CN113871023A - Social behavior-based infectious disease tracking method, device, equipment and medium - Google Patents
Social behavior-based infectious disease tracking method, device, equipment and medium Download PDFInfo
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Abstract
The disclosure provides an infectious disease tracking method, an infectious disease tracking device, infectious disease tracking equipment and an infectious disease tracking medium based on social behaviors, wherein the method comprises the following steps: acquiring social behavior data of a target object; determining an intimate contact of the target object based on social behavior data of the target object, and determining other target objects related to the intimate contact based on the intimate contact; calculating a closeness score of each close contact of the target objects based on the types of social behaviors in the social behavior data of all target objects associated with the close contacts and the propagation coefficient of the target objects; determining a high risk infectious agent based on the seal close score. According to the method and the device, high-risk close contact personnel can be comprehensively and accurately determined, the working efficiency of manual investigation during the epidemic situation is improved, and especially under the conditions that medical resources are in short supply and medical detection cannot be carried out in a large area, important data support is provided for the epidemic situation investigation work in various regions.
Description
Technical Field
One or more embodiments of the present disclosure relate to the field of data processing, and more particularly, to a social behavior-based infectious disease tracking method, apparatus, device, and medium.
Background
How to provide effective data support for accurate prevention and control of epidemic situation in the bud state of epidemic situation, provide technical guarantee for accurate social control, become a challenge of big data technology worker. The traditional tracking method tracks the close contact persons through single data, such as video data and non-contact type equipment data, and has no comprehensive and complete analysis application for integrating various types of data, and a corresponding integral model is not established to distinguish the exposure risk. Therefore, the tracking technology of the infectious disease close contact persons in the prior art has the problems of incomplete comprehensiveness and incapability of distinguishing risks.
Disclosure of Invention
In view of the above, an object of one or more embodiments of the present disclosure is to provide a method, an apparatus, a device and a storage medium for social behavior-based infectious disease tracking, so as to solve at least one of the above problems.
In view of the above object, according to a first aspect of the present disclosure, there is provided a social behavior-based infectious disease tracking method, including:
acquiring social behavior data of a target object;
determining an intimate contact of the target object based on social behavior data of the target object, and determining other target objects related to the intimate contact based on the intimate contact;
calculating a closeness score of each close contact of the target objects based on the types of social behaviors in the social behavior data of all target objects associated with the close contacts and the propagation coefficient of the target objects;
determining a high risk infectious agent based on the seal close score.
Optionally, calculating the affinity score of each of the target objects based on the type of social behavior in the social behavior data of all the target objects associated with the close contacts and the propagation coefficient of the target object, comprises:
the seal fraction M is calculated according to the following formula:
Optionally, the score of the social behavior comprises a base score of the social behavior, wherein the social behavior data comprises a plurality of different types of social behaviors, and each type of social behavior corresponds to a different base score.
Optionally, the score of the social behavior comprises a base score and an additional score of the social behavior, wherein for the same type of social behavior, at least one subclass is included, and each subclass corresponds to a different additional score.
Optionally, the target object includes confirmed persons and suspected persons of infectious diseases, and the transmission coefficient of the target object includes: the confirmed person has a first propagation coefficient and the suspected person has a second propagation coefficient, wherein the first propagation coefficient is greater than the second propagation coefficient.
Optionally, determining a high risk infectious agent based on the seal score comprises:
and comparing the close contact score with a preset threshold value, and determining that the close contact person corresponding to the close contact score higher than the preset threshold value is a high-risk infectious person.
Optionally, the method further comprises: analyzing a propagation path and/or a superpropagator based on social behavior data of the target object.
According to a second aspect of the present disclosure, there is provided an infectious disease tracking apparatus based on social behaviors, comprising:
the acquisition module is used for acquiring social behavior data of the target object;
the close contact confirmation module is used for determining close contacts of the target object based on social behavior data of the target object and then determining other target objects related to the close contacts based on the close contacts;
the score calculation module is used for calculating the close contact score of each close contact person of the target object based on the type of social behaviors in the social behavior data of all target objects related to the close contact persons and the propagation coefficient of the target object;
and the risk confirmation module is used for determining the high-risk infectious personnel based on the joint sealing score.
