Information processing system, information processing method, and program

Abstract

[Problem] To provide a novel technology. [Solution] One aspect of the present invention provides an information processing system. This information processing system comprises at least one processor, and the processor is configured to execute each of the following steps by reading a program. In a reception step, input information is received. The input information includes at least one of first input information related to the number of individuals infected with a specific infectious disease and second input information related to weather. In an output step, output information related to the risk of outbreak of the infectious disease is output on the basis of the input information and preset reference information.

Description

Information processing system, information processing method and program

[0001] The present invention relates to an information processing system, an information processing method, and a program.

[0002] For example, Patent Document 1 discloses an information providing device that more effectively determines the risk of infectious diseases.

[0003] This information providing device collects user information, calculates the risk level of each user for a predetermined disease based on the history information of each user indicated in the user information, identifies multiple users located in a predetermined area based on the location information indicated by the user information, and updates the risk level of each user based on the risk levels of the identified multiple users.The information providing device also calculates the risk level of each user using a machine learning model that has learned the relationship between the history information and the risk level of the predetermined disease.

[0004] JP 2023-159740 A

[0005] However, there is still room for improvement in the functions of the above-mentioned known techniques.

[0006] In view of the above circumstances, the present invention provides a novel technique.

[0007] According to one aspect of the present invention, there is provided an information processing system. The information processing system includes at least one processor configured to execute the following steps by reading a program: In the receiving step, input information is received. The input information includes at least one of first input information regarding the number of people infected with a specific infectious disease and second input information regarding weather. In the output step, output information regarding the risk of an infectious disease outbreak is output based on the input information and preset reference information.

[0008] According to the present disclosure, a novel technique can be provided.

[0009] FIG. 1 is a configuration diagram showing an information processing system 1 according to the present embodiment. FIG. 2 is a block diagram showing the hardware configuration of an information processing device 2. FIG. 3 is a block diagram showing the hardware configuration of a user terminal 3. FIG. 4 is a functional block diagram showing the functions of the information processing system 1 according to the present embodiment. FIG. 5 is a flowchart showing an overview of processing executed by the information processing system 1. FIG. 6 is an activity diagram showing a specific example of processing executed by the information processing system 1.

[0010] Hereinafter, embodiments of the present disclosure will be described with reference to the drawings. Various features shown in the following embodiments can be combined with each other.

[0011] Incidentally, a program for realizing the software appearing in one embodiment may be provided as a non-transitory computer-readable recording medium, or may be provided so as to be downloadable from an external server, or may be provided so that the program is started on an external computer and its functions are realized on a client terminal (so-called cloud computing).

[0012] Furthermore, various information processing according to an embodiment may realize input and output corresponding to the input. Here, the form of information referenced in such information processing (hereinafter referred to as reference information) is not limited as long as an output is obtained as a result of the input. The reference information may be, for example, rule-based information such as a database, a lookup table, or a predetermined function (including a decision formula such as a regression formula constructed using a statistical method), a trained model that has previously trained the correlation between input and output, or a large-scale language model that can output a desired result by inputting a prompt.

[0013] In one embodiment, the term "unit" may include, for example, a combination of hardware resources implemented by a circuit in the broad sense and software information processing that can be specifically realized by these hardware resources. In one embodiment, various information is handled, and this information is represented, for example, by physical values ​​of signal values ​​representing voltage or current, high or low signal values ​​as a binary bit set consisting of 0 or 1, or quantum superposition (so-called quantum bits), and communication and calculations can be performed on the circuit in the broad sense.

[0014] Furthermore, a circuit in a broad sense is a circuit realized by at least an appropriate combination of a circuit, circuitry, a processor, a memory, etc. That is, it includes an application specific integrated circuit (ASIC), a programmable logic device (e.g., a simple programmable logic device (SPLD), a complex programmable logic device (CPLD), and a field programmable gate array (FPGA)), etc.

[0015] 1. Hardware Configuration This section explains the hardware configuration.

[0016] 1.1 Information Processing System 1 FIG. 1 is a configuration diagram showing an information processing system 1 according to this embodiment. The information processing system 1 includes an information processing device 2 and a user terminal 3, which are connected via a general-purpose or dedicated communication network 11. Here, the system exemplified as the information processing system 1 is made up of one or more devices or components. Therefore, even the information processing device 2 alone or the user terminal 3 alone is included in the system exemplified as the information processing system 1. Each component included in the information processing system 1 will be further described below.

[0017] 2 is a block diagram showing the hardware configuration of the information processing device 2. The information processing device 2 has a communication unit 21, a storage unit 22, and a control unit 23, and these components are electrically connected via a communication bus 20 inside the information processing device 2. Each component will be further described.

[0018] The communication unit 21 is preferably a wired communication means such as USB, IEEE 1394, Thunderbolt (registered trademark), or wired LAN network communication, but may also include wireless LAN network communication, mobile communication such as 3G / LTE / 5G, Bluetooth (registered trademark), or the like, as needed. That is, it is more preferable to implement it as a collection of multiple communication means. That is, the information processing device 2 may communicate various information from the outside via the communication unit 21 and the communication network 11.

[0019] The memory unit 22 stores various pieces of information defined above. This can be implemented, for example, as a storage device such as a solid state drive (SSD) that stores various programs and the like related to the information processing device 2 executed by the control unit 23, or as a memory such as a random access memory (RAM) that stores temporarily required information (arguments, arrays, etc.) related to program calculations. The memory unit 22 stores various programs, variables, etc. related to the information processing device 2 executed by the control unit 23.

