Predictive management systems and predictive management programs

Abstract

To provide a prediction management system and a prediction management program capable of predicting an increase / decrease trend of the number of subjects infected with an infectious disease.SOLUTION: A prediction management system includes: a pathogen data storage device 53 which stores a collection date of a sample of sewage water and pathogen data related to a pathogen of an infectious disease contained in the sample obtained through analysis of the sample in association with each other; positive subject number data storage devices 63, 74 which store the date and time and positive subject number data related to the positive subject number on the date and time in association with each other; and a prediction management device 21 which acquires the pathogen data on the collection date from the pathogen data storage device 53 and the positive subject number data related to the positive subject number in a predetermined period prior to the collection date from the positive subject number data storage devices 63, 73, predicts an increase / decrease trend after the collection date on the basis of a ratio of the acquired pathogen data to the acquired positive subject number data, and transmits information about the predicted increase / decrease trend.SELECTED DRAWING: Figure 2

Description

Technical Field

[0001] The present invention relates to a prediction management system and a prediction management program for predicting the increasing and decreasing trends of the number of infected persons with infectious diseases.

Background Art

[0002] In order to suppress the spread of infectious diseases such as novel coronavirus, infectious disease tests such as PCR tests and antigen tests are being conducted on people who have symptoms such as fever. When a positive reaction is obtained in an infectious disease test, measures such as isolating the positive person and close contacts are being taken. However, among the positive persons, there are asymptomatic persons, or there are persons who do not undergo an infectious disease test due to mild symptoms or the like even when symptoms appear, so it is difficult to predict the increasing and decreasing trends of the number of infected persons with infectious diseases.

[0003] Patent Document 1 discloses an infectious disease management system including a collection result database, an analysis result database, a resident database, and a computer program accessible to each database. The computer program described in Patent Document 1 shows that when information on the presence of infectious pathogens in a sample collected from sewage during a collection period indicates that there may be an infected person with an infectious pathogen among the residents of a facility during at least the collection period, based on the data stored in each database, it is possible to execute a step of extracting a list of residents whose collection period and the period during which the residents stayed in the facility overlap.

[0004] However, while the infectious disease control system described in Patent Document 1 can indicate the possibility of infectious pathogens being present among facility occupants and extract a list of occupants whose stay overlaps with the sampling period, it is difficult to predict the trend of increase or decrease in the number of infectious disease cases. In other words, there is a difference in the timing of when the results become known between the analysis results regarding the presence of infectious pathogens in samples collected from sewage and the aggregated results regarding the number of positive cases compiled through public health administration, making it difficult to predict the trend of increase or decrease in the number of infectious disease cases.

[0005] Alternatively, analysis of the presence of infectious pathogens in samples collected from sewage (e.g., PCR analysis) is not a continuous analysis. That is, in the infectious disease testing method described in Patent Document 1, a collection company collects samples from sewage and submits them to an analysis company, which then analyzes the samples to obtain information on the presence of infectious pathogens in the samples. Therefore, the information on the presence of infectious pathogens in the samples is not continuous information over time, making it difficult to predict trends in the increase or decrease of the number of infected people with infectious diseases. Against this backdrop, there is a desire to predict trends in the increase or decrease of the number of infected people with infectious diseases such as COVID-19. [Prior art documents] [Patent Documents]

[0006] [Patent Document 1] Patent No. 7031957 [Overview of the project] [Problems that the invention aims to solve]

[0007] This invention has been made in view of the above circumstances, and aims to provide a predictive management system and a predictive management program that can predict the trend of increase or decrease in the number of people infected with infectious diseases. [Means for solving the problem]

[0008] A first aspect of the present invention is a predictive management system for predicting the trend of increase or decrease in the number of people infected with an infectious disease, comprising: a pathogen data storage device that stores, in association with each other, the date on which a sewage sample is collected and pathogen data relating to the pathogen of the infectious disease contained in the sample obtained by analyzing the sample; a positive case data storage device that stores, in association with each other, a date and positive case data relating to the number of positive cases of the infected person on that date; and a predictive management device that is communicably connected to the pathogen data storage device and the positive case data storage device, and which acquires the pathogen data on the collection date from the pathogen data storage device and acquires positive case data relating to the number of positive cases for a predetermined period prior to the collection date from the positive case data storage device, predicts the trend of increase or decrease after the collection date based on the ratio of the acquired pathogen data and the acquired positive case data, and transmits information regarding the predicted trend of increase or decrease.

[0009] A second aspect of the present invention is a predictive management program executed by a computer of a predictive management device that predicts the trend of increase or decrease in the number of infected persons of an infectious disease, and is communicably connected to a pathogen data storage device that stores, in association with the date of collection of a sewage sample and pathogen data relating to infectious disease pathogens contained in the sample obtained by analyzing the sample, and a positive number data storage device that stores, in association with a year, month, and day and positive number data relating to the number of positive persons of the infectious disease on the year, month, and day, and predicts the trend of increase or decrease in the number of infected persons of the infectious disease, wherein the computer is caused to perform a first acquisition step of acquiring the pathogen data on the collection date from the pathogen data storage device; a second acquisition step of acquiring positive number data relating to the number of positive persons of the infectious disease for a predetermined period prior to the collection date from the positive number data storage device; a prediction step of predicting the trend of increase or decrease after the collection date based on the ratio of the pathogen data acquired in the first acquisition step and the positive number data acquired in the second acquisition step; and a transmission step of transmitting information regarding the predicted trend of increase or decrease. [Effects of the Invention]

[0010] According to the present invention, it is possible to provide a predictive management system and a predictive management program that can predict the trend of increase or decrease in the number of people infected with infectious diseases. [Brief explanation of the drawing]

[0011] [Figure 1] This is a schematic diagram illustrating the outline of a predictive management system according to an embodiment of the present invention. [Figure 2] This is a block diagram showing the main hardware components of the predictive management system according to this embodiment. [Figure 3] This is a block diagram showing the main components of the predictive management device of this embodiment. [Figure 4] This is a block diagram showing the main components of the inspection company terminal in this embodiment. [Figure 5] This is a block diagram showing the main components of the administrative agency terminal and the medical institution terminal in this embodiment. [Figure 6] This is a block diagram showing the main components of the user terminal in this embodiment. [Figure 7] This is a schematic diagram illustrating the relationship between virus concentration and the number of positive cases in this embodiment. [Figure 8] This graph shows an example of the relationship between the number of positive cases, virus concentration, and the 7-period moving average of positive cases in a given region. [Figure 9] This is a schematic diagram illustrating the ratio between virus data and the number of positive cases. [Figure 10] This is a sequence chart showing a first specific example of the processing procedure of the predictive management system according to this embodiment. [Figure 11] This is a sequence chart showing a second specific example of the processing procedure of the predictive management system according to this embodiment. [Figure 12] This graph illustrates the first specific example of a method for calculating a predetermined probability (threshold). [Figure 13] This graph illustrates a second specific example of a method for calculating a predetermined probability (threshold). [Modes for carrying out the invention]