According to a third aspect of the present disclosure, there is provided an electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the method according to the first aspect when executing the program.
According to a fourth aspect of the present disclosure, there is provided a non-transitory computer-readable storage medium, characterized in that the non-transitory computer-readable storage medium stores computer instructions for causing the computer to perform the method of the first aspect.
As can be seen from the above description, according to the social behavior information, including time, space, and social behavior information, of the known target objects (usually suspected and confirmed persons of infectious diseases), the method, apparatus, and storage medium for infectious disease tracking based on social behaviors provided by one or more embodiments of the present disclosure calculate other unknown objects that may be in close contact with the known target objects under a big data environment, and calculate the close contact degree of each unknown object by constructing the close contact integral of each unknown object, thereby finding out an unknown object group with a high close contact degree, i.e., a high possibility of being infected, comprehensively and accurately determining high-risk close contact persons, improving the work efficiency of manual investigation during an epidemic situation, and particularly providing important data support for the work of examining the epidemic situations in various regions under the conditions of short medical resources and incapability of performing medical detection in a large area.
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In order to more clearly illustrate one or more embodiments or prior art solutions of the present disclosure, reference will now be made briefly to the attached drawings, which are used in the description of the embodiments or prior art, and it should be apparent that the attached drawings in the description below are only one or more embodiments of the present disclosure, and that other drawings may be obtained by those skilled in the art without inventive effort.
FIG. 1 is a schematic flow chart diagram of a social behavior-based infectious disease tracking method according to an embodiment of the present disclosure;
FIG. 2 is a schematic illustration of a social behavior-based infectious disease tracking method of an embodiment of the present disclosure;
FIG. 3 is a schematic block diagram of a social behavior-based infectious disease tracking apparatus according to an embodiment of the present disclosure;
fig. 4 is a schematic block diagram of an electronic device of an embodiment of the disclosure.
Detailed Description
For the purpose of promoting a better understanding of the objects, aspects and advantages of the present disclosure, reference is made to the following detailed description taken in conjunction with the accompanying drawings.
It is to be noted that unless otherwise defined, technical or scientific terms used in one or more embodiments of the present disclosure should have the ordinary meaning as understood by one of ordinary skill in the art to which the present disclosure belongs. The use of "first," "second," and similar terms in one or more embodiments of the present disclosure is not intended to indicate any order, quantity, or importance, but rather is used to distinguish one element from another. The word "comprising" or "comprises", and the like, means that the element or item listed before the word covers the element or item listed after the word and its equivalents, but does not exclude other elements or items. The terms "connected" or "coupled" and the like are not restricted to physical or mechanical connections, but may include electrical connections, whether direct or indirect.
At present, the traditional infectious disease tracking technology generally searches for potential infected persons by means of video frame data provided by video monitoring equipment in public places, or receives an identification code sent by an intelligent terminal in real time and stores the identification code in a database, and then searches for a contacter according to a terminal number of the infected person, or records a close contacter and establishes a contact history based on non-contact equipment such as bluetooth, or tracks network public opinions. However, these tracking technologies track the close contacts through a single data, such as video data and contactless device data, and there is no comprehensive and complete analysis application for integrating various types of data, and there is no corresponding integration model to differentiate the exposure level.
In view of the above, embodiments of the present disclosure provide a social behavior-based infectious disease tracking method. Referring to fig. 1, fig. 1 shows a schematic flow chart of a social behavior-based infectious disease tracking method according to an embodiment of the present disclosure. As shown in fig. 1, the social behavior-based infectious disease tracking method includes:
step S110, social behavior data of a target object is obtained;
step S120, determining the close contact person of the target object based on the social behavior data of the target object, and determining other target objects related to the close contact person based on the close contact person;
step S130, calculating the close contact score of each close contact of the target objects based on the types of social behaviors in the social behavior data of all the target objects related to the close contacts and the propagation coefficient of the target objects;
and step S140, determining the high-risk infectious people based on the close contact score.