[0020] The control unit 23 processes and controls the overall operation of the information processing device 2. The control unit 23 is, for example, a central processing unit (CPU) (not shown). The control unit 23 realizes various functions related to the information processing device 2 by reading predetermined programs stored in the storage unit 22. That is, information processing by software stored in the storage unit 22 is specifically realized by the control unit 23, which is an example of hardware, and can be executed as each functional unit included in the control unit 23. This will be described in more detail in the next section. Note that the control unit 23 is not limited to being a single unit, and multiple control units 23 may be provided for each function. A combination of these may also be used. That is, the information processing system 1 includes the control unit 23 as at least one processor. The control unit 23, which is a processor, is configured to execute each step described below by reading programs.

[0021] 1.3 User Terminal 3 FIG. 3 is a block diagram showing the hardware configuration of the user terminal 3. The user terminal 3 is a terminal operated by a user. In this case, the user is someone who needs to predict the risk of an outbreak of a specific infectious disease, such as a government agency, medical professional, pharmaceutical company, or individual user. Pathogens that cause specific infectious diseases include viruses, bacteria, fungi, and parasites that become prevalent depending on the season, weather conditions, etc., such as respiratory viruses (RS virus, influenza virus, coronavirus, etc.), Staphylococcus aureus, mycoplasma, streptococcus, malaria parasites, and new pathogens. The user terminal 3 may take any form, such as a smartphone, tablet, computer, or other device that can access the information processing device 2 via a telecommunications line. Specifically, the user terminal 3 includes a communication unit 31, a memory unit 32, a control unit 33, a display unit 34, and an input unit 35. These components are electrically connected within the user terminal 3 via a communication bus 30. The description of the communication unit 31, the storage unit 32, and the control unit 33 is omitted because they are the same as the description of each unit in the information processing device 2.

[0022] The display unit 34 may be, for example, included in the housing of the user terminal 3 or may be externally attached. The display unit 34 displays a graphical user interface (GUI) screen that can be operated by the user. This is preferably implemented by selectively using display devices such as a CRT display, a liquid crystal display, an organic EL display, or a plasma display depending on the type of the user terminal 3.

[0023] The input unit 35 may be included in the housing of the user terminal 3 or may be externally attached. For example, the input unit 35 may be implemented as a touch panel integrated with the display unit 34. A touch panel allows the user to input tapping, swiping, and the like. Of course, a switch button, a mouse, a QWERTY keyboard, or the like may be used instead of a touch panel. That is, the input unit 35 accepts an operation input made by the user. The input is transferred as a command signal to the control unit 33 via the communication bus 30, and the control unit 33 can execute predetermined control or calculation as necessary.

[0024] 2. Functional Configuration This section describes the functional configuration of this embodiment. As described above, information processing by software stored in the storage unit 22 is specifically realized by the control unit 23, which is an example of hardware, and each functional unit included in the control unit 23 can be executed.

[0025] 4 is a functional block diagram showing functions of the information processing system 1 according to this embodiment. Specifically, the information processing device 2, which is an example of the information processing system 1, includes a receiving unit 231, a calculation unit 232, an output unit 233, a display control unit 234, an acquisition unit 235, and a notification unit 236.

[0026] The reception unit 231 is configured to receive various pieces of information as a reception step. For example, the reception unit 231 receives data, information input by a user (for example, input information IFi), etc. from the storage unit 22, or from the input unit 35 of the user terminal 3 or other external devices via the communication network 11. In this embodiment, the various pieces of information received by the reception unit 231 will be described as being stored in the storage unit 22.

[0027] The calculation unit 232 is configured to execute various information processing calculations related to the information processing device 2. Details will be described later.

[0028] The output unit 233 is configured to output various information as an output step. For example, the output unit 233 outputs output information IFo. Details will be described later.

[0029] The display control unit 234 is configured to execute various display processes. For example, the display control unit 234 controls the display unit 34 of the user terminal 3 to display visually recognizable information such as screens, images including still images or videos, icons, messages, etc. The display control unit 234 may generate only rendering information for displaying visually recognizable information on the display unit 34 of the user terminal 3. Details will be described later.

[0030] The acquisition unit 235 is configured to acquire various pieces of information. In one embodiment, the various pieces of information acquired by the acquisition unit 235 will be described as being stored in the storage unit 22. Details will be described later.

[0031] The notification unit 236 is configured to notify the user of various information, the details of which will be described later.

[0032] 3. Operation of Information Processing System 1 In this section, with reference to the drawings, we will explain the information processing method of the information processing system 1. Note that the order of processes can be changed as appropriate, multiple processes can be executed simultaneously, or some processes can be omitted.

[0033] 5 is a flowchart showing an outline of the processing executed by the information processing system 1. In this processing, the reception unit 231 first receives input information IFi (step S001). The input information IFi includes at least one of first input information IFi1 regarding the number of people infected with a specific infectious disease and second input information IFi2 regarding weather. Then, the output unit 233 outputs output information IFo regarding the risk of an infectious disease outbreak based on the input information IFi and preset reference information RI (step S002).

[0034] To summarize the above, the information processing system 1 according to one embodiment includes at least one processor (e.g., the control unit 23). The processor is configured to execute the following steps by reading a program. The reception unit 231 receives input information IFi as a reception step. The input information IFi includes at least one of first input information IFi1 regarding the number of people infected with a specific infectious disease and second input information IFi2 regarding weather. The output unit 233 outputs output information IFo regarding the risk of an infectious disease outbreak based on the input information IFi and preset reference information RI as an output step. According to this aspect, the risk of an infectious disease outbreak can be predicted through information processing using a processor.