[0012] Hereinafter, embodiments of the present invention will be described with reference to the drawings. Note that the embodiments described below are preferred specific examples of the present invention, and thus are technically preferably subject to various limitations. However, the scope of the present invention is not limited to these aspects unless there is a description specifically limiting the present invention in the following description. For example, in the embodiments described below, the novel coronavirus is cited as a specific example of the pathogen, but the pathogen is not limited to the novel coronavirus, and may be other viruses such as influenza virus and norovirus. Further, in each drawing, the same components are denoted by the same reference numerals, and detailed descriptions thereof are appropriately omitted.

[0013] FIG. 1 is a schematic diagram for explaining the outline of a prediction management system according to an embodiment of the present invention. First, the outline of the prediction management system according to the present embodiment will be described with reference to FIG. 1. The prediction management system according to the present embodiment is a system for predicting the trend of increase and decrease in the number of infected persons with an infectious disease. In the present specification, an "infected person" means a person infected with a pathogen (that is, a person in whom the pathogen has invaded the body), and includes not only a person who shows a positive reaction in an infectious disease test (that is, a positive person), but also a person who has been infected with a pathogen and has not undergone an infectious disease test (that is, an untested infected person). Further, in the present specification, the scope of "prediction" includes not only the meaning of prediction such as predicting the course and result of an event in advance, but also the meaning of foresight such as seeing through an event before it occurs. In the prediction management system shown in FIG. 1, a management company 2, a collection operator 3, a sewage treatment plant 4, a testing company 5, an administrative agency 6 such as a municipal government office, and a medical institution 7 such as a hospital and a health department of a local government are related to each other. As shown by the two-dot chain line frame in FIG. 1, the testing company 5 may be included in the management company 2. That is, the testing company 5 may be the same company as the management company 2.

[0014] First, as shown by arrow A1 and arrow A2 in FIG. 1, the management company 2 receives a request regarding prediction of the increasing or decreasing trend of the number of infected persons with infectious diseases from at least one of the administrative institution 6 and the medical institution 7. Each of the administrative institution 6 and the medical institution 7 in the present embodiment is an example of the "institution" of the present invention.

[0015] Subsequently, as shown by arrow A3 in FIG. 1, the management company 2 requests the collection contractor 3 to collect a sewage sample. Note that the collection of the sewage sample may be performed by the management company 2. In this specification, for the sake of convenience of explanation, the sewage sample is also simply referred to as the "sewage sample".

[0016] Subsequently, as shown by arrow A4 in FIG. 1, the collection contractor 3 goes to the sewage treatment plant 4 upon receiving the request from the management company 2 and collects a sewage sample. For example, the collection contractor 3 collects a sewage sample flowing in the sewage pipe of a separate sewer. Alternatively, for example, the collection contractor 3 collects a sewage sample flowing in the combined sewer of a combined sewer. Alternatively, for example, the collection contractor 3 collects a sewage sample flowing in the inflow channel of the primary sedimentation tank of the sewage treatment plant 4. Subsequently, the collection contractor 3 provides the collected sewage sample to the inspection company 5 together with the date (i.e., the collection date) and the location (i.e., the collection location) of the sewage sample.

[0017] Subsequently, the inspection company 5 analyzes the sewage sample provided by the collection contractor 3 by PCR testing or the like and obtains pathogen data regarding the pathogens of infectious diseases contained in the sewage sample. Then, the inspection company 5 stores the obtained pathogen data, the collection date of the sewage sample, and the collection location of the sewage sample in association with each other. The method for obtaining the pathogen data may be a method for obtaining non-consecutive data over time, such as PCR testing, or may be a method for obtaining densely continuous data over time, such as testing by a sensor utilizing an antigen-antibody reaction.

[0018] As mentioned above, testing company 5 may be included in management company 2. In this case, management company 2 will analyze the sewage samples provided by collection company 3 using PCR testing or the like, and obtain pathogen data regarding infectious disease pathogens contained in the sewage samples. Management company 2 will then store the obtained pathogen data, the date the sewage samples were collected, and the location where the sewage samples were collected, associating them with each other.

[0019] On the other hand, administrative agency 6 collects data on the number of people who have tested positive for the infectious disease and stores it in association with the date and the data on the number of positive cases on that date. For the sake of clarity, in this specification, the data on the number of positive cases is also referred to simply as "data on the number of positive cases" or "number of positive cases." Alternatively, medical institution 7 may collect data on the number of people who have tested positive for the infectious disease and store it in association with the date and the data on the number of positive cases on that date.

[0020] Next, as indicated by arrow A5 in Figure 1, management company 2 obtains pathogen data from testing company 5 on the day the sewage sample is collected. Also, as indicated by arrows A1 and A2 in Figure 1, management company 2 obtains data on the number of positive cases for a predetermined period prior to the sewage sample collection date from at least one of administrative agency 6 and medical institution 7. Based on the ratio of the pathogen data obtained from testing company 5 to the data on the number of positive cases obtained from at least one of administrative agency 6 and medical institution 7, management company 2 predicts the trend of increase or decrease in the number of infectious disease cases after the sewage sample collection date. Details of the method for predicting the trend will be described later.

[0021] Next, as shown by arrows A6 and A7 in Figure 1, management company 2 transmits (i.e., provides) information regarding the predicted increase or decrease in the number of infected persons to at least one of administrative agency 6 and medical institution 7. Subsequently, as shown by arrow A8 in Figure 1, administrative agency 6 provides medical institution 7 with the information regarding the increase or decrease in the number of infected persons provided by management company 2. Alternatively, as shown by arrow A9 in Figure 1, medical institution 7 provides administrative agency 6 with the information regarding the increase or decrease in the number of infected persons provided by management company 2.

