Information processing device

Abstract

To provide a device which improves accuracy of infection risk determination of an infection disease.SOLUTION: The infection risk estimation system comprises: means for acquiring positional information of a user; means for acquiring biological information of the user and information on an affecting disease; estimation means for estimating a disease infection risk (possibility of infection from a patient of an infection disease) of the user; and reporting means for reporting the disease infection risk of the user. The estimation means determines from the history of the positional information of a first user affected by a disease a second user who was in contact with the first user, estimates the active state of the first user in a period when the first user was in contact with the second user from the biological information of the first user, and estimates the infection risk of the second user from the active state of the first user and the feature of the disease. When it is estimated that the risk of the second user having infected by the disease is high, the reporting means gives a report on high possibility of the infection by the disease.SELECTED DRAWING: Figure 6

Description

[Technical Field]

[0001] The present invention relates to an infection risk estimation method, an information processing device, and a program for estimating an infection risk. [Background technology]

[0002] Infectious diseases that are transmitted from person to person spread through contact with people who are infected, through the air, or through droplets. People who are infected often have difficulty realizing that they are positive until they are tested, which can lead to the spread of infection to those around them, creating a problem.

[0003] As a technology for preventing the spread of infectious diseases, Patent Document 1 discloses a system that notifies a terminal of the results of a determination of whether or not the terminal is infected with an infectious disease, as well as information for restricting behavior and information regarding treatment based on the terminal's location information.

[0004] Furthermore, Non-Patent Document 1 (Novel Coronavirus Contact-Confirming App (COCOA)) describes the "Novel Coronavirus Contact-Confirming App (COCOA)" and discloses software that uses a smartphone's near-field communication function (Bluetooth (registered trademark)) to ensure privacy so that users cannot know each other and can receive notifications about possible contact with a person who has tested positive for COVID-19. Non-Patent Document 1 states that by knowing that users may have come into contact with a positive person, they can quickly receive support from the public health center, such as getting tested. [Prior art documents] [Patent documents]

[0005] [Patent Document 1] Japanese Patent Publication No. 2022-71523 [Non-patent literature]

[0006] [Non-Patent Document 1] Ministry of Health, Labor and Welfare, “COVID-19 Contact-Confirming Application (COCOA),” Internet <URL: https: / / www.mhlw.go.jp / stf / seisakunitsuite / bunya / cocoa_00138.html> Summary of the Invention [Problem to be solved by the invention]

[0007] The conventional technology disclosed in the above-mentioned patent document measures the distance and time between an infected person and a target user, and uses this as a criterion for determining the risk of infection as a close contact.

[0008] However, distance and time are indirect factors in the risk of infectious diseases, and the risk of infection cannot be estimated without examining whether or not a person has actually engaged in behavior that exposes them to infection. For example, if the infection route is droplet-based, whether or not a person has been exposed to droplets is an important factor, and time and distance are merely indirect indicators. Therefore, if a person is exposed to droplets even for a short period of time, they will become infected, and if they are not exposed to droplets even for a long period of time, they will not become infected. Behavior due to the infection route is an important factor in infection, and conventional technology cannot estimate this.

[0009] Therefore, an object of the present invention has been made in consideration of the above-mentioned problems, and is to provide a device that improves the accuracy of determining the risk of infection with infectious diseases. [Means for solving the problem]

[0010] In order to achieve the above-mentioned object, the infection risk estimation system of the present invention comprises a means for acquiring a user's location information, a means for acquiring the user's biometric information and information on the disease contracted, an estimation means for estimating the user's risk of contracting the disease (possibility of infection from an infectious disease patient), and a notification means for notifying the user of the disease infection risk, wherein the estimation means determines a second user who was in contact with a first user who has contracted the disease based on the location information history of the first user, estimates the activity state of the first user during the period when the first user was in contact with the second user based on the biometric information of the first user, and estimates the second user's risk of infection based on the activity state of the first user and the characteristics of the disease, and the notification means notifies the second user that there is a high possibility that the second user has contracted the disease if it is estimated that the second user has a high risk of contracting the disease. [Effects of the Invention]