According to social behavior data of target objects (such as suspected and confirmed persons of infectious diseases), the joint sealing score is calculated for each unknown joint sealing object by constructing the social behavior and the propagation coefficient of the target objects to determine the joint sealing degree, so that high-risk infectious persons with high joint sealing degree, namely unknown object groups with high possibility of being infected, are found out, the high-risk joint sealing persons are comprehensively and accurately determined, the working efficiency of manual investigation during the epidemic situation is improved, and especially under the condition that medical resources are in short supply and medical detection cannot be carried out in a large area, important data support is provided for the epidemic situation investigation work in various places.
According to the embodiment of the present disclosure, in step S110, social behavior data of the target object is obtained.
Alternatively, the target subject may be a person diagnosed with an infectious disease or a person suspected of having an infectious disease.
Optionally, the social behavior data may include identity information, gender, age, residence, long-term residence, recent travel, etc. of the target object. The identity information may include a name (for example, a name after fuzzy processing, or a real name), an identification number, a passport number, an ID, and the like. Recent trips may include date of travel, mode of travel traffic and its information (e.g., flight number, train number, car number, seat number, net appointment license plate, etc.), areas of stay, symptoms and time of occurrence, etc.
In some embodiments, the social behavior data of the target objects reported in each region may be sorted, and the social behavior data sets of a plurality of target objects are processed. Because the mobility of the existing personnel is larger, social behavior data of a plurality of target objects in a plurality of regions are integrated, infectious disease relatives can be more comprehensively tracked, available data are more, and the tracking efficiency and the tracking effect are favorably improved.
Specifically, 1 confirmed diagnosis person L of an infectious disease inputted abroad was reported in X.Y. on A.B.month. The social behavior data of the identifying person L may include the following: the affirming personnel L, female, C year old, the place of residence is D province, E city, renting the G street H cell in the F cell of the X city. Recent trips include:
the departure from A1, B1 to A2, B2 and the arrival at J City of China I, and the arrival is kept in the J City.
When the flight number is K, the flight returns to the city X at A3, B3, day T1.
A4, B4, T2, arrived at airport of X city, monitored for normal body temperature by customs, and then arrived at rented land by taking a net appointment.
And B5 days A5, and going to supermarket M.
In A6 month and B6 day, fever and other symptoms appear, and the body temperature is 37.4 ℃.
Confirmation of infectious disease on day A7, month B7.
It should be appreciated that the social behavior data of the target object is merely an example, and is not intended to be limiting. Social behavior data may also include at least a portion thereof, or other content, without limitation.
According to the embodiment of the present disclosure, step S120 is to determine an intimate contact of the target object based on the social behavior data of the target object, and then determine other target objects related to the intimate contact based on the intimate contact.
Optionally, determining the close contact of the target object based on the social behavior data of the target object may include:
determining, based on social behavior data of the target object, an intimate contacter that intersects the target object within a first preset time period.
The intersection with the target object may refer to a situation that occurs simultaneously with the target object in a certain spatial area within a certain time range.
Optionally, determining other target objects associated with the close contact based on the close contact may include:
determining other target objects that intersect the close contact within a second preset time period.
Wherein, the intersection with the close contact person may refer to the situation that the close contact person and the close contact person occur simultaneously in a certain space area within a certain time range.
According to the embodiment of the present disclosure, step S130, the type of social behavior in the social behavior data of all target objects associated with the close contacts and the propagation coefficient of the target object calculate the close contact score of each close contact of the target object.
Optionally, the social behavior data includes a plurality of different types of social behaviors. Further, each type of social behavior may correspond to a different base score.
In some embodiments, social behaviors include, but are not limited to, the following types: closed places (e.g., a co-room, iOS, and Android device exposure notifications, etc.), public places (e.g., hotels, malls, stations, supermarkets, etc.), or vehicles (e.g., buses, subways, flights, motor cars, network appointments, etc.). Further, in some embodiments, the closed space type social behavior may have a first score, such as 30. The public place type social behavior may have a second score, e.g., 10. The social behavior of the vehicle type may have a third score, e.g., 20.