[0035] 3.2 Specific Example Next, a specific example of processing according to an embodiment will be described. The specific example may fall within the scope defined in the overview above. FIG. 6 is an activity diagram illustrating a specific example of processing executed by the information processing system 1. The following outlines the flow of information processing when predicting the risk of an outbreak of a specific infectious disease using this activity diagram. As an example, the following describes a case in which a pharmaceutical company and a supplier of raw materials used in pharmaceutical production to the pharmaceutical company (collectively referred to as "User A") predict (hereinafter also referred to as "this prediction") the risk of an outbreak of a respiratory infection caused by respiratory syncytial virus (RSV) infection (an example of a specific infectious disease, hereinafter simply referred to as "RSV infection"). User A needs to make this prediction in order to timely manufacture and supply diagnostic agents, test kits, etc. for diagnosing and testing RSV infection (hereinafter referred to as "diagnostics, etc.") and therapeutic agents, vaccines, etc. effective against RSV infection (hereinafter referred to as "therapeutics, etc.", including diagnostic agents, etc.).

[0036] First, using a browser on the user terminal 3, user A accesses a service (hereinafter simply referred to as the "service") provided by the information processing system 1, which predicts the risk of an infection outbreak of a specific infectious disease. The service provided by the information processing system 1 may be provided by a dedicated mobile application. In this case, user A launches the application. In response, the display control unit 234 displays a login screen (not shown) on the display unit 34 of the user terminal 3. Then, the reception unit 231 receives input of user A's login information (e.g., a click, tap, swipe, or selection operation) through user A's operation via the login screen. When the login is complete, the display control unit 234 controls the display unit 34 of the user terminal 3 to display its top screen (not shown) (activity A001).

[0037] Next, the reception unit 231 receives an input to select an infectious disease through an operation by user A via the top screen (activity A002). This infectious disease is the target for predicting the risk of an infection outbreak. Through this input, user A selects respiratory syncytial virus infection as the specific infectious disease. In other words, the top screen is configured to allow the risk of an infection outbreak for various infectious diseases to be predicted by depicting infectious diseases other than respiratory syncytial virus infection as selectable. Note that the top screen may also be configured to predict the risk of an infection outbreak only for respiratory syncytial virus infection.

[0038] In response to receiving an input selecting RS virus infection, the acquisition unit 235 acquires prediction settings (activity A003). The prediction settings include settings related to the input information IFi and the output information IFo. Furthermore, the prediction settings may be appropriately set when user A uses the service, or predetermined settings may be used. Specifically, for example, the settings related to the input information IFi include settings specifying the storage location, URL, etc., necessary for acquiring the input information IFi from the memory unit 22 of the information processing device 2, the memory unit 32 of the user terminal 3, or another external device, and settings for the frequency of automatically acquiring the input information IFi. Furthermore, the settings related to the output information IFo include settings for the output format, the prediction period PP, the prediction area, etc. Details of the input information IFi and the output information IFo will be described later.

[0039] In a receiving step, the receiving unit 231 receives input information IFi based on the settings related to prediction (activity A004). Specifically, for example, if the settings related to the input information IFi specify a storage location in the storage unit 32 of the user terminal 3, the receiving unit 231 receives the acquired input information IFi in response to the acquisition unit 235 acquiring the input information IFi from the user terminal 3. As another example, if the settings related to the input information IFi specify a URL for acquiring the input information IFi from another external device, the receiving unit 231 may receive the acquired input information IFi in response to the acquisition unit 235 acquiring the input information IFi from the other external device. Note that the processing of activity A004 is not limited to being performed based on an operation by user A, and may also be performed based on a setting for the frequency of automatic acquisition in the settings related to prediction, regardless of an operation by user A.

[0040] Here, the input information IFi is information used to predict the risk of an infectious outbreak of RS virus infection, and includes, for example, at least one of the first input information IFi1 and the second input information IFi2. That is, the receiving unit 231 may receive only the first input information IFi1, only the second input information IFi2, or both the first input information IFi1 and the second input information IFi2 as the input information IFi. Furthermore, the input information IFi may be information divided by area, such as information compiled by country, prefecture, city, town, or village. The following description will be given taking as an example a case where the first input information IFi1 and the second input information IFi2 compiled by each of Japan's 47 prefectures are received.

[0041] The first input information IFi1 is information regarding the number of persons infected with respiratory syncytial virus infection (an example of a specific infectious disease), and is, for example, information regarding the number of persons infected with respiratory syncytial virus infection during an arbitrary period PD obtained from an administrative agency, a medical institution, etc. Note that the number of persons infected with respiratory syncytial virus infection during the period PD may be, for example, the number of persons newly infected during the period PD, or the cumulative number of persons infected based on the number of persons newly infected during the period PD and the number of persons previously infected, or the rate of increase or decrease in the number of persons infected with respiratory syncytial virus infection during the period PD, calculated based on the number of persons infected at the beginning and end of the period PD.

[0042] The period PD is at least one period. Specifically, for example, if the period PD includes two periods, a first period PD1 and a second period PD2, one week from last week is selected as the first period PD1, and one week from the week before last is selected as the second period PD2. In this case, the second period PD2 is a period different from the first period PD1 and is a period that is temporally continuous with the first period PD1. The period selected as the period PD is, for example, 21 days, preferably 14 days, and more preferably 7 days. Specifically, for example, the period selected as the period PD may be 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, or 60 days, or may be within a range between any two of the numbers exemplified here.

[0043] Furthermore, the multiple periods selected as the period PD are, for example, four periods, preferably three periods, and more preferably two periods. Specifically, for example, the period PD, which is at least one period, is a combination of 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 periods, and may be within a range between any two of the numerical values ​​exemplified here. In more detail, for example, if three periods are selected as the period PD, one week last week, one week two weeks ago, and one week three weeks ago are selected.