[0011] According to the present invention, it is possible to provide a device with improved accuracy by using estimated behavior. [Brief explanation of the drawings]

[0012] [Figure 1] 1 is a configuration diagram of an infection risk estimation system according to an embodiment of the present invention. [Figure 2] FIG. 2 is a system configuration diagram of a mobile terminal according to the present embodiment. [Figure 3] 1 is a system configuration diagram of an infection risk estimation device according to an embodiment of the present invention. [Figure 4] FIG. 10 is a table showing infection risk score addition values ​​in this embodiment. [Figure 5] FIG. 10 is a table showing the influence of distance from an infectious disease patient in this embodiment. [Figure 6] FIG. 10 is a flowchart of infection risk estimation in this embodiment. [Figure 7] FIG. 10 is a table showing infection risk score addition values ​​in this embodiment. [Figure 8] FIG. 10 is a flowchart of infection risk estimation in this embodiment. [Figure 9] FIG. 4 is a notification determination table according to the present embodiment. [Figure 10] FIG. 10 is a flowchart of notification determination in the present embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0013] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Preferred embodiments of the present invention will now be described in detail with reference to the accompanying drawings. Figure 1 is a block diagram of an infection risk estimation system according to an embodiment of the present invention.

[0014] (First embodiment) An infection risk estimation system according to a first embodiment of the present invention will now be described with reference to the drawings. As shown in Fig. 1, the infection risk estimation system 1 includes an infection risk estimation device 2, at least one mobile terminal 3, and a wearable terminal 4. The infection risk estimation device 2 and the mobile terminal 3 are connected via a network 5. The network 5 is, for example, the Internet.

[0015] The infection risk estimation device 2 is, for example, a server device or a computer such as a PC (Personal Computer). The mobile terminal 3 is, for example, a tablet terminal, a smartphone, or a computer such as a PC. The mobile terminal 3 is an example of a terminal in this embodiment. The wearable terminal 4 is a terminal that can be worn by a target user (including infected individuals and those at risk of infection) and measures the user's vital data. Alternatively, the mobile terminal 3 and the wearable terminal 4 may be collectively referred to as an example of a terminal in this embodiment.

[0016] <Mobile device description> The mobile terminal 3 includes an input device 101 , a display device 102 , a network connection device 103 , a memory circuit 104 , and a processing circuit 105 .

[0017] The input device 101 is realized by a trackball, switch buttons, a mouse, a keyboard, a touchpad that performs input operations by touching the operation surface, a touchscreen that integrates a display screen and a touchpad, a non-contact input circuit using an optical sensor, a voice input circuit, etc. The input device 101 is connected to a processing circuit 105, and converts input operations received from the user into electrical signals and outputs them to the processing circuit 105.

[0018] In this specification, the input device 101 is not limited to devices equipped with physical operation parts such as a mouse, a keyboard, etc. For example, an example of the input device 101 also includes an electrical signal processing circuit that receives an electrical signal corresponding to an input operation from an external input device provided separately from the device and outputs this electrical signal to the processing circuit 105.

[0019] The display device 102 is a liquid crystal display, an organic electroluminescence (OEL) display, or the like. The input device 101 and the display device 102 may be integrated. For example, the input device 101 and the display device 102 may be realized by a touch panel. The display device 102 is an example of a display unit or an output unit. Alternatively, the entire mobile terminal 3 may be an example of a display unit or an output unit.

[0020] The network connection device 103 is connected to the processing circuit 105, and controls the transmission and communication of various data between the infection risk estimation device 2 and the mobile terminal 3. The network connection device 103 is realized by a network card, a network adapter, a NIC (Network Interface Controller), etc. The network connection device 103 also controls the transmission and communication of various data between the mobile terminal 3 and the wearable terminal 4.

