in a set range of the node and acquiring information of other nodes adjacent to the node.
Preferably, the electrocardiograph sensor is a PPG biosensor.
Preferably, the simulating a disease transmission process comprises:
at each time step in the disease transmission simulation process, each transmission center in the infection state I transfers the neighbor nodes in the susceptible state S to the infection state I with the probability beta, and meanwhile, the infection state I recovers health and transfers to the recovery state R with the probability gamma; when there are no individuals in the network in the infection state I, the simulated disease transmission process ends.
Preferably, based on bayesian theory, the traceability problem is written in the form of a maximum a posteriori estimate or a maximum likelihood estimate as follows:
s＝argmax
s∈V
P(s|O)＝argmax
s∈V
P(O|s)
wherein s is an infection source, i.e. a source node, V is all nodes to be monitored, O is an infection state of a graph formed by observing a state, i.e. starting from s, P (s | O) is a probability that a certain propagation state O is known, the source node is s, and P (O | s) is a probability that a state of each vertex obtained after a disease is diffused through an infection graph network is O, wherein the state O includes a susceptible state s (susceptible), an infected state i (infectious), and a recovery state r (recovered).
Preferably, the tracing algorithm, which uses the SSE algorithm to calculate a predicted source node s, includes:
s＝argmaxP(O|s)
＝argmaxs∈GNP(GN|s)
＝argmaxs∈GNR(s,GN)
wherein GN represents an infection map network, N is the number of vertexes in the infection map network, and R (s, GN) is the count of all possible infection sequences of all vertexes in the infection map network when the disease propagates from the vertexes, and is defined as the transmission centrality; the top point in the infection map network where the center of propagation is the greatest is defined as the center of propagation.
According to another aspect of the present invention, there is provided a disease propagation prevention and control system including:
the data acquisition module takes a person to be monitored as a node and sets a label, and acquires body basic data of each node and other node information adjacent to the node as node data;
the data analysis module is used for processing the obtained node data, comparing the basic data of the body with a set threshold value and taking the node of which the basic data of the body exceeds the set threshold value as an initial infector node; (ii) a
The network construction module is used for constructing an infection graph network by taking the initial infected node as a vertex and based on the vertex and other node information adjacent to the vertex on the basis of an SIR model; setting a plurality of top states in the infection graph network as infection states I, setting all other nodes as susceptible states S, and simulating a disease propagation process;
the prevention and control module adopts a tracing algorithm and calculates a predicted source node in the constructed infection graph network; and generating the number of nodes needing to be isolated according to the importance degree of other nodes related to the predicted source node, and simultaneously outputting labels of the source node and other nodes to prevent and control disease propagation.
Preferably, the data acquisition module adopts an intelligent bracelet, and the intelligent bracelet comprises a bracelet body, a temperature sensor, an electrocardio sensor and a photoelectric sensor which are arranged on the inner side wall of the bracelet body, and a Bluetooth sensor arranged on the outer side wall of the bracelet body; wherein:
the temperature sensor is used for acquiring body temperature data of the basic data of the node body;
the electrocardio sensor is used for acquiring heart rate and/or heart rate data of the node body basic data to obtain electrocardio data;
the blood pressure sensor is used for acquiring diastolic pressure and systolic pressure of the basic data of the node body to obtain blood pressure data;
the Bluetooth sensor is used for acquiring the number of Bluetooth gateways and labels in a set range of the node and acquiring information of other nodes adjacent to the node.
Preferably, the electrocardiograph sensor is a PPG biosensor.
Due to the adoption of the technical scheme, compared with the prior art, the invention has the following beneficial effects:
the disease transmission prevention and control method and the disease transmission prevention and control system can collect basic body data of each person in the crowd to be monitored, further judge the infection source and the close contact person of the infection source, evaluate the risk of disease transmission, timely isolate the infection source and the close contact person of the infection source and screen diseases, and effectively prevent the disease transmission.