[0044] Furthermore, if the week of last week is selected as the first period PD1 and the week before last is selected as the second period PD2, the first input information IFi1 will be information regarding the number of people infected with RS virus infection in the week of last week and the week before last. That is, the first input information IFi1 includes information regarding the number of people infected with RS virus infection in the first period PD1 and information regarding the number of people infected with RS virus infection in the second period PD2. According to this aspect, by using information regarding the number of people infected with RS virus infection in multiple arbitrary periods PD as input information IFi, it is possible to predict with higher accuracy the risk of an RS virus infection outbreak.

[0045] Furthermore, the first input information IFi1 may be a rate of increase / decrease in the number of individuals infected with respiratory syncytial virus infection in the first period PD1 and the second period PD2, calculated based on the number of individuals infected at the beginning and end of the first period PD1 and the second period PD2. That is, the first input information IFi1 may be a rate of increase / decrease in the number of individuals infected with respiratory syncytial virus infection based on the number of individuals infected with respiratory syncytial virus infection in the first period PD1 and the number of individuals infected with respiratory syncytial virus infection in the second period PD2. According to this aspect, by using the rate of increase / decrease in the number of individuals infected with respiratory syncytial virus infection as the input information IFi, the risk of an outbreak of respiratory syncytial virus infection can be predicted with higher accuracy.

[0046] Furthermore, the first input information IFi1 may be a parameter PM corresponding to the rate of increase or decrease in the number of infected people with RS virus infection. The parameter PM is flagged on when the rate of increase or decrease satisfies a predetermined condition, and is flagged off when the rate of increase or decrease does not satisfy the predetermined condition. In other words, the first input information IFi1 may be a parameter PM that is flagged on when the rate of increase or decrease satisfies the predetermined condition, and is flagged off when the rate of increase or decrease does not satisfy the predetermined condition.

[0047] Here, the predetermined condition is that the rate of increase or decrease is equal to or greater than a predetermined rate, or that the rate of increase or decrease is equal to or greater than a predetermined number of times in succession, etc.

[0048] The predetermined ratio is, for example, 2 to 50%, preferably 3 to 30%, and more preferably 5 to 10%. Specifically, the predetermined ratio is, for example, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, or 50%, and may be within a range between any two of the numerical values ​​exemplified here.

[0049] The predetermined number of times is, for example, 1 to 20 times, preferably 2 to 10 times, and more preferably 3 to 5 times. Specifically, the predetermined number of times is, for example, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20 times, and may be within a range between any two of the numerical values ​​exemplified here.

[0050] Specifically, for example, if the predetermined percentage is set to 5% and the predetermined number of times is set to 2, the parameter PM flag is turned on if the increase / decrease rate is 5% or greater two or more consecutive times. For example, if the increase / decrease rate in the number of RS virus infections during the last week (an example of a first period PD1) for which this prediction is made is 8% and the increase / decrease rate in the number of RS virus infections during the week two weeks before (an example of a second period PD2) for which this prediction is made is 6%, the predetermined condition of an increase / decrease rate of 5% or greater two or more consecutive times is met, and the parameter PM flag is turned on. Thereafter, the parameter PM flag is turned off when the increase / decrease rate is less than 5%. According to this aspect, by using the parameter PM, which is indicated by an on / off flag, as the input information IFi, it is possible to level out the differences in the degree of increase / decrease in the number of infected people caused by differences in population between areas, thereby enabling more accurate prediction of the risk of an RS virus infection outbreak. In the following, an example will be described in which the parameters PM for the last week and the week before last, calculated by each of the 47 prefectures, are received as the first input information IFi1.

[0051] The second input information IFi2 is information related to weather, such as information about weather, information that changes depending on weather conditions, etc. In other words, the second input information IFi2 may include at least one of information about weather and information that changes depending on weather conditions.

[0052] Here, the weather information is information such as temperature, air pressure, sunshine hours, precipitation, humidity, wind speed, etc. obtained from government agencies, weather information companies, etc. Furthermore, the weather information may include average, maximum, and minimum values ​​for temperature, air pressure, sunshine hours, precipitation, humidity, wind speed, etc. over a predetermined period (e.g., day, week, month, etc.). Specifically, for example, the weather information is the average, maximum, and minimum values ​​of temperature over the past week.

[0053] The weather-related information may include not only past actual values ​​but also future predicted values. That is, the second input information IFi2 may include past actual values ​​and future predicted values. Specifically, for example, the weather-related information may include the average, maximum, and minimum temperature values ​​for the past week as well as the average, maximum, and minimum temperature values ​​for the next week. According to this aspect, by using the second input information IFi2 consisting of past actual values ​​and future predicted values ​​as the input information IFi, it is possible to more accurately predict the risk of an RS virus infection outbreak.

[0054] Furthermore, the information that changes depending on weather conditions is information that changes depending on temperature, precipitation, sunshine hours, wind speed, etc., obtained from government agencies, electric power companies, etc., such as power consumption for heating and cooling, river water levels, and power generation by solar power or wind power. Specifically, for example, the information that changes depending on weather conditions is power consumption for heating and cooling over the past week. According to this aspect, by using second input information IFi2 that is composed of at least one piece of information about weather and information that changes depending on weather conditions as input information IFi, it is possible to predict with higher accuracy the risk of an RS virus infection outbreak.

[0055] In the following, an example will be described in which the second input information IFi2 is received as information on average, maximum, and minimum values ​​of temperature, atmospheric pressure, sunshine hours, and precipitation for the past week and the week before last for which the prediction is made. In this case, in the input information IFi, the period for acquiring the information in the first input information IFi1 is the same as the period for acquiring the information in the second input information IFi2. That is, the input information IFi is information in which the period for acquiring the information in the first input information IFi1 corresponds to the period for acquiring the information in the second input information IFi2. According to this aspect, by using multiple pieces of information in which the periods for acquiring information correspond as input information IFi, it is possible to more accurately predict the risk of an outbreak of RS virus infection.