[0022] Next, as shown by arrows A11 and A12 in Figure 1, at least one of the administrative agency 6 and the medical institution 7 notifies the user terminals 8 of residents near the sewage sample collection site of the trend in the increase or decrease in the number of infected people, based on the information received from the management company 2. Residents near the sewage sample collection site include, for example, residents living in the administrative area that includes the service area of ​​the public sewer and sewage treatment plant 4 where the collection company 3 collected the sewage samples. The user terminal 8 is a portable terminal device used by residents (i.e., users), such as a smartphone, tablet computer, or mobile phone.

[0023] As shown by arrow A13 in Figure 1, management company 2 may notify the user terminals 8 of residents near the sewage sample collection site of the trend in the increase or decrease in the number of infected people, based on information about the trend in the increase or decrease in the number of infected people that management company 2 itself has predicted.

[0024] Next, the hardware of the predictive management system according to this embodiment will be described with reference to the drawings. Figure 2 is a block diagram showing the main hardware components of the predictive management system according to this embodiment.

[0025] The predictive management system primarily comprises a predictive management device 21, a testing company terminal 51, at least one of a government agency terminal 61 and a medical institution terminal 71, and a user terminal 8. The user terminal 8 is not limited to one, but may be multiple. Figure 2 shows one user terminal 8.

[0026] The predictive management device 21 is owned by and used by management company 2, and comprises a computer (i.e., a server computer) 22, a storage device 23 connected to the computer 22, and a communication unit 24 connected to the computer 22. The computer 22 has a control unit 25 (see Figure 3) and performs various calculations and processes. In this specification, "computer" is not limited to personal computers, but also includes arithmetic processing units, microcomputers, etc., included in information processing equipment, and is a general term for equipment and devices that can realize the functions of the present invention by program.

[0027] The inspection company terminal 51 is a terminal owned and used by the inspection company 5, and includes a computer 52, a storage device 53 connected to the computer 52, and a communication unit 54 connected to the computer 52. The computer 52 has a control unit 55 (see Figure 4) and performs various calculations and processes. As mentioned above, the inspection company 5 may be included in the management company 2. In this case, the inspection company terminal 51 is a terminal owned and used by the management company 2, and may be integrated with the predictive management device 21.

[0028] The administrative agency terminal 61 is a terminal owned and used by the administrative agency 6, and comprises a computer 62, a storage device 63 connected to the computer 62, and a communication unit 64 connected to the computer 62. The computer 62 has a control unit 65 (see Figure 5) and performs various calculations and processes. The medical institution terminal 71 is a terminal owned and used by the medical institution 7, and comprises a computer 72, a storage device 73 connected to the computer 72, and a communication unit 74 connected to the computer 72. The computer 72 has a control unit 75 (see Figure 5) and performs various calculations and processes. The administrative agency terminal 61 and the medical institution terminal 71 are examples of "agency terminals" of the present invention.

[0029] The user terminal 8 is a portable terminal device used by residents (i.e., users), such as a smartphone, tablet computer, or mobile phone, and includes a computer 82, a storage device 83 connected to the computer 82, and a communication unit 84 connected to the computer 82. The computer 82 has a control unit 85 (see Figure 6) and performs various calculations and processes. The user terminal 8 may have a display including a touch panel that can detect, for example, the user's finger touch. The user terminal 8 may also have a function to detect the user's current location (the location of the user terminal 8). The user's current location can be detected by, for example, GPS (Global Positioning System).

[0030] As shown in Figure 2, the predictive management device 21, the inspection company terminal 51, the government agency terminal 61, the medical institution terminal 71, and the user terminal 8 are all connected to each other via a network 9, such as the Internet, through their respective communication units 24, 54, 64, 74, and 84.

[0031] Figure 3 is a block diagram showing the main components of the predictive management device of this embodiment. As shown in Figure 3, the prediction management device 21 includes a control unit 25, a storage device 23, and a communication unit 24.

[0032] The control unit 25 is, for example, a CPU (central processing unit), and reads the program 231 stored in the storage device 23 and performs various calculations and processes. The control unit 25 has a prediction unit 251, a transmission processing unit 252, and a notification processing unit 253. The control unit 25 does not necessarily have a notification processing unit 253. The prediction unit 251, the transmission processing unit 252, and the notification processing unit 253 are realized by the computer 22 (see Figure 2) executing the program 231 stored in the storage device 23. The prediction unit 251, the transmission processing unit 252, and the notification processing unit 253 may be realized by hardware, or by a combination of hardware and software.

[0033] The prediction unit 251 predicts the trend of increase or decrease in the number of infected individuals with infectious diseases after the date of sewage sample collection, based on the ratio of pathogen data 532 (see Figure 4) obtained from the storage device 53 of the testing company terminal 51 via the communication unit 24 and network 9, and positive case data 632, 732 (see Figure 5) obtained from at least one of the storage devices 63 of the administrative agency 6 and the storage device 73 of the medical institution 7 via the communication unit 24 and network 9. The prediction unit 251 then stores the predicted trend as trend data 232 in the storage device 23. Details of how the prediction unit 251 predicts the trend of increase or decrease in the number of infected individuals with infectious diseases will be described later.

[0034] The transmission processing unit 252 transmits information regarding the trend of increase or decrease in the number of infected persons of the infectious disease predicted by the prediction unit 251, i.e., the increase / decrease trend data 232 stored in the storage device 23, to at least one of the administrative agency 6 and the medical institution 7 via the communication unit 24 and the network 9.

[0035] The notification processing unit 253 notifies user terminals 8 of residents near the sewage sample collection site of the trend in the increase or decrease of the number of infected people, via the communication unit 24 and the network 9, based on information regarding the trend in the increase or decrease of the number of infected people predicted by the prediction unit 251, i.e., the trend in increase or decrease data 232 stored in the storage device 23.

[0036] The storage device 23 stores the program 231 and the increase / decrease trend data 232. The increase / decrease trend data 232 is information regarding the increase / decrease trend in the number of infected people of the infectious disease predicted by the prediction unit 251. Examples of storage devices 23 include semiconductor memory or a hard disk drive (HDD) built into the prediction management device 21. Alternatively, the storage device 23 may be an external storage device connected to the computer 22.