Further, in some embodiments, for the same type of social behavior, at least one sub-category may be included, which may be a classification of the type of social behavior based on a finer granularity (e.g., seating relationships). In some embodiments, different sub-categories may correspond to different additional scores. Further, if a social behavior belongs to a subclass of a certain type of social behavior, the score of the social behavior may include the basic score of the type of social behavior plus an additional score corresponding to the subclass. For example, for a social behavior of a vehicle type, the base score may be 20. The social behavior of this vehicle type can be classified based on seat relationships into the following sub-categories: the co-compartment subclass corresponds to a first additional score, such as +0, and if the social behavior was in the same compartment as the target object, the social behavior is the co-compartment subclass for the vehicle type, and the score for the social behavior is 20 points, which is the base score of 20 points plus the first additional score of +0 points. A front-back subclass corresponding to a second additional score, for example +6, then if the target object was in a front-back positional relationship with the social behavior, the social behavior is a front-back subclass of the vehicle type, and the score of the social behavior is the base score of 20 plus the second additional score of +6, i.e., 26. And a neighboring seat subclass corresponding to a third additional score, for example +9 points, if the social behavior was in a neighboring seat position relationship with the target object, the social behavior is the neighboring seat subclass of the vehicle type, and the score of the social behavior is the base score of 20 points plus the third additional score of +9 points, i.e. 29 points. From this, it is understood that the higher the score of the social behavior, the higher the degree of contact with the target object, and the higher the possibility of infection.
Optionally, the propagation coefficient of the target object may include: the confirmed person has a first propagation coefficient and the suspected person has a second propagation coefficient, wherein the first propagation coefficient is greater than the second propagation coefficient. In some embodiments, the first propagation coefficient may be 1 and the second propagation coefficient may be 0.6.
Optionally, calculating a closeness score for each of the intimate contacts of the target object comprises:
the seal fraction M is calculated according to the following formula:
For example, in the above embodiment, the relevant close contacts and their closeness scores for the target object L may include:
| sealing person | Tight integral | Contact with the source of infection |
| A1 | 90 | 6 persons who touch the doubtful person and act 6 times |
| A2 | 65 | 5 persons who touch the doubtful person and conduct 5 times |
| A3 | 65 | 5 persons who touch the doubtful person and conduct 5 times |
| A4 | 47 | Contact with the suspected person for 3 times, conduct 3 times |
| A5 | 35 | 2 persons who touch the doubtful person and act for 2 times |
Among them, the contact condition of the close contact a4 may include:
according to an embodiment of the present disclosure, step S140, a high-risk infectious person is determined based on the seal contact score.
Optionally, determining a high risk infectious agent based on the seal score may include:
and comparing the close contact score with a preset threshold value, and determining that the close contact person corresponding to the close contact score higher than the preset threshold value is a high-risk infectious person.
Wherein a higher contact score indicates a higher risk of infection. Therefore, high-risk personnel with infectious diseases can be comprehensively, quickly and accurately determined under the condition that the public cannot be comprehensively and medically checked, the checking efficiency is improved, and the checking effect is ensured.
According to an embodiment of the present disclosure, the method further comprises:
and S150, analyzing the propagation path and/or the super propagator based on the social behavior data of the target object.
Wherein, the superpropagator can refer to the target object with infection frequency higher than the preset frequency.
Optionally, analyzing the propagation path based on the social behavior data of the target object may include:
analyzing other target objects contacted by each target object, and determining the super propagators in all the target objects based on the reverse order arrangement of the contact times;
constructing a propagation path of the target object based on the superpropagator and the attack time of the target object.