The disease transmission prevention and control method and the disease transmission prevention and control system provided by the invention are particularly suitable for disease transmission prevention and control in the intensive environment of middle and primary schools, have high disease screening efficiency and can effectively prevent disease transmission.
The disease transmission prevention and control method and the system provided by the invention are particularly suitable for epidemic prevention work of novel coronavirus pneumonia. Because the main expression of taking to generate heat of novel coronavirus pneumonia, through gathering each personnel's among the crowd of treating monitoring health basic data, can discover the personnel of having a fever in real time to can in time master with the person of having a fever's person of being in close contact, carry out effectual isolation, and provide effectual auxiliary work for subsequent diagnosis and treatment.
According to the disease propagation prevention and control method and system provided by the invention, the related traceability algorithm is based on real interpersonal contact data, and the prediction result obtained by the method is more reliable through experimental verification.
According to the disease propagation prevention and control method and system provided by the invention, the related traceability algorithm is relatively comprehensive, and the functions of traceability and isolation can be realized simultaneously.
The disease transmission prevention and control method and system provided by the invention can be used for evaluating the risk of disease transmission.
It is not necessary for any product that embodies the invention to achieve all of the above-described advantages simultaneously.
Drawings
Other features, objects and advantages of the invention will become more apparent upon reading of the detailed description of non-limiting embodiments with reference to the following drawings:
FIG. 1 is a flow chart of a disease transmission prevention and control method according to a preferred embodiment of the present invention;
FIG. 2 is a diagram illustrating a traceability problem in an infection map network in accordance with a preferred embodiment of the present invention;
FIG. 3 is a diagram illustrating data flow in an exemplary application of the present invention;
fig. 4 is a diagram showing the relationship between modules of the disease propagation prevention and control system in a preferred embodiment of the present invention.
Detailed Description
The following examples illustrate the invention in detail: the embodiment is implemented on the premise of the technical scheme of the invention, and a detailed implementation mode and a specific operation process are given. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the inventive concept, which falls within the scope of the present invention.
An embodiment of the present invention provides a disease transmission prevention and control method, which forms an infection graph network by modeling a relationship between a possible infected person (i.e., an initial infected person node) and a close contact person of the possible infected person based on an SIR model, simulates a disease transmission process, calculates a source node based on a traceability algorithm, prevents and controls disease transmission by the importance degree of other nodes related to the source node, and can evaluate a disease transmission risk by using obtained data.
As shown in fig. 1, the method for preventing and controlling disease transmission provided by this embodiment includes:
step S1, taking a person to be monitored as a node and setting a label, and collecting body basic data of each node and other node information adjacent to the node as node data;
step S2, processing the obtained node data, comparing the basic data of the body with a set threshold value, and setting the node of which the basic data of the body exceeds the set threshold value as a possible infected person as an initial infected person node;
step S3, based on the SIR model, with the initial infected node as the vertex, and based on the vertex and other node information adjacent to the vertex, constructing an infection graph network; setting a plurality of top states in the infection graph network as infection states I, setting all other nodes as susceptible states S, and simulating a disease propagation process;
step S4, a source tracing algorithm is adopted to calculate a predicted source node in the constructed infection graph network;
and step S5, generating the number of nodes needing to be isolated according to the importance degree of other nodes related to the predicted source node, and simultaneously outputting labels of the source node and other nodes to prevent and control disease propagation.
As a preferred embodiment, in step S1, a smart bracelet is used to collect body basic data of each node and contact persons, where the body basic data includes: body temperature, electrocardiogram, and blood pressure data.
As a preferred embodiment, the smart band includes: the temperature sensor, the electrocardio sensor and the photoelectric sensor are arranged on the inner side wall of the bracelet body, and the Bluetooth sensor is arranged on the outer side wall of the bracelet body; wherein:
the temperature sensor is used for acquiring body temperature data of the nodes;
the electrocardio sensor is used for acquiring the heart rate and/or the heart rate data of the nodes to obtain the electrocardio data;
the blood pressure sensor is used for acquiring diastolic pressure and systolic pressure of the nodes to obtain blood pressure data;
the Bluetooth sensor is used for acquiring the number and the labels of the Bluetooth gateways in the set range of the node and acquiring the information of other nodes adjacent to the node.