[0037] Program 231 includes calculation programs for predicting trends in the increase or decrease of the number of people infected with infectious diseases. Program 231 is not limited to being stored in the storage device 23; it may also be pre-stored and distributed on a computer-readable storage medium, or downloaded to the prediction management device 21 via the network 9.

[0038] Figure 4 is a block diagram showing the main components of the inspection company terminal in this embodiment. As shown in Figure 4, the inspection company terminal 51 includes a control unit 55, a storage device 53, a communication unit 54, and a display unit 56. However, the inspection company terminal 51 does not necessarily have to include a display unit 56.

[0039] The control unit 55 is, for example, a CPU, and reads the program 531 stored in the memory device 53 and performs various calculations and processes. The control unit 55 stores the pathogen data 532 obtained by PCR testing, the date of collection of the sewage sample, and the location of collection of the sewage sample in the memory device 53, associating them with each other.

[0040] The storage device 53 stores the program 531 and the pathogen data 532. The storage device 53 in this embodiment is an example of the "pathogen data storage device" of the present invention. Examples of the storage device 53 include semiconductor memory or hard disk drive built into the testing company terminal 51. Alternatively, the storage device 53 may be an external storage device connected to the computer 52. The program 531 includes calculation programs for analyzing and processing the pathogen data 532.

[0041] Pathogen data 532 is data obtained by analyzing sewage samples and concerns infectious disease pathogens contained in the sewage samples. Pathogen data 532 may be the data obtained by analyzing sewage samples as is, or it may be data obtained by analyzing sewage samples and then subjected to a predetermined process.

[0042] The pathogen data 532 stored in the memory device 53 is, for example, the concentration of pathogens obtained by analyzing a sample of sewage flowing through a sewer pipe of a separate sewer system. Alternatively, the pathogen data 532 stored in the memory device 53 is, for example, the concentration of pathogens obtained by analyzing a sample of sewage flowing through a combined sewer pipe of a combined sewer system.

[0043] As mentioned above, the pathogen data 532 may be data obtained by analyzing a sewage sample and then processing it according to a predetermined procedure. The pathogen data 532 stored in the storage device 53 is, for example, the amount of pathogen load calculated by multiplying the concentration of pathogens obtained by analyzing a sewage sample flowing through a sewer pipe of a separate sewer system by the flow rate of the sewage. Alternatively, the pathogen data 532 stored in the storage device 53 is, for example, the amount of pathogen load calculated by multiplying the concentration of pathogens obtained by analyzing a sewage sample flowing through a combined sewer pipe of a combined sewer system by the flow rate of the sewage.

[0044] The display unit 56 is, for example, a display including a liquid crystal panel. Alternatively, the display unit 56 may be a display including a touch panel capable of detecting, for example, human finger contact. In this case, the operator can input various types of information, such as necessary information, by operating the display unit 56.

[0045] Figure 5 is a block diagram showing the main components of the administrative agency terminal and the medical institution terminal in this embodiment. The administrative agency terminal 61 and the medical institution terminal 71 are similar to each other. Therefore, in this embodiment, the "agency terminal" of the present invention will be explained using the administrative agency terminal 61 as an example.

[0046] As shown in Figure 5, the administrative agency terminal 61 includes a control unit 65, a storage device 63, a communication unit 64, and a display unit 66. However, the administrative agency terminal 61 does not necessarily have to include a display unit 66.

[0047] The control unit 65 is, for example, a CPU, which reads the program 631 stored in the storage device 63 and performs various calculations and processes. The control unit 65 has a notification processing unit 653. The notification processing unit 653 is realized by the computer 62 (see Figure 2) executing the program 631 stored in the storage device 63. The notification processing unit 653 may be realized by hardware, or by a combination of hardware and software.

[0048] The control unit 65 stores the date and the number of positive cases of the infectious disease on that date in the storage device 63, associating them with each other. Based on information received from the predictive management device 21 via the communication unit 24 and network 9 regarding the trend of increase or decrease in the number of infected people, the notification processing unit 653 notifies the user terminals 8 of residents near the sewage sample collection site of the trend of increase or decrease in the number of infected people via the communication unit 24 and network 9.

[0049] The storage device 63 stores the program 631 and the positive case count data 632. The storage device 63 in this embodiment is an example of the "positive case count data storage device" of the present invention. Examples of the storage device 63 include semiconductor memory or hard disk drive built into the administrative agency terminal 61. Alternatively, the storage device 63 may be an external storage device connected to the computer 62. The program 631 includes a calculation program for collecting and processing the positive case count data 632.

[0050] The positive case data 632 is data on the number of people who have tested positive for infectious diseases, collected by the administrative agency 6. The positive case data 632 may be the collected data as is, or it may be the collected data after a predetermined processing has been performed. The positive case data 632 stored in the storage device 63 is, for example, the total or average number of positive cases during a predetermined period prior to the day on which the sewage sample was collected. Here, "predetermined period" refers to, for example, about one week. Alternatively, the positive case data 632 stored in the storage device 63 is, for example, the total or average number of hospitalized patients among the positive cases during a predetermined period prior to the day on which the sewage sample was collected. Alternatively, the positive case data 632 stored in the storage device 63 is, for example, the total or average number of deaths among the positive cases during a predetermined period prior to the day on which the sewage sample was collected.

[0051] The display unit 66 is, for example, a display including a liquid crystal panel. Alternatively, the display unit 66 may be a display including a touch panel capable of detecting, for example, human finger contact. In this case, the operator can input various types of information, such as necessary information, by operating the display unit 66.

[0052] With respect to Figure 5, similar to the administrative agency terminal 61 described above, the medical institution terminal 71 has a control unit 75, a storage device 73, a communication unit 74, and a display unit 76. Note that the medical institution terminal 71 does not necessarily have a display unit 76. The control unit 75 has a notification processing unit 753. The storage device 73 is an example of the "positive case count data storage device" of the present invention, and stores a program 731 and positive case count data 732.

[0053] Figure 6 is a block diagram showing the main components of the user terminal in this embodiment. As shown in Figure 6, the user terminal 8 includes a control unit 85, a storage device 83, a communication unit 84, and a display unit 86.

[0054] The control unit 85 is, for example, a CPU, and reads a program (not shown) stored in the storage device 83 and performs various calculations and processes. For example, the control unit 85 receives notifications regarding trends in the increase or decrease of the number of infected people from at least one of the administrative agency terminal 61, the medical institution terminal 71, and the predictive management device 21 via the communication unit 24 and the network 9, and performs the process of displaying the notifications regarding trends in the increase or decrease of the number of infected people on the display unit 86.