[0056] Next, the calculation unit 232 calculates a prediction of the risk of an RS virus infection outbreak based on the input information IFi and the preset reference information RI (activity A005). Specifically, for example, if this prediction is performed for the third week of April, the input information IFi uses the first input information IFi1 and the second input information IFi2 for the second week of April (the week before last) and the first week of April (the week before last). That is, the first input information IFi1 is the parameter PM for the first and second weeks of April, aggregated by each of Japan's 47 prefectures, and the second input information IFi2 is information on the average, maximum, and minimum values ​​of temperature, atmospheric pressure, sunshine hours, and precipitation for the first and second weeks of April. The calculation unit 232 then calculates a prediction of the risk of an RS virus infection outbreak based on the input information IFi and the reference information RI.

[0057] In this case, the reference information RI is information that results in the output information IFo being obtained as a result of inputting the input information IFi. For example, the reference information RI may be rule-based information such as a database, a lookup table, or a predetermined function (including a determination formula such as a regression formula constructed using statistical techniques). However, it is preferably a trained model LM that has been trained to learn correlations with risk using at least one of the first input information IFi1 and the second input information IFi2 as training data. According to this aspect, the trained model LM trained using any input information IFi as training data can be used to more accurately predict the risk of an RS virus infection outbreak. Details of the trained model LM will be described later.

[0058] Then, the output unit 233 outputs output information IFo regarding the risk of respiratory syncytial virus infection spreading explosively (activity A006). That is, as an output step, the output unit 233 outputs output information IFo regarding the risk of respiratory syncytial virus infection spreading explosively based on the input information IFi and the preset reference information RI. Furthermore, the display control unit 234 displays the output output information IFo on the display unit 34 of the user terminal 3 based on an operation by user A. Note that the display control unit 234 may display the output information IFo on the display unit 34 of the user terminal 3 in response to the output of the output information IFo.

[0059] In this case, the output information IFo is output based on the settings for the output information IFo, which are settings related to prediction. The settings for the output information IFo include settings for the output format, the prediction time PP, the prediction area, etc. The output format, the prediction time PP, and the prediction area will be described below.

[0060] The output format is a format that shows the results of this prediction, such as an index Id indicating the risk of RS virus infection causing an infection explosion, whether or not there is such a risk (hereinafter also referred to as "whether or not there is a risk"), a level indicating such a risk (hereinafter also referred to as "level indicating the risk"), etc. In other words, the output information IFo includes information indicating the risk of RS virus infection causing an infection explosion as an index Id and information indicating whether or not there is a risk of RS virus infection causing an infection explosion.

[0061] The index Id is an index that indicates the risk of RS virus infection spreading explosively as a percentage, and is, for example, an index that is expressed as a percentage of 0 to 100% or 0 to 1. Specifically, for example, the index Id indicates the lowest risk as 0 and the highest risk as 1. In other words, user A can understand the degree of risk of RS virus infection spreading explosively based on the index Id that is expressed in the range of 0 to 1. According to this aspect, user A can understand the risk of RS virus infection spreading explosively based on the index Id that indicates the risk of RS virus infection spreading explosively.

[0062] The presence or absence of risk is information indicating the risk of an RS virus infection outbreak as "yes" or "no." Specifically, for example, the presence or absence of risk may be indicated as "yes" or "no" based on the input information IFi and the reference information RI, or may be indicated as "yes" or "no" based on the value of the calculation result (e.g., the index Id) and a predetermined condition. In this case, the predetermined condition may be satisfying a given threshold or more, satisfying a given threshold or more times, etc.

[0063] In the case of an index Id expressed as a ratio of 0 to 1, the predetermined threshold is, for example, 0.1 to 0.6, preferably 0.25 to 0.5, and more preferably 0.3 to 0.4. Specifically, for example, the predetermined threshold is 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1, 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 2, 2.1, 2.2, 2.3, 2.4, 2.5, 2.6, 2.7, 2.8, 2.9, 3, 3.1, 3.2, 3.3, 3.4, 3.5, 3.6, 3.7, 3.8, 3.9, 4, 4.1, 4.2, 4.3, 4.4, 4.5, 4.6, 4.7, 4.8, 4.9, 5, 5.1, 5.2, 5.3, 5.4, 5.5, 5.6, 5.7, 5.8, 5.9, 6, and may be within a range between any two of the numerical values ​​exemplified here.

[0064] The predetermined number of times is, for example, 1 to 20 times, preferably 2 to 10 times, and more preferably 3 to 5 times. Specifically, the predetermined number of times is, for example, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20 times, and may be within a range between any two of the numerical values ​​exemplified here.

[0065] In more detail, for example, when the index Id is expressed as a value between 0 and 1, and the arbitrary threshold is 0.3, the output information IFo indicates a risk of "no risk" when the index Id is less than 0.3, and a risk of "yes" when the index Id is 0.3 or greater. As another example, when the index Id is expressed as a value between 0 and 1, the arbitrary threshold is 0.2, and the predetermined number of times is 3, the output information IFo indicates a risk of "no risk" when the index Id is less than 0.2 or when the number of times an index Id of 0.2 or greater is 1 or 2, and a risk of "yes" when the number of times an index Id of 0.2 or greater is 3 or greater. Furthermore, the predetermined condition may be a combination of satisfying a threshold or greater and satisfying a threshold or greater a predetermined number of times. According to this aspect, by indicating the risk of respiratory syncytial virus infection spreading by the presence or absence of the risk, user A can grasp the risk of respiratory syncytial virus infection spreading.