[0021] The memory circuitry 104 is connected to the processing circuitry 105 and stores various types of information and programs used by the processing circuitry 105.

[0022] The memory circuit 104 is realized by, for example, a semiconductor memory element such as a RAM (Random Access Memory), a flash memory, a hard disk, an optical disk, etc. The memory circuit 104 is also called a memory unit.

[0023] The processing circuitry 105 is a processor that reads out and executes programs from the storage circuitry 104 to realize functions corresponding to the programs. The processing circuitry 105 of this embodiment includes an acquisition unit 106, a reception unit 107, a transmission unit 108, and a display control unit 109.

[0024] Here, for example, each processing function of the components of the processing circuitry 105, namely, the acquisition unit 106, the transmission unit 108, the reception unit 107, and the display control unit 109, is stored in the storage circuitry 104 in the form of a computer-executable program. The processing circuitry 105 is a processor. For example, the processing circuitry 105 realizes the function corresponding to each program by reading and executing the program from the storage circuitry 104. Note that, in FIG. 2, the processing functions performed by the acquisition unit 106, the transmission unit 108, the reception unit 107, and the display control unit 109 are described as being realized by a single processor. However, the processing circuitry 105 may be configured by combining multiple independent processors, and each processor may realize a function by executing a program. Furthermore, in FIG. 2, the single storage circuitry 104 is described as storing a program corresponding to each processing function. However, a configuration may also be adopted in which multiple storage circuits are distributed and the processing circuitry 105 reads the corresponding program from each individual storage circuit.

[0025] The acquisition unit 106 acquires the user's vital data and the user's location information from the wearable device 3.

[0026] The transmission unit 108 transmits the user's vital data and user's location information to the infection risk estimation device 2 via the network connection device 103. Note that the user's vital data and user's location information transmitted by the transmission unit 108 are not limited to those acquired by the acquisition unit 106, and may be those measured by the mobile terminal 3.

[0027] Furthermore, the transmission unit 108 transmits the contents of the infection information input by the user to the infection risk estimation device 2.

[0028] The receiving unit 107 receives the notification from the infection risk estimation device 2 via the network connection device 103 .

[0029] The display control unit 109 causes the display device 102 to display a notification regarding the user's risk of infection.

[0030] <Wearable device description> A wearable terminal is a measuring device that can be worn on the user's body and measures the user's vital data. In FIG. 2, the wearable terminal 4 is a wristwatch-type smart watch or a ring-type smart ring, but the form of the wearable terminal 4 is not limited to these. Also, measuring devices other than the wearable terminal 4 may be used. For example, a thermometer, room temperature gauge, or smart home appliance equipped with various sensors that can communicate with the mobile terminal 3 may be used as the measuring device.

[0031] Examples of the user's vital data measured by the wearable device 4 include body temperature, blood pressure, pulse rate, blood oxygen saturation, blood glucose level, voice data, sweat components, etc. Note that one wearable device 4 may have multiple functions, or multiple wearable devices 4 may be used.

[0032] The wearable terminal 4 also measures the user's location information, for example, GPS information of the wearable terminal 4. Alternatively, the location information may be GPS information of the mobile terminal 3.

[0033] The wearable terminal 201 transmits the measured vital data and the user's location information to the mobile terminal 3.

[0034] <Explanation of the infection risk estimation device> The configuration of the infection risk estimation device 2 will be described with reference to Fig. 3. In this embodiment, an infectious disease such as influenza, which has an infection route via droplets, is taken as an example.

[0035] The infection risk estimation device 2 includes a network connection device 203, a memory circuitry 204, an input device 201, a display device 202, and a processing circuitry 205. The hardware configurations of the network connection device 203, the memory circuitry 204, the input device 201, and the display device 202 are, for example, similar to the hardware configurations of the network connection device 103, the memory circuitry 104, the input device 201, and the display device 202 of the mobile terminal 3 described above.