[0055] The display unit 86 is, for example, a display including a liquid crystal panel. Alternatively, the display unit 86 may be a display including a touch panel capable of detecting, for example, a person's finger touch. In this case, users can input various types of information, such as necessary information, by operating the display unit 86.

[0056] Next, the method by which the predictive management device 21 of this embodiment predicts the trend of increase or decrease in the number of people infected with infectious diseases will be explained with reference to the drawings. In the following explanation, we will use the case where the pathogen is a virus as an example. For the sake of convenience, in this specification, data concerning viruses contained in sewage samples will also be simply referred to as "virus data." Here, virus data is an example of the "pathogen data" of the present invention, and is the concentration of the virus obtained by analyzing sewage samples in the combined sewer pipe of a combined sewer system or the wastewater pipe of a separate sewer system. For the sake of convenience, in this specification, this concentration of the virus will also be simply referred to as "virus concentration."

[0057] Figure 7 is a schematic diagram illustrating the relationship between virus concentration and the number of positive cases in this embodiment. Figure 8 is a graph showing an example of the relationship between the number of positive cases, virus concentration, and the 7-period moving average number of positive cases in a given region. Figure 9 is a schematic diagram illustrating the ratio of virus data to the number of positive cases.

[0058] For example, to curb the spread of infectious diseases such as COVID-19, PCR tests and antigen tests are conducted on people who exhibit symptoms such as fever. If a positive result is obtained from an infectious disease test, measures such as isolating the infected person and close contacts are taken. However, it is difficult to predict the trend of increase or decrease in the number of infected people because some positive individuals are asymptomatic, and some people who develop symptoms do not undergo infectious disease testing because their symptoms are mild.

[0059] Therefore, the inventors attempted to predict the trend of increase or decrease in the number of infectious disease cases in the administrative area including the service area of ​​the public sewer and sewage treatment plant 4 by collecting general wastewater samples, such as domestic wastewater, from public sewers and sewage treatment plants 4, and using data on viruses contained in those samples.

[0060] However, there is a difference in the timing of when the results become available between the data on viruses contained in sewage samples from a public sewer system and sewage treatment plant 4 (i.e., virus data) and the data on the number of positive cases compiled through public health administration in the administrative area that includes the service area of ​​that public sewer system and sewage treatment plant 4. Therefore, there is a time difference between the trend of increase or decrease in virus data and the trend of increase or decrease in the number of positive cases data. For example, as shown in Figure 7, the trend of increase or decrease in the number of positive cases in infectious disease tests (i.e., positive case data) lags behind the trend of increase or decrease in the virus concentration contained in sewage samples (i.e., virus data).

[0061] Figure 8 is a graph compiled by the inventor, showing the number of positive cases collected in a certain administrative area, the most recent 7-period moving average of the number of positive cases, the virus concentration in the combined sewer system (System 1) and the virus concentration in the separate sewer system (System 2) within that administrative area. According to Figure 8, the peaks on the vertical axis of the graph after January 6, when the number of positive cases began to increase, are first on February 24 (virus concentration in System 2), followed by the virus concentration in System 1 on March 3, and finally the 7-period moving average of the number of positive cases on March 5. In the subsequent upward phase of the number of positive cases after May 5, the peaks on the vertical axis of the graph are first on May 12 (virus concentration in System 1), followed by the virus concentration in System 2 on May 19, and finally the 7-period moving average of the number of positive cases on May 26. In other words, regarding the virus concentration, the peak on the vertical axis of the graph can occur either first for System 1 or first for System 2. However, the 7-period moving average of positive cases is the last to be calculated for both the period after January 6th and the period after May 5th. This also shows that the trend of increase or decrease in positive case data lags behind the trend of increase or decrease in virus data (i.e., virus concentration in Figure 8).

[0062] Thus, there is a time lag between the trend of increase or decrease in virus data and the trend of increase or decrease in the number of positive cases. Therefore, simply obtaining data on viruses contained in sewage samples makes it difficult to predict the trend of increase or decrease in the number of infected people with infectious diseases. For example, as shown in Figure 7, there are two values ​​W for the concentration of virus (i.e., virus concentration) contained in sewage samples: virus concentration W1 in the first period T1 when there is an increasing trend, and virus concentration W2 in the second period T2 when there is a decreasing trend. Therefore, simply obtaining data on viruses contained in sewage samples does not tell us whether it is from a period of increasing or decreasing trends. For this reason, it is difficult to predict the trend of increase or decrease in the number of infected people with infectious diseases.

[0063] Furthermore, as shown in Figure 8, when analyzing sewage samples using methods such as PCR testing to obtain virus concentration, the analysis using PCR testing is not continuous. In other words, in the example in Figure 8, the sampling company 3 collects sewage samples from public sewers and sewage treatment plants 4, etc., and delivers them to another location such as testing company 5. Then, testing company 5 analyzes the sewage samples to obtain virus concentration. Therefore, the obtained virus concentration data does not constitute closely continuous data over time. Specifically, the analysis of sewage samples to obtain virus concentration is performed once a week for both combined sewer systems (system 1) and separate sewer systems (system 2). For this reason, for example, it is not possible to accurately determine whether the virus concentration obtained today is on an increasing or decreasing trend until the virus concentration is obtained again a week later. In this regard, for example, the virus concentration (system 2) in Figure 8 reached a small peak on February 3rd, then showed a decreasing trend until February 17th, but then reached a very large peak the following week on February 24th. In this example, the virus concentration (system 2) as of February 17th was on a downward trend compared to February 3rd. If we were to judge the following week based on the increase / decrease trend from the past to the present, the concentration on February 24th should have been lower than the current level (February 17th). However, this did not actually happen, and the virus concentration (system 2) became very high. As this example shows, it is difficult to predict the increase or decrease trend in the number of infected people in an infectious disease when continuous analysis is not performed.