As a preferred embodiment, the electrocardiograph sensor is a PPG biosensor.
As a preferred embodiment, in step S3, simulating the disease transmission process includes:
at each time step in the disease transmission simulation process, each transmission center in the infection state I transfers the neighbor nodes in the susceptible state S to the infection state I with the probability beta, and meanwhile, the infection state I recovers health and transfers to the recovery state R with the probability gamma; when there are no individuals in the network in the infection state I, the simulated disease transmission process ends.
As a preferred embodiment, in step S4, based on bayesian theory, the tracing problem is written in the form of maximum posterior estimation or maximum likelihood estimation as follows:
s＝argmax
s∈V
P(s|O)＝argmax
s∈V
P(O|s)
wherein s is an infection source, i.e. a source node, V is all nodes to be monitored, O is an infection state of a graph formed by observing a state, i.e. starting from s, P (s | O) is a state O knowing a certain propagation, the probability that the source node is s is opposite to P (O | s), and P (O | s) is the probability that the state of each vertex is O obtained after the disease is diffused through an infection graph network, wherein the state O comprises a susceptible state s (susceptible), an infected state i (infectious), and a recovery state r (recovered).
As a preferred embodiment, as shown in fig. 2, in step S4, the source tracing algorithm, which uses the SSE algorithm, calculates a predicted source node, including:
s＝argmaxP(O|s)
＝argmaxs∈GNP(GN|s)
＝argmaxs∈GNR(s,GN)
wherein GN represents the infection map network, N is the number of vertices in the infection map network, and R (s, GN) is the count of all possible infection sequences of all vertices in the infection map network when the disease propagates from the vertices, defined as the propagation centrality
The disease propagation prevention and control method provided by the above embodiment of the present invention can be implemented by the following algorithm flow, and the data flow diagram of the algorithm flow is shown in fig. 3:
1. calling an igraph library according to input data to generate a virus propagation network with a fixed size in a memory by a method of an adjacent matrix;
2. simulating virus propagation according to the transmitted parameters days and seed, wherein an epidemiological model is SIR, and generating an infection map network III;
3. tracing according to an SSE tracing algorithm, and calculating to obtain a predicted source node;
4. and (3) sequencing according to the importance degree of the nodes, namely the number of the connecting edges, indicating the number of people needing to be isolated, finally packaging all the generated information, labels (such as numbers) of all the infected nodes, the number of the infected nodes and the number of people needing to be isolated into json data to be returned, preventing and controlling disease transmission, and evaluating disease transmission risk through the returned data.
Another embodiment of the present invention provides a disease propagation prevention and control system, as shown in fig. 4, including:
the data acquisition module is used for taking a person to be monitored as a node and setting a label, and acquiring body basic data of each node and other node information adjacent to the node as node data;
the data analysis module is used for processing the obtained node data, comparing the basic data of the body with a set threshold value and taking the node of which the basic data of the body exceeds the set threshold value as an initial infector node; (ii) a
The network construction module is used for constructing an infection graph network by taking the initial infected node as a vertex and based on the vertex and other node information adjacent to the vertex on the basis of an SIR model; setting a plurality of top states in the infection graph network as infection states I, setting all other nodes as susceptible states S, and simulating a disease propagation process;
the prevention and control module calculates to obtain a predicted source node in the constructed infection graph network by adopting a tracing algorithm; and generating the number of nodes needing to be isolated according to the importance degree of other nodes related to the predicted source node, and simultaneously outputting labels of the source node and other nodes to prevent and control disease propagation.