[0036] The network connection device 203 is connected to the processing circuit 205 and controls the transmission and communication of various data between the infection risk estimation device 2 and the mobile terminal 3.

[0037] The memory circuitry 204 is connected to the processing circuitry 205 and stores various information and programs used by the processing circuitry 205. In this embodiment, the memory circuitry 204 also stores information related to the user's vital data and user's location information transmitted from the mobile terminal 3 in association with the measurement time.

[0038] The input device 201 is connected to a processing circuit 205 , converts an input operation received from a user into an electrical signal, and outputs the electrical signal to the processing circuit 205 .

[0039] The processing circuitry 205 is a processor that reads out and executes programs from the storage circuitry 204 to realize functions corresponding to the programs. The processing circuitry 205 of this embodiment includes an acquisition unit 206, an estimation unit 207, a transmission unit 208, a display control unit 209, and a notification determination unit 210.

[0040] Here, for example, the processing functions of the acquisition unit 206, the estimation unit 207, the transmission unit 208, the display control unit 209, and the notification determination unit 210, which are components of the processing circuitry 205, are stored in the storage circuitry 204 in the form of computer-executable programs. The processing circuitry 205 is a processor. For example, the processing circuitry 205 realizes the functions corresponding to each program by reading and executing the programs from the storage circuitry 204. In other words, the processing circuitry 205 in a state in which each program has been read out has each process shown in the processing circuitry 205 of FIG. 3. Note that, although FIG. 3 illustrates the processing functions performed by the acquisition unit 206, the estimation unit 207, the notification determination unit 210, the medical interview processing function 154, and the output control function 155 being realized by a single processor, the processing circuitry 205 may be configured by combining multiple independent processors, and each processor may realize a function by executing a program. Furthermore, although FIG. 1 illustrates a single memory circuit 204 storing a program corresponding to each processing function, multiple memory circuits may be distributed and arranged, and the processing circuit 205 may read out the corresponding program from each memory circuit.

[0041] In the above description, an example has been described in which a "processor" reads and executes a program corresponding to each function from a storage circuit. However, the embodiment is not limited to this. In this embodiment, the term "processor" refers to a circuit such as a central processing unit (CPU), a graphics processing unit (GPU), an application-specific integrated circuit (ASIC), or a programmable logic device (e.g., a field programmable gate array (FPGA)). If the processor is a CPU, for example, the processor realizes the function by reading and executing a program stored in a storage circuit. On the other hand, if the processor is an ASIC, instead of storing the program in the storage circuit 204, 220, the function is directly incorporated as a logic circuit within the processor circuit. Note that each processor in this embodiment is not limited to being configured as a single circuit, but may be configured as a single processor by combining multiple independent circuits to realize its function. Furthermore, multiple components in FIG. 3 may be integrated into a single processor to realize its function.

[0042] The acquisition unit 206 acquires the subject's vital data, location information, and risk information (infection, location information, and vital data of infectious disease patient P). The location information is information indicating when and where the subject was. The vital data is data acquired from the wearable device 4, and the risk information is information from other infectious disease patients.

[0043] In this embodiment, the acquisition unit 206 acquires the vital data and location information of the user from, for example, the mobile terminal 3. The source of the user's vital data and location information is the mobile terminal 3 or the wearable terminal 4. Risk information is acquired by inputting infectious disease information from the user himself / herself or a third party such as a medical institution staff member.

[0044] The estimation unit 207 estimates the activity state based on the acquired vital data. It also acquires the contact state with the infectious disease patient from the location information, calculates the infection risk score based on the contact state and the activity state of the infectious disease patient, and determines the infection risk level based on the calculated result.

[0045] The activity state is estimated from vital data acquired from the wearable device 4, but estimation may also be performed by extracting feature amounts from video or still images captured by a camera, for example.