The propagator whose onset time is earlier than the doubtful person, i.e. the target object, can be defined as the source propagator, and the propagator whose onset time is later than the doubtful person is the propagator. If the number of contacts exceeds a certain amount, a super-propagator can be defined, and a more complete propagation path can be constructed. For example:
| allegedly confirmed person | Time of onset | Contact times ↓ | Source propagator | The person to be transmitted |
| X | 2020/1/15 | 153 | X1、X2、……Xn | Xa、Xb、……Xm |
| Y | 2020/1/17 | 112 | Y1、Y2、……Yn | Ya、Yb、……Ym |
| Z | 2020/1/13 | 110 | Z1、Z2、……Zn | Za、Zb、……Zm |
In some embodiments, referring to fig. 2, fig. 2 shows a schematic example of a method according to an embodiment of the present disclosure. As shown in fig. 2, the social behavior-based infectious disease tracking method includes:
step 1: forming a social behavior data set of a target object, namely an alleged person; executing the step 2;
step 2: calculating joint sealing personnel meeting the space-time condition according to the social behavior of the suspected personnel within a period of time; executing the step 3;
and step 3: calculating the joint sealing score of each joint sealing person according to the score of the social behavior and the propagation coefficient of the suspected person; wherein, still include:
step 3-1: calculating a score of the social behavior;
step 3-2: calculating a propagation coefficient;
executing the step 4;
and 4, step 4: finding out high-risk infectious people, namely a group of objects which are not shown and have high contact sealing degree and high possibility of being infected; executing the step 5;
and 5: and analyzing the propagation path to find out the super propagator.
Therefore, according to the infectious disease tracking method based on social behaviors, the high-risk close-contact person can be accurately pushed based on the data of the existing doubtful persons, the working efficiency of manual investigation during the epidemic situation is improved, and especially under the conditions that medical resources are in short supply and nucleic acid detection cannot be carried out in a large area, important data support can be provided for the epidemic situation investigation work in various regions.
It should be noted that the method of one or more embodiments of the present disclosure may be performed by a single device, such as a computer or server. The method of the embodiment can also be applied to a distributed scene and completed by the mutual cooperation of a plurality of devices. In such a distributed scenario, one of the devices may perform only one or more steps of the method of one or more embodiments of the present disclosure, and the devices may interact with each other to complete the method.
It is noted that the above describes specific embodiments of the present disclosure. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.
Based on the same inventive concept, corresponding to any embodiment method, one or more embodiments of the present disclosure further provide an infectious disease tracking device based on social behaviors.
Referring to fig. 3, the social behavior-based infectious disease tracking apparatus includes:
the acquisition module is used for acquiring social behavior data of the target object;
the close contact confirmation module is used for determining close contacts of the target object based on social behavior data of the target object and then determining other target objects related to the close contacts based on the close contacts;
the score calculation module is used for calculating the close contact score of each close contact person of the target object based on the type of social behaviors in the social behavior data of all target objects related to the close contact persons and the propagation coefficient of the target object;
and the risk confirmation module is used for determining the high-risk infectious personnel based on the joint sealing score.
For convenience of description, the above devices are described as being divided into various modules by functions, and are described separately. Of course, the functionality of the various modules may be implemented in the same one or more software and/or hardware implementations in implementing one or more embodiments of the present disclosure.
The apparatus of the above embodiment is used to implement the social behavior-based infectious disease tracking method in any of the foregoing embodiments, and has the beneficial effects of the corresponding method embodiment, which are not described herein again.
Based on the same inventive concept, corresponding to any of the above embodiments, one or more embodiments of the present disclosure further provide an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and when the processor executes the computer program, the social behavior-based infectious disease tracking method according to any of the above embodiments is implemented.
Fig. 4 is a schematic diagram illustrating a more specific hardware structure of an electronic device according to this embodiment, where the electronic device may include: a processor 410, a memory 420, an input/output interface 430, a communication interface 440, and a bus 450. Wherein processor 410, memory 420, input/output interface 430, and communication interface 440 are communicatively coupled to each other within the device via bus 450.
The processor 410 may be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an Application Specific Integrated Circuit (ASIC), or one or more Integrated circuits, and is configured to execute related programs to implement the technical solutions provided by the embodiments of the present disclosure.
The Memory 420 may be implemented in the form of a ROM (Read Only Memory), a RAM (Random Access Memory), a static storage device, a dynamic storage device, or the like. The memory 420 may store an operating system and other application programs, and when the technical solution provided by the embodiments of the present disclosure is implemented by software or firmware, the relevant program codes are stored in the memory 420 and called to be executed by the processor 410.
The input/output interface 430 is used for connecting an input/output module to realize information input and output. The i/o module may be configured as a component in a device (not shown) or may be external to the device to provide a corresponding function. The input devices may include a keyboard, a mouse, a touch screen, a microphone, various sensors, etc., and the output devices may include a display, a speaker, a vibrator, an indicator light, etc.