As a preferred embodiment, the data acquisition module adopts an intelligent bracelet, and the intelligent bracelet comprises a bracelet body, a temperature sensor, an electrocardio sensor and a photoelectric sensor which are arranged on the inner side wall of the bracelet body, and a Bluetooth sensor arranged on the outer side wall of the bracelet body; wherein:
the temperature sensor is used for acquiring body temperature data of the basic data of the node body;
the electrocardio sensor is used for acquiring heart rate and/or heart rate data of the node body basic data to obtain electrocardio data;
the blood pressure sensor is used for acquiring diastolic pressure and systolic pressure of the basic data of the node body to obtain blood pressure data;
and the Bluetooth sensor is used for acquiring the number and the labels of adjacent Bluetooth gateways in the set range of the node and acquiring information of other nodes adjacent to the node.
As a preferred embodiment, the electrocardiograph sensor is a PPG biosensor.
The disease transmission prevention and control method and the disease transmission prevention and control system provided by the embodiment of the invention can collect basic body data of each person in a crowd to be monitored, further judge the infection source and the person in close contact with the infection source, evaluate the risk of disease transmission, timely isolate and screen the infection source and the person in close contact with the infection source, prevent and control the disease transmission and effectively prevent the disease transmission; the method and the system are particularly suitable for disease transmission prevention and control in the dense environment of the middle and primary schools, have high disease screening efficiency and can effectively prevent disease transmission; the method and the system are particularly suitable for epidemic prevention work of the novel coronavirus pneumonia: because the main expression of taking to generate heat of novel coronavirus pneumonia, through gathering each personnel's among the crowd of treating monitoring health basic data, can discover the personnel of having a fever in real time to can in time master with the person of having a fever's person of being in close contact, carry out effectual isolation, and provide effectual auxiliary work for subsequent diagnosis and treatment.
It should be noted that, the steps in the method provided by the present invention may be implemented by using corresponding modules, devices, units, and the like in the system, and those skilled in the art may implement the composition of the system by referring to the technical solution of the method, that is, the embodiment in the method may be understood as a preferred example for constructing the system, and will not be described herein again.
Those skilled in the art will appreciate that, in addition to implementing the system and its various devices provided by the present invention in purely computer readable program code means, the method steps can be fully programmed to implement the same functions by implementing the system and its various devices in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers and the like. Therefore, the system and various devices thereof provided by the present invention can be regarded as a hardware component, and the devices included in the system and various devices thereof for realizing various functions can also be regarded as structures in the hardware component; means for performing the functions may also be regarded as structures within both software modules and hardware components for performing the methods.
The foregoing description of specific embodiments of the present invention has been presented. It is to be understood that the present invention is not limited to the specific embodiments described above, and that various changes and modifications may be made by one skilled in the art within the scope of the appended claims without departing from the spirit of the invention.
Claims (10)
1. A method for preventing and controlling disease transmission, comprising:
taking a person to be monitored as a node and setting a label, and acquiring body basic data of each node and other node information adjacent to the node as node data;
processing the obtained node data, comparing the basic data of the body with a set threshold value, and taking the node of which the basic data of the body exceeds the set threshold value as an initial infected person node;
based on an SIR model, taking an initial infected node as a vertex, and based on the vertex and other node information adjacent to the vertex, constructing an infection graph network; setting a plurality of top states in the infection graph network as infection states I, setting all other nodes as susceptible states S, and simulating a disease propagation process;
calculating to obtain a predicted source node in the constructed infection graph network by adopting a tracing algorithm;
and generating the number of nodes needing to be isolated according to the importance degree of other nodes related to the predicted source node, and simultaneously outputting labels of the source node and other nodes to prevent and control the spread of diseases.
2. The disease propagation prevention and control method according to claim 1, wherein a smart bracelet is used to collect body basic data of each node and contact persons, wherein the body basic data comprises: body temperature, electrocardiogram, and blood pressure data.
3. The disease propagation prevention and control method according to claim 2, wherein the smart band comprises: the temperature sensor, the electrocardio sensor and the photoelectric sensor are arranged on the inner side wall of the bracelet body, and the Bluetooth sensor is arranged on the outer side wall of the bracelet body; wherein:
the temperature sensor is used for acquiring body temperature data of the nodes;
the electrocardio sensor is used for acquiring heart rate and/or heart rate data of the nodes to obtain electrocardio data;
the blood pressure sensor is used for acquiring diastolic pressure and systolic pressure of the nodes to obtain blood pressure data;
the Bluetooth sensor is used for acquiring the number of Bluetooth gateways and labels in a set range of the node and acquiring information of other nodes adjacent to the node.