[0064] Therefore, the prediction unit 251 of the prediction management device 21 of this embodiment actively utilizes the fact that there is a time difference between the trend of increase or decrease in virus data and the trend of increase or decrease in the number of positive cases data, and predicts the trend of increase or decrease in the number of infected people of the infectious disease after the day the sewage sample was collected, based on the ratio of virus data to the number of positive cases data. To explain in detail with reference to Figure 7, the ratio of virus concentration W1 to the number of positive cases C1 (W1 / C1) in the first period T1 is greater than the ratio of virus concentration W2 to the number of positive cases C2 (W2 / C2) in the second period T2. Here, virus concentration W1 is equal to virus concentration W2. In this specification, the ratio of virus data to the number of positive cases is defined as the virus concentration W relative to the number of positive cases C (i.e., W / C), but the specification is not limited to this, and the opposite (i.e., C / W) may also be used.

[0065] The prediction unit 251 of the prediction management device 21 of this embodiment utilizes these properties and, as shown in Figure 9, predicts that in the third period T3, when the ratio of virus concentration W to the number of positive cases C (W / C) is relatively high, the number of infected people may increase in the future, or that a large proportion of infected people have not yet been identified. On the other hand, the prediction unit 251 of the prediction management device 21 of this embodiment predicts that in the fourth period T4, when the ratio of virus concentration W to the number of positive cases C (W / C) is relatively low, the number of infected people may decrease in the future, or that a small proportion of infected people have not yet been identified. Alternatively, the prediction unit 251 of the prediction management device 21 of this embodiment sets a threshold for the ratio (W / C) that distinguishes between periods when the number of infected people is increasing and periods when it is decreasing, based on past statistical values, and predicts the trend of increase or decrease in the number of infected people by comparing the ratio of virus concentration W to the number of positive cases C (W / C) on a predetermined day with the threshold.

[0066] Next, a specific example of the processing procedure of the predictive management system according to this embodiment will be described with reference to the drawings. Figure 10 is a sequence chart showing a first specific example of the processing procedure of the predictive management system according to this embodiment. Figure 11 is a sequence chart showing a second specific example of the processing procedure of the predictive management system according to this embodiment. Figure 12 is a graph illustrating the first specific example of a method for calculating a predetermined probability (threshold). Figure 13 is a graph illustrating a second specific example of the method for calculating a predetermined probability (threshold). In the following explanation, we will use the case where the "institutional terminal" of the present invention is an administrative agency terminal 61 as an example.

[0067] First, as shown in Figure 10, in step S11, the testing company terminal 51 stores the virus data (i.e., pathogen data 532) obtained by PCR testing, etc., the date of collection of the sewage sample, and the location of collection of the sewage sample in the storage device 53, associating them with each other. Meanwhile, in step S12, the administrative agency terminal 61 collects data on the number of people who tested positive for the infectious disease and stores the date and the positive case data 632 related to the number of people who tested positive for the infectious disease on that date in the storage device 63, associating them with each other.

[0068] Step S11 may be executed before or after step S12. Steps S11 and S12 may be executed in parallel. Steps S11 and S12 may also be executed independently of each other.

[0069] Next, in step S13, the predictive management device 21 acquires virus data for the day the sewage sample was collected from the storage device 53 of the testing company terminal 51 via the communication unit 24 and the network 9. Step S13 in this embodiment is an example of the "first acquisition step" of the present invention. Next, in step S14, the predictive management device 21 acquires positive case data 632 regarding the number of positive cases for a predetermined period prior to the day the sewage sample was collected from the storage device 63 of the administrative agency 6 via the communication unit 24 and the network 9. Step S14 in this embodiment is an example of the "second acquisition step" of the present invention.

[0070] Next, in step S15, the prediction unit 251 of the prediction management device 21 calculates the ratio (W / C) of the virus concentration W to the number of positive cases C based on the virus data acquired from the storage device 53 of the testing company terminal 51 and the positive case data 632 acquired from the storage device 63 of the administrative agency 6. Then, based on the calculated ratio, the prediction unit 251 of the prediction management device 21 predicts the trend of increase or decrease in the number of infected people after the day on which the sewage sample was collected. Step S15 of this embodiment is an example of the "prediction step" of the present invention. For example, in step S15, the prediction unit 251 of the prediction management device 21 predicts the trend of increase or decrease in the number of infected people based on the ratio at which the probability that the number of positive cases data 632 one week after a predetermined day has increased compared to the number of positive cases data 632 on a predetermined day is greater than or equal to a predetermined probability (threshold). An example of how to calculate the predetermined probability (threshold) will be described below with reference to the drawings.

[0071] Figure 12 is a graph illustrating an example of the relationship between the increase rate of the number of PCR-positive cases (i.e., the number of PCR-positive cases shown on the horizontal axis of Figure 12) in the following week and the ratio of the viral concentration W to the number of positive cases C (i.e., the W / C ratio shown on the left vertical axis of Figure 12). The W / C ratio shown on the right vertical axis of Figure 12 is the ratio of the viral load W, calculated by multiplying the viral concentration by the sewage flow rate, to the number of positive cases C (Figure 13, described later, is similar). When the value on the horizontal axis of Figure 12 (the increase rate of the number of PCR-positive cases in the following week) is greater than "1.00", the number of positive cases one week after a given day increases relative to the number of positive cases on that day. When the value on the horizontal axis of Figure 12 is less than "1.00", the number of positive cases one week after a given day decreases relative to the number of positive cases on that day.

[0072] Here, focusing on the ratio (W / C ratio; system concentration) of the virus concentration W in a sewage sample flowing through a combined sewer pipe, to the number of positive cases C, if the W / C ratio (virus concentration) shown on the left vertical axis of Figure 12 is 400, then out of the total number of data points (5) with a W / C ratio of 400 or more, there are 4 data points where the value on the horizontal axis shown in Figure 12 (the increase ratio of the number of positive PCR test cases in the following week) is greater than "1.00", and there is 1 data point where the value on the horizontal axis shown in Figure 12 is less than "1.00".

[0073] Therefore, in step S15, the prediction unit 251 of the prediction management device 21 sets "400" as the W / C ratio threshold, for example, the probability that the number of positive cases one week after a predetermined day has increased compared to the number of positive cases on a predetermined day is 80% (= 4 cases / 5 cases × 100%) or more. The prediction unit 251 of the prediction management device 21 then predicts that the number of infected people is on an increasing trend if the ratio of the virus concentration W to the number of positive cases C (W / C ratio; system concentration) on a predetermined day is 400 or more. On the other hand, the prediction unit 251 of the prediction management device 21 predicts that the number of infected people is on a decreasing trend if the ratio of the virus concentration W to the number of positive cases C (W / C ratio; system concentration) on a predetermined day is less than 400. However, "400" is just one example of a W / C ratio threshold, and the W / C ratio threshold is not limited to "400".