[0066] The risk levels are information indicating the level of risk of RS virus infection spreading, and are indicated, for example, by levels of "high, medium, low," symbols of "A, B, C, D," or numerical values ​​such as "Level 1, Level 2, Level 3, Level 4, Level 5." According to this aspect, the risk can be grasped by the level that indicates the risk of RS virus infection spreading.

[0067] The prediction time PP is set by specifying an arbitrary prediction time PP as the time to be displayed as the result of this prediction. Specifically, for example, the prediction time PP is set to one week in the future as the time to be displayed as the result of this prediction. The arbitrary prediction time PP is, for example, one week in the future, preferably three weeks in the future, and more preferably one month in the future. More specifically, for example, the arbitrary prediction time PP may be 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, or 30 weeks in the future, or may be within a range between any two of the values ​​exemplified here. This aspect makes it possible to grasp the risk of an outbreak of RS virus infection at a predetermined prediction time PP.

[0068] Furthermore, the prediction time PP may be set to specify multiple arbitrary prediction times PP as the time shown as the result of this prediction. That is, the arbitrary prediction time PP may include at least a first prediction time PP1 and a second prediction time PP2 different from the first prediction time PP1. Specifically, for example, the prediction time PP may be set to specify the first prediction time PP1 as one month in the future and the second prediction time PP2 as two months in the future as the time shown as the result of this prediction. In this case, the output information IFo includes first output information IFo1 and second output information IFo2, including the first output information IFo1 corresponding to the first prediction time PP1 and the second output information IFo2 corresponding to the second prediction time PP2. The first prediction time PP1 and the second prediction time PP2 are, for example, one week and two weeks in the future, preferably two weeks and four weeks in the future, and more preferably one month and two months in the future. Specifically, for example, the first prediction time PP1 and the second prediction time PP2 may be 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, or 30 weeks in the future, or may be within a range between any two of the values ​​exemplified here. The prediction time PP may be specified by combining various periods for the first prediction time PP1 and the second prediction time PP2, or may even be a combination of three or more prediction times. Specifically, for example, the prediction time PP may be set to specify two weeks, one month, and three months in the future as the time periods shown as the prediction results. According to this aspect, the risk of an outbreak of RS virus infection at multiple prediction times PP can be grasped.

[0069] The prediction area is an area in which the results of this prediction are shown. For example, the area may be a country, prefecture, city, ward, town, or village. Specifically, for example, if Tokyo and Osaka are set in the prediction area setting, the output information IFo outputs the results of this prediction for Tokyo and the results of this prediction for Osaka. Furthermore, the prediction area may be set to correspond to the area in the input information IFi, or an area related to the area in the input information IFi may be set. Specifically, for example, if the area in the input information IFi is information aggregated by the 47 prefectures of Japan, the prediction area may be set to perform this prediction for each of the 47 prefectures, or may be set to perform this prediction for a specific prefecture among the 47 prefectures. Furthermore, specifically, for example, the prediction area may be set to a neighboring or adjacent area (such as a country, region, prefecture, city, ward, town, or village) or an area with opposing seasons, weather conditions, etc., as an area related to the area in the input information IFi.

[0070] In the following, an example will be described in which the first output information IFo1 indicates whether there is a risk of an RS virus infection outbreak one month from the present prediction, and the second output information IFo2 indicates whether there is a risk of an RS virus infection outbreak two months from the present prediction. As an example of the output information IFo in this case, Table 1 shows the results of the present prediction in Tokyo and Osaka when the present prediction was made in the third week of April.

[0071] Furthermore, the processing of activities A001 to A006 may be repeatedly executed to continuously perform this prediction. For example, the processing of activities A001 to A006 may be executed for each period PD during which input information IFi is aggregated. This allows user A to continuously obtain output information IFo in a timely manner. Specifically, for example, user A performs this prediction in the third week of April, and then performs this prediction again in the fourth week of April. That is, the output unit 233 outputs output information IFo corresponding to the period PD during which input information IFi is aggregated. As an example of output information IFo in this case, Table 2 shows the results of this prediction in Tokyo and Osaka when this prediction is performed in the fourth week of April.

[0072] Furthermore, User A may manufacture and supply therapeutic drugs, etc. effective against RS virus infection based on the contents of output information IFo. Specifically, for example, User A may start manufacturing therapeutic drugs, etc. based on the contents of Table 1 indicating a risk of "yes" in Osaka in the third week of June. User A may increase the amount of therapeutic drugs, etc. manufactured based on the contents of Table 2 indicating a risk of "yes" in Tokyo in the fourth week of June, and may start supplying therapeutic drugs, etc. to medical institutions, etc. in Osaka based on the risk of "yes" in Osaka in the fourth week of May. According to this aspect, User A can manufacture and supply therapeutic drugs, etc. effective against RS virus infection in a timely manner.

[0073] The above is the flow of processing according to the specific example.

[0074] 4. Related technical matters

[0075] (Trained Model LM) The trained model LM is a model that uses at least one of the first input information IFi1 and the second input information IFi2 as training data to learn the correlation with the risk of an RS virus infection outbreak. The first input information IFi1 and the second input information IFi2 used as training data may be long-term information, for example, information covering a period of 3 to 10 years, preferably 5 to 15 years, and more preferably 10 to 30 years. Specifically, the training data may be, for example, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, or 30 years, or may be within a range between any two of the values ​​exemplified here. In particular, for example, information obtained by aggregating weekly data over the past 10 years may be used as training data. In this case, the first input information IFi1 is information that summarizes data on the number of infected people with RS virus infection per week for the past 10 years, and the second input information IFi2 is information that summarizes data on the average, maximum, and minimum values ​​of temperature, atmospheric pressure, sunshine hours, and precipitation over the past 10 years.