4. The method according to claim 3, wherein the electrocardiographic sensor is a PPG biosensor.
5. The disease propagation prevention and control method according to claim 1, wherein the simulation of the disease propagation process comprises:
at each time step in the disease transmission simulation process, each transmission center in the infection state I transfers the neighbor nodes in the susceptible state S to the infection state I with the probability beta, and meanwhile, the infection state I recovers health and transfers to the recovery state R with the probability gamma; when there are no individuals in the network in the infection state I, the simulated disease transmission process ends.
6. The disease propagation prevention and control method according to claim 1, wherein the tracing problem is written in the form of maximum posterior estimation or maximum likelihood estimation as follows based on bayesian theory:
s＝argmax
s∈V
P(s|O)＝argmax
s∈V
P(O|s)
wherein S is an infection source, i.e. a source node, V is all nodes to be monitored, O is an observation of one state, i.e. an infection state of a graph formed from S, P (S | O) is a probability that a certain propagation state O is known, the source node is S, and P (O | S) is a probability that a state of each vertex obtained after a disease is propagated through an infection graph network is O, wherein the state O includes a susceptible state S, an infected state I and a recovery state R.
7. The disease propagation prevention and control method of claim 5, wherein the tracing algorithm, using the SSE algorithm, calculates a predicted source node s, comprising:
s＝argmaxP(O|s)
＝argmaxs∈GNP(GN|s)
＝argmaxs∈GNR(s,GN)
wherein GN represents an infection map network, N is the number of vertexes in the infection map network, and R (s, GN) is the count of all possible infection sequences of all vertexes in the infection map network when the disease propagates from the vertexes, and is defined as the transmission centrality; the top point in the infection map network where the center of propagation is the greatest is defined as the center of propagation.
8. A disease transmission prevention and control system, comprising:
the data acquisition module takes a person to be monitored as a node and sets a label, and acquires body basic data of each node and other node information adjacent to the node as node data;
the data analysis module is used for processing the obtained node data, comparing the basic data of the body with a set threshold value and taking the node of which the basic data of the body exceeds the set threshold value as an initial infector node; (ii) a
The network construction module is used for constructing an infection graph network by taking the initial infected node as a vertex and based on the vertex and other node information adjacent to the vertex on the basis of an SIR model; setting a plurality of top states in the infection graph network as infection states I, setting all other nodes as susceptible states S, and simulating a disease propagation process;
the prevention and control module adopts a tracing algorithm and calculates a predicted source node in the constructed infection graph network; and generating the number of nodes needing to be isolated according to the importance degree of other nodes related to the predicted source node, and simultaneously outputting labels of the source node and other nodes to evaluate, prevent and control disease propagation.
9. The disease propagation prevention and control system according to claim 8, wherein the data acquisition module is an intelligent bracelet, the intelligent bracelet comprising a bracelet body, and a temperature sensor, an electrocardio sensor, a photoelectric sensor and a Bluetooth sensor arranged on the inner side wall of the bracelet body, wherein the temperature sensor, the electrocardio sensor and the photoelectric sensor are arranged on the inner side wall of the bracelet body; wherein:
the temperature sensor is used for acquiring body temperature data of the basic data of the node body;
the electrocardio sensor is used for acquiring heart rate and/or heart rate data of the node body basic data to obtain electrocardio data;
the blood pressure sensor is used for acquiring diastolic pressure and systolic pressure of the basic data of the node body to obtain blood pressure data;
the Bluetooth sensor is used for acquiring the number of Bluetooth gateways and labels in a set range of the node and acquiring information of other nodes adjacent to the node.
10. The disease propagation prevention and control system of claim 9, wherein the electrocardiograph sensor is a PPG biosensor.
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