The communication interface 440 is used for connecting a communication module (not shown in the figure) to realize communication interaction between the device and other devices. The communication module can realize communication in a wired mode (such as USB, network cable and the like) and also can realize communication in a wireless mode (such as mobile network, WIFI, Bluetooth and the like).
Bus 450 includes a pathway to transfer information between various components of the device, such as processor 410, memory 420, input/output interface 430, and communication interface 440.
It should be noted that although the above-mentioned device only shows the processor 410, the memory 420, the input/output interface 430, the communication interface 440 and the bus 450, in a specific implementation, the device may also include other components necessary for normal operation. Moreover, those skilled in the art will appreciate that the above-described apparatus may also include only those components necessary to implement the embodiments of the present disclosure, and need not include all of the components shown in the figures.
The electronic device of the above embodiment is used to implement the social behavior-based infectious disease tracking method in any of the foregoing embodiments, and has the beneficial effects of the corresponding method embodiment, which are not described herein again.
Based on the same inventive concept, corresponding to any of the above-described embodiment methods, one or more embodiments of the present disclosure also provide a non-transitory computer-readable storage medium storing computer instructions for causing the computer to perform the social behavior-based infectious disease tracking method according to any of the above-described embodiments.
Computer-readable media of the present embodiments, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device.
The storage medium of the above embodiment stores computer instructions for causing the computer to execute the social behavior-based infectious disease tracking method according to any of the above embodiments, and has the beneficial effects of the corresponding method embodiments, which are not described herein again.
Those of ordinary skill in the art will understand that: the discussion of any embodiment above is meant to be exemplary only, and is not intended to intimate that the scope of the disclosure, including the claims, is limited to these examples; features from the above embodiments or from different embodiments may also be combined, steps may be implemented in any order, and there are many other variations of different aspects of one or more embodiments of the disclosure as described above, which are not provided in detail for the sake of brevity, within the spirit of the disclosure.
In addition, well-known power/ground connections to Integrated Circuit (IC) chips and other components may or may not be shown in the provided figures, for simplicity of illustration and discussion, and so as not to obscure one or more embodiments of the disclosure. Furthermore, devices may be shown in block diagram form in order to avoid obscuring one or more embodiments of the present disclosure, and this also takes into account the fact that specifics with respect to implementation of such block diagram devices are highly dependent upon the platform within which one or more embodiments of the present disclosure are to be implemented (i.e., specifics should be well within purview of one skilled in the art). Where specific details (e.g., circuits) are set forth in order to describe example embodiments of the disclosure, it should be apparent to one skilled in the art that one or more embodiments of the disclosure can be practiced without, or with variation of, these specific details. Accordingly, the description is to be regarded as illustrative instead of restrictive.
While the present disclosure has been described in conjunction with specific embodiments thereof, many alternatives, modifications, and variations of these embodiments will be apparent to those of ordinary skill in the art in light of the foregoing description. For example, other memory architectures (e.g., dynamic ram (dram)) may use the discussed embodiments.
The one or more embodiments of the present disclosure are intended to embrace all such alternatives, modifications and variances that fall within the broad scope of the appended claims. Therefore, any omissions, modifications, substitutions, improvements, and the like that may be made without departing from the spirit and principles of one or more embodiments of the disclosure are intended to be included within the scope of the disclosure.
Claims (10)
1. An infectious disease tracking method based on social behaviors is characterized by comprising the following steps:
acquiring social behavior data of a target object;
determining an intimate contact of the target object based on social behavior data of the target object, and determining other target objects related to the intimate contact based on the intimate contact;
calculating a closeness score of each close contact of the target objects based on the types of social behaviors in the social behavior data of all target objects associated with the close contacts and the propagation coefficient of the target objects;
determining a high risk infectious agent based on the seal close score.
2. The method of claim 1, wherein calculating the affinity score for each of the target objects based on the type of social activity in the social activity data of all target objects associated with the close contacts and the propagation coefficient of the target object comprises:
the seal fraction M is calculated according to the following formula:
3. The method of claim 2, wherein the score of the social behavior comprises a base score of the social behavior, wherein the social behavior data comprises a plurality of different types of social behaviors, and wherein each type of social behavior corresponds to a different base score.