[0046] The obtained estimated values ​​of the activity state are stored in the memory circuit 204 in association with time.

[0047] In this example, behaviors that are likely to cause droplet infection were estimated to be those in which people are speaking, breathing heavily such as eating or exercising, situations in which attention is reduced due to alcohol consumption, and silence when not doing anything in particular.

[0048] To estimate the speaking state, the system identifies the user's voice from their voiceprint and measures the volume of the voice (loudness of the sound). The measured value is divided into two levels: "loud" and "quiet," and saved in association with the time the voice was spoken. A voice volume of 60dB or more is considered loud, and one below 60dB is considered quiet. The louder the voice volume, the higher the added score is set, as it is more likely that an infected person is spreading droplets.

[0049] Next, meal times are estimated based on blood glucose measurement results. In this example, the meal start time is recorded as 10 minutes before the blood glucose level begins to rise, and the meal end time is recorded as 2 hours before the blood glucose level peaks. Because the relationship between blood glucose level rise and meals varies between individuals, particularly in the presence of illness, it is advisable to change the meal time estimation parameters and estimation method using the user's health registration information, etc.

[0050] Additionally, the state of rapid breathing is estimated from the heart rate, and whether or not the person is drinking alcohol is estimated from the measured blood alcohol concentration. In this embodiment, a heart rate of 100 or more and a blood alcohol concentration of 0.1% or more are defined as the state of alcohol ingestion. Furthermore, since the higher the blood alcohol concentration, the lower the level of attention tends to be, so the blood alcohol concentration may be classified into stages such as high and low.

[0051] In this embodiment, data acquired by a wearable device is described, but the behavior of an infectious disease patient may also be estimated by performing image analysis on still images or videos that record the patient's behavior.

[0052] Next, we will explain how to calculate the infection risk score.

[0053] Figure 4 shows the infection risk score addition value corresponding to the activity state of an infectious disease patient, and Figure 5 shows the influence of distance from an infectious disease patient. The product of the score and influence of the user whose infection risk is estimated is added every unit second.

[0054] The added value changes depending on the activity of the infected person. For example, if an infected person is nearby and the user is doing nothing, they will not be exposed to droplets, and the added value will be 0. On the other hand, if they are speaking, the droplets will be larger.

[0055] Furthermore, the score table shown in this example is merely an example of droplet infection, and in the case of an infectious disease whose infection route is airborne, for example, simply being in the same space creates an infection risk, so even if you do nothing, the infection risk score addition value will not become 0, and the infection risk score addition value will increase overall. Therefore, it is advisable to change the parameters depending on the characteristics of the infectious disease in question (infection route, infectivity, etc.).

[0056] The estimation method is an example, and the estimation unit 207 may estimate the user's disease candidate using a trained model, a mathematical model, or other method. For example, the estimation unit 207 may estimate the infection risk of the target user whose infection risk is to be estimated using a trained model that associates contact status information between the user and an infectious disease patient, the infectious disease the infectious disease patient is infected with, and the activity status of the infectious disease patient. The trained model is, for example, a trained model generated by deep learning such as a neural network. As a deep learning method, a convolutional neural network (CNN), a recurrent neural network (RNN), or the like can be applied, but is not limited to these.

[0057] The estimation unit 207 sends the calculated infection risk score to the notification necessity determination function.

[0058] The notification determination unit 210 determines whether or not a notification is required for the user whose infection risk is to be estimated, based on the score estimated by the estimation unit 207.

[0059] When the notification determination unit 210 determines that notification to the user whose infection risk is estimated is necessary, it sends the notification content to the transmission unit 208.

[0060] The transmitting unit 208 outputs a notification regarding the disease candidate estimated by the estimation unit 207. In this embodiment, the transmitting unit 208 outputs the notification regarding the disease candidate estimated by the estimation unit 207 to the mobile terminal 3 held by the user whose infection risk is to be estimated. The transmitting unit 208 displays the notification or a medical interview screen including medical interview items on the display device 202 of the mobile terminal 3, for example, by transmitting the notification content and a control signal to the mobile terminal 3.