[0074] Furthermore, Figure 13, similar to the graph described above for Figure 12, is a graph that shows an example of the relationship between the weekly increase rate of the number of positive PCR test results and the ratio of viral concentration W to the number of positive cases C (W / C ratio). The values ​​on the horizontal axis of Figure 13 (weekly increase rate of the number of positive PCR test results) are as described above for Figure 12.

[0075] Here, focusing on the ratio (W / C ratio; 2-system concentration) of the virus concentration W in a sewage sample flowing through a sewer pipe of a separate sewer system to the number of positive cases C, if the W / C ratio (virus concentration) shown on the left vertical axis of Figure 13 is 400, then out of the total number of data points (12) with a W / C ratio of 400 or more, there are 9 data points where the value on the horizontal axis shown in Figure 13 (the increase ratio of the number of positive PCR test cases in the following week) is greater than "1.00", and there are 3 data points where the value on the horizontal axis shown in Figure 13 is less than "1.00".

[0076] Therefore, in step S15, the prediction unit 251 of the prediction management device 21 sets "400" as the W / C ratio threshold, for example, the probability that the number of positive cases one week after a predetermined day has increased compared to the number of positive cases on a predetermined day is 75% (= 9 cases / 12 cases × 100%) or more. The prediction unit 251 of the prediction management device 21 then predicts that the number of infected people is on an increasing trend if the ratio of the virus concentration W to the number of positive cases C (W / C ratio; 2-system concentration) on a predetermined day is 400 or more. On the other hand, the prediction unit 251 of the prediction management device 21 predicts that the number of infected people is on a decreasing trend if the ratio of the virus concentration W to the number of positive cases C (W / C ratio; 2-system concentration) on a predetermined day is less than 400. However, as mentioned above with respect to Figure 12, "400" is just one example of a W / C ratio threshold, and the W / C ratio threshold is not limited to "400".

[0077] In step S16, following step S15, the transmission processing unit 252 of the prediction management device 21 transmits information regarding the increase or decrease trend in the number of infected persons of the infectious disease predicted by the prediction unit 251, i.e., the increase or decrease trend data 232 stored in the storage device 23, to the administrative agency terminal 61 via the communication unit 24 and the network 9. Step S16 in this embodiment is an example of the "transmission step" of the present invention. At this time, the transmission processing unit 252 of the prediction management device 21 classifies the increase or decrease trend in the number of infected persons of the infectious disease according to the W / C ratio and transmits information regarding the classified increase or decrease trend to the administrative agency terminal 61.

[0078] For example, the transmission processing unit 252 of the predictive management device 21 divides the trend of increase or decrease in the number of infected people into three categories according to the W / C ratio. When the W / C ratio is relatively high, it transmits information with the trend marked in red to the administrative agency terminal 61. When the W / C ratio is relatively moderate, it transmits information with the trend marked in yellow to the administrative agency terminal 61. When the W / C ratio is relatively low, it transmits information with the trend marked in blue to the administrative agency terminal 61. However, the number of categories into which the trend of increase or decrease in the number of infected people is divided by the transmission processing unit 252 of the predictive management device 21 is not limited to three; it may be two, four or more. Furthermore, the form in which the trend of increase or decrease in the number of infected people is divided by the transmission processing unit 252 of the predictive management device 21 is not limited to color; it may also be a classification form using shapes and patterns.

[0079] Next, in step S17, the notification processing unit 653 of the administrative agency terminal 61 notifies the user terminals 8 of residents near the sewage sample collection site of the trend in the increase or decrease of the number of infected persons, based on the information on the trend in the increase or decrease of the number of infected persons received from the predictive management device 21 via the communication unit 24 and the network 9.

[0080] Alternatively, as shown in step S27 in Figure 11, the notification processing unit 253 of the prediction management device 21 may notify the user terminals 8 of residents near the sewage sample collection site of the trend in the increase or decrease of the number of infected persons, based on information regarding the trend in the increase or decrease of the number of infected persons predicted by the prediction unit 251 of the prediction management device 21, i.e., the increase or decrease trend data 232 stored in the storage device 23, via the communication unit 24 and the network 9. Steps S12 to S26 shown in Figure 11 are the same as steps S11 to S16 described above with respect to Figure 10.

[0081] Next, in step S18, the control unit 85 of the user terminal 8 executes a process to display a notification regarding the trend of increase or decrease in the number of infected people on the display unit 86. Step S28 shown in Figure 11 is the same as step S18 described above with respect to Figure 10.

[0082] As described above, according to the prediction management system and prediction management program of this embodiment, the prediction management device 21 acquires virus data on the day the sewage sample is collected from the storage device 53 of the testing company terminal 51 via the communication unit 24 and the network 9. The prediction management device 21 also acquires positive case data 632 regarding the number of positive cases for a predetermined period prior to the day the sewage sample is collected from the storage device 63 of the administrative agency 6 via the communication unit 24 and the network 9. The prediction unit 251 of the prediction management device 21 then calculates the ratio of virus concentration W to the number of positive cases C (W / C) based on the virus data acquired from the storage device 53 of the testing company terminal 51 and the positive case data 632 acquired from the storage device 63 of the administrative agency 6, and predicts the trend of increase or decrease in the number of infected people after the day the sewage sample is collected based on the calculated ratio. Furthermore, the transmission processing unit 252 of the prediction management device 21 transmits information regarding the trend of increase or decrease in the number of infected persons of infectious diseases predicted by the prediction unit 251, i.e., the increase / decrease trend data 232 stored in the storage device 23, to the administrative agency terminal 61 via the communication unit 24 and the network 9. As a result, the prediction management system and prediction management program according to this embodiment can more reliably predict the trend of increase or decrease in the number of infected persons of infectious diseases.

[0083] Furthermore, the notification processing unit 653 of the administrative agency terminal 61 notifies the user terminals 8 of residents near the sewage sample collection site of the trend in the number of infected persons of the infectious disease, based on information regarding the trend in the number of infected persons of the infectious disease received from the predictive management device 21 via the communication unit 24 and the network 9. Alternatively, the notification processing unit 253 of the predictive management device 21 notifies the user terminals 8 of residents near the sewage sample collection site of the trend in the number of infected persons of the infectious disease, based on information regarding the trend in the number of infected persons of the infectious disease predicted by the prediction unit 251 of the predictive management device 21, i.e., the trend in the number of infected persons 232 stored in the storage device 23, via the communication unit 24 and the network 9. This allows residents near the sewage sample collection site to understand the trend in the number of infected persons of the infectious disease in the vicinity of the sewage sample collection site by checking the information notified to their user terminal 8.