4. The method of claim 3, wherein the scores of social behaviors comprise a base score and an additional score of social behaviors, wherein at least one subclass is included for the same type of social behavior, and wherein each subclass corresponds to a different additional score.
5. The method of claim 2, wherein the target objects include confirmed and suspected persons of infectious disease, and the target objects' transmission coefficients include: the confirmed person has a first propagation coefficient and the suspected person has a second propagation coefficient, wherein the first propagation coefficient is greater than the second propagation coefficient.
6. The method of claim 1, wherein determining a high risk infectious agent based on the seal contact score comprises:
and comparing the close contact score with a preset threshold value, and determining that the close contact person corresponding to the close contact score higher than the preset threshold value is a high-risk infectious person.
7. The method of claim 1, further comprising: analyzing a propagation path and/or a superpropagator based on social behavior data of the target object.
8. An infectious disease tracking device based on social behaviors, comprising:
the acquisition module is used for acquiring social behavior data of the target object;
the close contact confirmation module is used for determining close contacts of the target object based on social behavior data of the target object and then determining other target objects related to the close contacts based on the close contacts;
the score calculation module is used for calculating the close contact score of each close contact person of the target object based on the type of social behaviors in the social behavior data of all target objects related to the close contact persons and the propagation coefficient of the target object;
and the risk confirmation module is used for determining the high-risk infectious personnel based on the joint sealing score.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the method according to any of claims 1 to 7 when executing the program.
10. A non-transitory computer readable storage medium storing computer instructions for causing a computer to perform the method of any one of claims 1 to 7.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114420306A (en) * | 2022-01-21 | 2022-04-29 | 腾讯烟台新工科研究院 | Infectious disease close contact person tracking system and method |
| CN114694850A (en) * | 2022-04-12 | 2022-07-01 | 北京交通大学 | Epidemic situation tight-seal crowd tracing method for subway network |
| CN115294778A (en) * | 2022-09-28 | 2022-11-04 | 四川科泰智能电子有限公司 | Regional vehicle access statistical method and system |
| WO2023246705A1 (en) * | 2022-06-22 | 2023-12-28 | 清华大学 | Epidemiological investigation data processing method and apparatus, and computer device |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20190164234A1 (en) * | 2017-11-30 | 2019-05-30 | The Johns Hopkins University | Method and apparatus for monitoring complex contagion and critical mass in online social media |
| CN112366000A (en) * | 2020-11-25 | 2021-02-12 | 重庆市卫生健康统计信息中心 | Method for predicting number of specific population in region during infectious disease transmission |
| CN112599253A (en) * | 2020-12-29 | 2021-04-02 | 医渡云（北京）技术有限公司 | Method, device, equipment and medium for determining epidemic situation propagation path according to close contact map |
-
2021
- 2021-07-07 CN CN202110768689.2A patent/CN113871023A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20190164234A1 (en) * | 2017-11-30 | 2019-05-30 | The Johns Hopkins University | Method and apparatus for monitoring complex contagion and critical mass in online social media |
| CN112366000A (en) * | 2020-11-25 | 2021-02-12 | 重庆市卫生健康统计信息中心 | Method for predicting number of specific population in region during infectious disease transmission |
| CN112599253A (en) * | 2020-12-29 | 2021-04-02 | 医渡云（北京）技术有限公司 | Method, device, equipment and medium for determining epidemic situation propagation path according to close contact map |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114420306A (en) * | 2022-01-21 | 2022-04-29 | 腾讯烟台新工科研究院 | Infectious disease close contact person tracking system and method |
| CN114694850A (en) * | 2022-04-12 | 2022-07-01 | 北京交通大学 | Epidemic situation tight-seal crowd tracing method for subway network |
| WO2023246705A1 (en) * | 2022-06-22 | 2023-12-28 | 清华大学 | Epidemiological investigation data processing method and apparatus, and computer device |
| CN115294778A (en) * | 2022-09-28 | 2022-11-04 | 四川科泰智能电子有限公司 | Regional vehicle access statistical method and system |
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