[0061] The notification content may be, for example, a message indicating a suspected disease, such as "You may have had close contact," or a message instructing how to deal with the suspected disease, such as "Please consult a medical institution."

[0062] Next, the flow of processing executed by the infection risk estimation device 2 configured as above will be described.

[0063] 6 is a flowchart showing an example of the flow of the infection risk notification process for infection risk estimation target user B, which is executed by the infection risk estimation device 2 according to the first embodiment. The process of this flowchart is executed repeatedly at regular intervals, for example. Alternatively, the process of this flowchart may be executed when the mobile terminal 3 collects target data.

[0064] Next, a detailed description will be given using the flowchart of FIG.

[0065] First, when an infectious disease patient A tests positive for an infectious disease, the infectious disease is registered via the input device 201 (S101).

[0066] The distance at which the impact level does not become zero and the contact state is obtained from the infectious disease score impact level table (S102).

[0067] Also, a target period for estimating the infection risk is acquired. The period can be set freely, but it is preferable to set a period that is the number of days before the time when the infected person contracted the infectious disease and the incubation period of the infectious disease (S103).

[0068] The distance corresponding to the acquired contact state and whether the infectious disease patient A and the infection risk estimated target user B were in contact during the estimation target period are determined from the location information (S104).

[0069] If there is a contact state, information on the contact state (date, time and distance) is acquired, and if there is no contact state, the process ends (S105).

[0070] The activity state of the infectious disease patient A is obtained according to the date and time of contact (S106).

[0071] An infection risk score for user B is obtained from the activity status of infectious disease patient A corresponding to the contact status (S107).

[0072] The notification determination unit determines whether to notify user B. If the infection risk score exceeds a preset threshold, it determines that notification is necessary. In this embodiment, the threshold is set to 600. If the threshold is not exceeded, this flow ends (S108).

[0073] The notification is displayed on the display device. Specifically, a notification and a control signal related to the infectious disease estimated by the estimation unit 207 are transmitted to the mobile terminal 3 held by the infection risk estimated user B, causing the mobile terminal 3 to display the notification (S109).

[0074] Then, the flow ends.

[0075] In this way, the infection risk estimation device of this embodiment estimates the activity state of a target infectious disease patient, calculates an infection risk score from the obtained activity state and the contact state between the infectious disease patient and the infection risk estimation target user, and estimates the infection risk. Therefore, it is possible to estimate the infectious disease risk of the infection risk estimation target user based on actions that lead to infection.

[0076] Furthermore, according to this embodiment, behavior and the like can be acquired automatically, which eliminates the need for interview surveys and reduces the burden on medical professionals and staff.

[0077] (Second embodiment) In the first embodiment described above, the infection risk estimation device performed estimation using the activity state of the infectious disease patient, but in the second embodiment, estimation is performed using the activity state of the infection risk estimation target user as well. For example, even if the infectious disease patient is speaking, the degree of exposure to droplets will vary depending on whether the infection risk estimation target user is also speaking and having a conversation. Similarly, the degree of exposure to droplets will also vary depending on the attention level of the infection risk estimation target user. Therefore, the infection risk is estimated using the activity state of the infection risk estimation target user and the infectious disease patient.

[0078] The device and acquisition method used in this embodiment are the same as those in the first embodiment, so a description thereof will be omitted, and the estimation unit 207 that calculates the infection risk score will be described.

[0079] Figure 7 shows the infection risk score addition value corresponding to the activity state of an infectious disease patient. The product of the score of the user whose infection risk is estimated and the influence of the distance from the infectious disease patient shown in Figure 5 is added every second.