[0084] Furthermore, if the trend of increase or decrease in the number of infected individuals is categorized and displayed on the display unit 86 of the user terminal 8, residents near the sewage sample collection site can more easily grasp the trend of increase or decrease in the number of infected individuals in the vicinity of the sewage sample collection site.

[0085] Embodiments of the present invention have been described above. However, the present invention is not limited to the above embodiments, and various modifications can be made without departing from the scope of the claims. The configurations of the above embodiments can be partially omitted or combined in any way different from those described above. [Explanation of Symbols]

[0086] 2: Management company, 3: Sampling company, 4: Sewage treatment plant, 5: Testing company, 6: Government agency, 7: Medical institution, 8: User terminal, 9: Network, 21: Predictive management device, 22: Computer, 23: Storage device, 24: Communication unit, 25: Control unit, 51: Testing company terminal, 52: Computer, 53: Storage device, 54: Communication unit, 55: Control unit, 56: Display unit, 61: Government agency terminal, 62: Computer, 63: Storage device, 64: Communication unit, 65: Control unit, 66: Display unit, 71: Medical institution terminal, 72: Computer, 73: Storage device, 74: Communication unit, 75: Control unit, 76: Display unit, 82: Computer, 83: Storage device, 84: Communication unit, 85: Control unit, 86: Display unit, 231: Program, 232: Increase / decrease trend data, 251: Prediction unit, 252: Transmission processing unit, 253: Notification processing unit, 531: Program, 532: Pathogen data, 631: Program, 632: Number of positive cases data, 653: Notification processing unit, 731: Program, 732: Number of positive cases data, 753: Notification processing unit

Claims

1. A predictive management system that predicts the trend of increase or decrease in the number of people infected with infectious diseases, A pathogen data storage device that stores the date of collection of a sewage sample and pathogen data relating to the pathogen of the infectious disease contained in the sample, obtained by analyzing the sample, in correspondence with each other. A data storage device for the number of positive cases that stores a date and the number of positive cases of the infectious disease on that date in correspondence with each other. A predictive management device is connected to the pathogen data storage device and the positive case count data storage device in a communicative manner, and acquires the pathogen data on the collection date from the pathogen data storage device and the positive case count data relating to the number of positive cases for a predetermined period prior to the collection date from the positive case count data storage device, predicts the increase or decrease trend after the collection date based on the ratio of the acquired pathogen data and the acquired positive case count data, and transmits information regarding the predicted increase or decrease trend. A predictive management system characterized by having the following features.

2. An institutional terminal used by an institution that collects the aforementioned positive case data and has a storage device for the aforementioned positive case data, User terminals used by residents, Furthermore, The pathogen data storage device further stores the sample collection location in association with the collection date and the pathogen data. The predictive management device transmits information regarding the increase / decrease trend to the agency terminal. The predictive management system according to claim 1, characterized in that the institutional terminal notifies the user terminals of the residents in the vicinity of the sampling location of the increase / decrease trend based on the information on the increase / decrease trend received from the predictive management device.

3. User terminals used by residents, Furthermore, The pathogen data storage device further stores the sample collection location in association with the collection date and the pathogen data. The predictive management system according to claim 1, characterized in that the predictive management device notifies the user terminals of the residents near the sampling location of the increase or decrease trend based on the information regarding the increase or decrease trend.

4. The prediction management device is characterized by classifying the increase / decrease trends according to the ratio and transmitting information regarding the classified increase / decrease trends, as described in claim 1.

5. The predictive management device is characterized in that it predicts the increase or decrease trend based on the ratio at which the probability of the number of positive cases one week after a predetermined date increasing relative to the number of positive cases on a predetermined date is greater than or equal to a predetermined probability.

6. The predictive management system according to claim 1, characterized in that the pathogen data is the concentration of the pathogen obtained by analyzing the sample of sewage flowing through the sewage pipes of a separate sewer system.

7. The predictive management system according to claim 1, characterized in that the pathogen data is the amount of pathogen load calculated by multiplying the concentration of the pathogen obtained by analyzing the sample of sewage flowing through the sewage pipe of a separate sewer system by the flow rate of the sewage.

8. The predictive management system according to claim 1, characterized in that the pathogen data is the concentration of the pathogen obtained by analyzing the sample of sewage flowing through the combined pipe of a combined sewer system.

9. The predictive management system according to claim 1, characterized in that the pathogen data is the amount of pathogen load calculated by multiplying the concentration of the pathogen obtained by analyzing the sample of sewage flowing through the combined pipe of a combined sewer system by the flow rate of the sewage.

10. The predictive management system according to claim 1, characterized in that the positive case data is the total or average value of the number of positive cases during the predetermined period.

11. The predictive management system according to claim 1, characterized in that the positive case data is the total or average value of the number of hospitalized patients among the positive cases during the predetermined period.

12. The predictive management system according to claim 1, characterized in that the positive case data is the total or average value of the number of deaths among the positive cases during the predetermined period.

13. A predictive management program is executed by a computer of a predictive management device that predicts the trend of increase or decrease in the number of infected persons of the infectious disease, and is communicably connected to a pathogen data storage device that stores, in correspondence, the date on which a sewage sample is collected and pathogen data relating to the infectious disease pathogens contained in the sample obtained by analyzing the sample, and a positive case count data storage device that stores, in correspondence, the date and the number of positive cases of the infectious disease on the date, and predictive management program is executed by the computer of the predictive management device that predicts the trend of increase or decrease in the number of infected persons of the infectious disease. To the aforementioned computer, A first acquisition step involves acquiring the pathogen data on the aforementioned collection date from the pathogen data storage device, The second step involves acquiring data on the number of positive cases of the infectious disease during a predetermined period prior to the aforementioned collection date from the positive case data storage device, Based on the ratio of the pathogen data obtained in the first acquisition step and the number of positive cases data obtained in the second acquisition step, the process predicts the trend of increase or decrease after the collection date. A transmission step that transmits information regarding the predicted increase or decrease trend, A predictive management program characterized by its ability to execute the following actions.

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