[0076] Furthermore, the first input information IFi1 may be a parameter PM indicated by an on / off flag, and used as training data. Specifically, for example, this information is information obtained by aggregating data on the parameter PM for each of the 47 prefectures for each week over the past 10 years. This aspect makes it possible to level out the degree of increase or decrease in the number of infected people that occurs due to differences in population between areas, and the leveled information can be used as training data.

[0077] [Others] The information processing system 1 according to the above-described embodiment may also be modified as follows.

[0078] The above-described embodiment may be implemented as a distributable program. That is, this program causes at least one computer to execute the steps of the information processing system 1. According to this aspect, the risk of an RS virus infection outbreak can be predicted through information processing by the program. Also, an information processing method including the steps of the information processing system 1 may be implemented. That is, this information processing method includes the steps of the information processing system 1. According to this aspect, the risk of an RS virus infection outbreak can be predicted through information processing.

[0079] In the above embodiment, the output unit 233 outputs the output information IFo based on an operation by user A. However, this is not limiting. For example, information may be notified by the notification unit 236. Specifically, for example, the acquisition unit 235 automatically acquires input information IFi from an arbitrary storage location at an arbitrary frequency based on a setting related to prediction. Next, the acceptance unit 231 accepts the acquired input information IFi. Subsequently, the calculation unit 232 calculates output information IFo in response to the acquisition of the input information IFi. Then, the notification unit 236 notifies the user terminal 3 that, as a result of the output information IFo being calculated, newly calculated output information IFo is available and can be displayed. Furthermore, the display control unit 234 displays the calculated output information IFo on the display unit 34 of the user terminal 3 based on an operation by user A. According to this aspect, user A can obtain output information IFo according to input information IFi, which is updated at a predetermined frequency, through information processing using a processor, thereby enabling timely prediction of the risk of an RS virus infection outbreak. Furthermore, the output information IFo may be configured to be linked to a production line, for example, related to the production of the aforementioned therapeutic drug. In such a case, at least one processor included in the production line may be configured to control the operation of the production line based on the output information IFo.

[0080] In the above embodiment, the output information IFo indicates whether or not there is a risk as information regarding the risk of respiratory syncytial virus infection spreading. However, this is not limited to this. For example, the output information IFo may include characteristics of respiratory syncytial virus infection, such as the infection rate, the degree of infection rate (e.g., "high," "medium," or "low"), and the infection route (e.g., "droplet infection," "airborne infection," etc.). In other words, the output information IFo includes information indicating the characteristics of respiratory syncytial virus infection. This aspect makes it possible to grasp the characteristics of respiratory syncytial virus infection and predict the risk of an infection outbreak of respiratory syncytial virus infection in more detail.

[0081] In the above embodiment, the output information IFo indicates whether or not there is a risk as information regarding the risk of an RS virus infection outbreak. However, this is not limited to this. For example, the output information IFo may include information regarding the progression of an RS virus infection outbreak. Specifically, the information regarding the progression of an infection outbreak may include, for example, the time when the number of infected people will peak, the time when the number of infected people will begin to converge after peaking, and the time when the infection outbreak will end. This aspect makes it possible to grasp information regarding the progression of an RS virus infection outbreak, thereby making it possible to more precisely predict the risk of an RS virus infection outbreak.

[0082] In the above embodiment, the information processing system 1 includes at least one device (e.g., the information processing device 2, etc.), but at least one of these devices may be installed outside Japan. For example, the information processing device 2 may be installed outside Japan, and the user terminal 3 may be installed in Japan. Similarly, user A may access various information processing devices 2 from outside Japan using his or her user terminal 3.

[0083] In the above embodiment, the reception unit 231, the calculation unit 232, the output unit 233, the display control unit 234, the acquisition unit 235, and the notification unit 236 are described as functional units realized by the control unit 23 of the information processing device 2, but they may also be implemented as functional units realized by the control unit 33 of the user terminal 3. Furthermore, the various pieces of information described in the above example may be stored not only in the storage unit 22 of the information processing device 2, but also in a distributed manner in other external devices.

[0084] Furthermore, it may be provided in the following aspects.

[0085] (1) An information processing system comprising at least one processor, the processor being configured to execute each of the following steps by reading a program, wherein in a reception step, input information is received, the input information including at least one of first input information regarding the number of people infected with a specific infectious disease and second input information regarding weather, and in an output step, output information regarding the risk of the infectious disease causing an infection explosion based on the input information and preset reference information.

[0086] According to this aspect, the risk of a specific infectious disease spreading can be predicted through information processing using a processor.

[0087] (2) In the information processing system described in (1) above, the output information includes information indicating, as an indicator, the risk of the infectious disease causing an infection explosion.

[0088] According to this embodiment, the risk of a specific infectious disease causing an infection explosion can be displayed as an index.

[0089] (3) In the information processing system described in (1) or (2) above, the output information includes information indicating whether or not there is a risk of the infectious disease causing an infection explosion.

[0090] According to this embodiment, it is possible to indicate whether or not there is a risk of a specific infectious disease spreading.

[0091] (4) In the information processing system described in any one of (1) to (3) above, the first input information includes information regarding the number of infected persons contracting the infectious disease in a first period and information regarding the number of infected persons contracting the infectious disease in a second period, wherein the second period is a period different from the first period.

[0092] According to this aspect, by using information regarding the number of people infected with an infectious disease over multiple arbitrary periods as input information, it is possible to predict with greater accuracy the risk of an infection outbreak of a specific infectious disease.

[0093] (5) In the information processing system described in (4) above, the second period is a period that is temporally continuous with the first period, and the first input information is the rate of increase / decrease in the number of infected persons contracting the infectious disease based on the number of infected persons contracting the infectious disease in the first period and the number of infected persons contracting the infectious disease in the second period.