[0080] Furthermore, the score table shown in this example is merely an example of droplet infection, and in the case of an infectious disease whose infection route is airborne, for example, simply being in the same space creates an infection risk, so even if you do nothing, the infection risk score addition value will not become 0, and the infection risk score addition value will increase overall. Therefore, it is a good idea to change the parameters for each infectious disease in question.

[0081] Next, the flow of processing executed by the infection risk estimation device 2 configured as above will be described.

[0082] 8 is a flowchart showing an example of the flow of the infection risk notification process for infection risk estimation target user B, which is executed by the infection risk estimation device 2 according to the second embodiment. The process of this flowchart is executed repeatedly at regular intervals, for example. Alternatively, the process of this flowchart may be executed when the mobile terminal 3 collects target data.

[0083] Next, a detailed description will be given using the flowchart of FIG.

[0084] First, when an infectious disease patient A tests positive for the infection, the infection is registered via the input device 201 (S201).

[0085] The distance at which the impact level does not become zero and the contact state is obtained from the infectious disease score impact level table (S202).

[0086] Also, the target period for estimating the infection risk is acquired. The period can be set freely, but it is preferable to set the period before the time when the infected person contracted the infectious disease and the number of days before the incubation period of the infectious disease (S203).

[0087] The distance corresponding to the acquired contact state and whether the infectious disease patient A and the infection risk estimated user B were in contact during the estimated target period are determined from the location information (S204).

[0088] If there is a contact state, information on the contact state (date, time and distance) is acquired, and if there is no contact state, the process ends (S205).

[0089] The activity status of the infectious disease patient A and the infection risk estimated target user B is obtained according to the date and time of contact (S206).

[0090] An infection risk score for user B is obtained from the activity status of infectious disease patient A corresponding to the contact status and user B whose infection risk is estimated (S207).

[0091] The notification determination unit determines whether to notify user B, who is a target of estimated infection risk. If the infection risk score exceeds a preset threshold, it is determined that notification is necessary. In this embodiment, the threshold is set to 600. If the threshold is not exceeded, this flow ends (S208).

[0092] The notification is displayed on the display device. Specifically, a notification and a control signal related to the infectious disease estimated by the estimation unit 207 are transmitted to the mobile terminal 3 held by the infection risk estimated user B, causing the mobile terminal 3 to display the notification (S209).

[0093] Then, the flow ends.

[0094] In this way, the infection risk estimation device of this embodiment estimates the activity status of the target infectious disease patient and the infection risk estimation target user, calculates an infection risk score from the obtained activity status and the contact status between the infectious disease patient and the infection risk estimation target user, and estimates the infection risk. Therefore, by estimating the infection risk of the infection risk estimation target user based on a combination of the infection risk and the behavior of the infected party, it is possible to make an estimate more accurately than when estimating the infection risk based only on the activity status of the infectious disease patient.

[0095] (Third embodiment) In the third embodiment, notifications for users with an estimated risk of infection are changed depending on user information. Infectious diseases are more likely to become severe if the user is elderly or has an underlying illness. Therefore, the level of caution is changed depending on basic information such as the user's age and disease state, and notification is determined.

[0096] In this embodiment, the functions other than the notification determination unit 210 are common, so a description thereof will be omitted.

[0097] In Examples 1 and 2, notifications were made when the threshold value was equal to or greater than a certain value, but in this example, notifications are made according to the notification determination table shown in Figure 9. The notification determination table is made up of age and disease, with each number indicating the notification threshold for the infection risk score. The older the person, the lower the threshold, and the threshold is set higher when there is no disease compared to when there is no disease. Diseases here include, for example, underlying diseases such as diabetes, heart disease, and kidney disease, as well as other diseases.

[0098] Next, the flow of the notification determination unit 210 shown in FIG. 10 will be described.

[0099] First, the infection risk score of the user suspected of infection calculated by the estimation unit 207 is obtained (S301).

[0100] Basic health information of the suspected infected user is acquired. The basic health information is stored in, for example, the memory circuitry 204 and acquired by the acquisition unit 206. Alternatively, the basic health information may be acquired from a user health management database such as an electronic medical record (not shown) via the network connection device 203 (S302).

[0101] Based on the infection risk score and the basic health information data, the notification determination table value shown in FIG. 9 is compared with the infection risk score (S303).

[0102] If the notification determination table value is exceeded, a notification is made and the flow is terminated (S304, S305). If the notification determination table value is not exceeded, the flow is terminated without making a notification (S304).

[0103] In this way, the infection risk estimation device of this embodiment changes the notification judgment depending on the health condition of the user whose infection risk is to be estimated, and by notifying users who are at high risk of developing severe symptoms even if their infection risk estimation score is low, the user can take early action.

[0104] (Fourth embodiment) In the fourth embodiment, if the infection risk score of a user whose infection risk is to be estimated is high, the infection risk of user C, who has been in contact with the person with an infectious disease since the user whose infection risk is to be estimated came into contact with the person with the infectious disease until the present, is estimated and notified using the flow of Examples 1, 2, and 3. The device and flow are the same as those of Examples 1, 2, and 3, and explanations thereof are omitted as they are self-explanatory.

[0105] Although the preferred embodiments of the present invention have been described above, the present invention is not limited to these embodiments, and various modifications and changes are possible within the scope of the gist of the present invention. [Explanation of symbols]

[0106] 1. Infection risk estimation system 2. Infection risk estimation device 3. Mobile devices 4. Wearable devices 5. Network 101 Input Device 102 Display device 103 Network connection device 104 Memory circuit 105 Processing circuit

Claims

1. A means for obtaining user location information; means for acquiring biometric information and disease information of the user; An estimation means for estimating the disease infection risk of the user; a notification means for notifying the user of a risk of disease infection; The estimation means determines a second user who has been in contact with the first user who has the disease based on a history of location information of the first user, estimating an activity state of the first user during a period in which the first user was in contact with the second user from the biometric information of the first user; Estimating an infection risk of the second user based on the activity status and disease characteristics of the first user; The infection risk estimation device is characterized in that the notification means notifies the second user that there is a high possibility of the second user being infected with the disease if it is estimated that the second user is at high risk of being infected with the disease.

2. The infection risk estimation device of claim 1, wherein the biometric information includes at least one of the donor's heart rate, blood pressure, blood glucose level, respiratory rate, body temperature, brain waves, electrocardiogram, blood flow, heart sounds, blood oxygen concentration, cholesterol, blood alcohol concentration, sweating, electromyography, and voice.

3. The infection risk estimation system according to claim 1 , wherein the biological information is acquired from a wearable device.

4. 2. The infection risk estimation system according to claim 1, wherein the estimation means changes the estimation method depending on the characteristics of the disease.

5. A means for obtaining user location information; means for acquiring biometric information and disease information of the user; An estimation means for estimating the user's risk of infection with the disease (possibility of infection from an infectious disease patient); a notification means for notifying the user of a risk of disease infection; The estimation means determines a second user who has been in contact with the first user who has the disease based on a history of location information of the first user, estimating an activity state of the first user and an activity state of the second user during a period in which the first user was in contact with the second user from the biometric information of the first user; Estimating an infection risk of the second user based on the activity status and disease characteristics of the first user; An infection risk estimation system characterized in that the notification means notifies the second user that there is a high possibility of the second user being infected with the disease if it is estimated that the second user is at high risk of being infected with the disease.

6. An infection risk estimation system as described in claim 1 or 5, characterized in that the means for acquiring health information of the second user and the means for notifying the infection risk estimated by the estimation means make the notification according to the health information.

7. Depending on the second user's risk of infection, determining a third user who has been in contact with the second user based on the location information history of the second user; Estimating an infection risk for the third user using the estimation means; The infection risk estimation system according to claim 1 or 5, wherein the notification is made by the notification means.

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