[0094] According to this aspect, by using the rate of increase or decrease in the number of people infected with an infectious disease as input information, it is possible to predict with higher accuracy the risk of an infection explosion of a specific infectious disease.

[0095] (6) In the information processing system described in (5) above, the first input information is a parameter that turns on a flag when the rate of increase or decrease satisfies a predetermined condition, and turns off the flag when the rate of increase or decrease does not satisfy the predetermined condition.

[0096] According to this aspect, by using parameters as input information that turn on a flag when the rate of increase or decrease meets certain conditions, it becomes possible to eliminate the influence of differences in population that arise depending on the area, and to predict with greater accuracy the risk of an infection explosion of a specific infectious disease.

[0097] (7) In the information processing system described in any one of (1) to (6) above, the second input information includes at least one of information about weather and information that changes depending on weather conditions.

[0098] According to this aspect, by using as input information the second input information consisting of at least one of information about the weather and information that changes depending on the weather conditions, it is possible to predict with greater accuracy the risk of an infection explosion of a specific infectious disease.

[0099] (8) In the information processing system described in any one of (1) to (7) above, the second input information includes past actual values ​​and future predicted values.

[0100] According to this aspect, by using second input information consisting of past actual values ​​and future predicted values ​​as input information, it is possible to predict with greater accuracy the risk of an infection explosion of a specific infectious disease.

[0101] (9) In the information processing system described in any one of (1) to (7) above, the input information is information in which the period for acquiring information in the first input information corresponds to the period for acquiring information in the second input information.

[0102] According to this aspect, by using multiple pieces of information corresponding to the time periods for which the information is acquired as input information, it is possible to predict with higher accuracy the risk of an infection outbreak of a specific infectious disease.

[0103] (10) In the information processing system described in any one of (1) to (9) above, the reference information is a trained model that uses at least one of the first input information and the second input information as training data to learn the correlation with the risk.

[0104] According to this aspect, it is possible to predict with higher accuracy the risk of an infection outbreak of a specific infectious disease using a trained model that has been trained using arbitrary input information as training data.

[0105] (11) An information processing method comprising the steps of the information processing system according to any one of (1) to (10) above.

[0106] According to this aspect, it is possible to predict the risk of a specific infectious disease causing an infection explosion through information processing.

[0107] (12) A program that causes at least one computer to execute each step of the information processing system described in any one of (1) to (10) above.

[0108] According to this aspect, it is possible to predict the risk of an outbreak of a specific infectious disease through information processing by a program. Of course, this is not limited to this.

[0109] Finally, while various embodiments of the present disclosure have been described, they are presented as examples and are not intended to limit the scope of the invention. The novel embodiments may be embodied in various other forms, and various omissions, substitutions, and modifications may be made without departing from the spirit of the invention. Such embodiments and modifications are intended to be included within the scope and spirit of the invention, as well as within the scope of the invention and its equivalents as defined in the claims.

[0110] 1: Information processing system 11: Communication network 2: Information processing device 20: Communication bus 21: Communication unit 22: Memory unit 23: Control unit 231: Reception unit 232: Calculation unit 233: Output unit 234: Display control unit 235: Acquisition unit 236: Notification unit 3: User terminal 30: Communication bus 31: Communication unit 32: Memory unit 33: Control unit 34: Display unit 35: Input unit IFi: Input information IFi1: First input information IFi2: Second input information IFo: Output information IFo1: First output information IFo2: Second output information Id: Index LM: Trained model PD: Period PD1: First period PD2: Second period PM: Parameter PP: Forecast period PP1: First forecast period PP2: Second forecast period RI: Reference information

Claims

1. An information processing system comprising at least one processor, the processor being configured to execute the following steps by reading a program: in a receiving step, input information is received, the input information including at least one of first input information regarding the number of people infected with a specific infectious disease and second input information regarding weather; and in an output step, output information regarding the risk of the infectious disease causing an infection explosion based on the input information and preset reference information.

2. An information processing system according to claim 1, wherein the output information includes information indicating, as an index, the risk of the infectious disease causing an infection explosion.

3. An information processing system according to claim 1 or claim 2, wherein the output information includes information indicating whether or not there is a risk of the infectious disease causing an infection explosion.

4. An information processing system according to any one of claims 1 to 3, wherein the first input information includes information relating to the number of infected persons contracting the infectious disease in a first period and information relating to the number of infected persons contracting the infectious disease in a second period, wherein the second period is a period different from the first period.

5. An information processing system as described in claim 4, wherein the second period is a period that is temporally continuous with the first period, and the first input information is a rate of increase / decrease in the number of infected persons contracting the infectious disease based on the number of infected persons contracting the infectious disease in the first period and the number of infected persons contracting the infectious disease in the second period.

6. An information processing system according to claim 5, wherein the first input information is a parameter that turns on a flag when the rate of increase or decrease satisfies a predetermined condition, and turns off the flag when the rate of increase or decrease does not satisfy the predetermined condition.

7. An information processing system according to any one of claims 1 to 6, wherein the second input information includes at least one of information about weather and information that changes depending on weather conditions.

8. An information processing system according to any one of claims 1 to 7, wherein the second input information includes past actual values ​​and future predicted values.

9. An information processing system according to any one of claims 1 to 7, wherein the input information is information in which the period for acquiring information in the first input information corresponds to the period for acquiring information in the second input information.

10. An information processing system according to any one of claims 1 to 9, wherein the reference information is a trained model that uses at least one of the first input information and the second input information as training data to learn the correlation with the risk.

11. An information processing method comprising the steps of the information processing system according to any one of claims 1 to 10.

12. A program that causes at least one computer to execute each step of the information processing system according to any one of claims 1 to 10.

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