Prediction management system and prediction management program

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 predictive management system and a predictive management program that predict the increase or decrease in the number of people infected with an infectious disease. [Background technology]

[0002] In order to prevent the spread of infectious diseases such as the new coronavirus, PCR tests, antigen tests, and other infectious disease tests are conducted on people who show symptoms such as fever. If a positive reaction is found in an infectious disease test, measures such as isolating the positive person and close contacts are taken. However, some positive people are asymptomatic, and some people do not undergo infectious disease testing even if they show symptoms because they are mild, making it difficult to predict the increase or decrease in the number of infected people.

[0003] Patent Document 1 discloses an infectious disease management system including a collection result database, an analysis result database, a visitor database, and a computer program that can access each database. The computer program described in Patent Document 1 is capable of executing a step of extracting a list of visitors whose period of stay at the facility overlaps with the collection period based on the data stored in each database, when information regarding the presence of infectious pathogens in samples collected from sewage during a collection period along the path of sewage discharged from a facility until it flows into a public sewer pipe indicates that there may be an infectious pathogen among visitors at the facility at least during the collection period.

[0004] However, while the infectious disease management system described in Patent Document 1 can indicate the possibility that there is an infectious pathogen among the facility's residents and extract a list of residents whose collection period overlaps with their stay at the facility, it is difficult to predict the increase or decrease in the number of infectious disease residents. In other words, there is a difference in the time when the analysis results regarding the presence of infectious pathogens in samples collected from sewage and the tabulated results regarding the number of positive cases tabulated through health administration are known, making it difficult to predict the increase or decrease in the number of infectious disease residents.

[0005] Alternatively, an analysis (e.g., PCR analysis) regarding the presence of infectious pathogens in a sample collected from sewage 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 contracting company, which then analyzes the samples submitted by the collection company to obtain information regarding the presence of infectious pathogens in the samples. Therefore, information regarding the presence of infectious pathogens in the samples is not closely continuous in time, making it difficult to predict the increase or decrease in the number of people infected with an infectious disease. Against this background, there is a demand for predicting the increase or decrease in the number of people infected with an infectious disease such as the new coronavirus. [Prior art documents] [Patent documents]

[0006] [Patent Document 1] Patent No. 7031957 Summary of the Invention [Problem to be solved by the invention]

[0007] The present invention has been made in consideration of the above circumstances, and aims to provide a predictive management system and a predictive management program that can predict the increase / decrease in the number of people infected with an infectious disease. [Means for solving the problem]

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

[0009] A second aspect of the present invention is a predictive management program executed by a computer of a predictive management device that is communicatively connected to a pathogen data storage device that stores the collection date of a sewage sample and pathogen data regarding the pathogen of an infectious disease obtained by analyzing the sample and contained in the sample, in correspondence with each other, and a positive case number data storage device that stores a date and a positive case number data regarding the number of positive cases of the infectious disease on the date, in correspondence with each other, and predicts a trend of increase or decrease in the number of infected people of the infectious disease, characterized in that it causes the computer to execute 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 case number data regarding the number of positive cases of the infectious disease in a predetermined period prior to the collection date from the positive case number data storage device, a predicting step of predicting the trend of increase or decrease after the collection date based on the ratio between the pathogen data acquired in the first acquisition step and the positive case number data acquired in the second acquisition step, and a transmission step of transmitting information regarding the predicted trend of increase or decrease. Effect 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 increase / decrease trend in the number of people infected with an infectious disease. [Brief description of the drawings]

[0011] [Figure 1] FIG. 1 is a schematic diagram illustrating an overview of a prediction management system according to an embodiment of the present invention. [Diagram 2] FIG. 2 is a block diagram showing the main hardware configuration of the prediction management system according to the present embodiment. [Diagram 3] 1 is a block diagram showing a configuration of a main part of a prediction management device according to an embodiment of the present invention. [Figure 4] FIG. 2 is a block diagram showing a configuration of a main part of an inspection company terminal according to the present embodiment. [Diagram 5] FIG. 2 is a block diagram showing the configuration of the main parts of an administrative agency terminal and a medical institution terminal according to the present embodiment. [Figure 6] 2 is a block diagram showing the main configuration of a user terminal according to the present embodiment. FIG. [Figure 7] FIG. 1 is a schematic diagram illustrating the relationship between virus concentration and the number of positive cases in this embodiment. [Figure 8] 1 is a graph showing an example of the relationship between the number of positive cases, virus concentration, and the seven-interval moving average number of positive cases in a given region. [Figure 9] FIG. 1 is a schematic diagram explaining the ratio of virus data to positive case number data. [Figure 10] 1 is a sequence chart illustrating a first specific example of a processing procedure of a prediction management system according to an embodiment of the present invention. [Figure 11] 13 is a sequence chart illustrating a second specific example of the processing procedure of the prediction management system according to the present embodiment. [Figure 12] 11 is a graph illustrating a first specific example of a method for calculating a predetermined probability (threshold value). [Figure 13] 13 is a graph illustrating a second specific example of a method for calculating a predetermined probability (threshold value). DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0012] Hereinafter, an embodiment of the present invention will be described with reference to the drawings. The embodiments described below are preferred specific examples of the present invention, and therefore various technically preferable limitations are attached to them. However, the scope of the present invention is not limited to these aspects unless otherwise specified in the following description to the effect that the present invention is limited to these aspects. For example, in the embodiments described below, the novel coronavirus is given as a specific example of a pathogen, but the pathogen is not limited to the novel coronavirus and may be other viruses such as influenza virus and norovirus. Furthermore, in each drawing, similar components are given the same reference numerals and detailed descriptions are omitted as appropriate.

[0013] FIG. 1 is a schematic diagram illustrating an overview of a prediction management system according to an embodiment of the present invention. First, an overview of the prediction management system according to this embodiment will be described with reference to FIG. 1. The prediction management system according to this embodiment is a system for predicting the trend of increase and decrease in the number of infected people of an infectious disease. In this specification, the term "infected person" refers to a person infected with a pathogen (i.e., a person whose body has been invaded by a pathogen), and includes not only a person who has tested positive for an infectious disease (i.e., a positive person), but also a person infected with a pathogen but has not been tested for an infectious disease (i.e., an untested infected person). In this specification, the term "prediction" includes not only a prediction of predicting the course or outcome of an event in advance, but also a foresight of foreseeing an event before it occurs. In the prediction management system shown in FIG. 1, a management company 2, a collection company 3, a sewage treatment plant 4, a testing company 5, an administrative agency 6 such as a city, ward, town, or village office, and a medical institution 7 such as a hospital or a health department of a local government are mutually related. As shown in the two-dot chain line frame in FIG. 1, the testing company 5 may be included in the management company 2. In other words, the testing company 5 may be the same company as the management company 2.

[0014] First, as indicated by arrows A1 and A2 in Fig. 1, the management company 2 receives a request from at least one of an administrative agency 6 and a medical institution 7 to predict the trend of increase or decrease in the number of people infected with an infectious disease. Each of the administrative agency 6 and the medical institution 7 in this embodiment is an example of an "agency" in the present invention.

[0015] Next, as indicated by arrow A3 in Fig. 1, the management company 2 requests the sampling company 3 to collect a sewage sample. The collection of the sewage sample may be performed by the management company 2. In this specification, for convenience of explanation, the sewage sample is also simply referred to as a "sewage sample."

[0016] Next, as indicated by arrow A4 in FIG. 1, the sampling company 3 receives a request from the management company 2 and heads to the sewage treatment plant 4 to collect a sewage sample. For example, the sampling company 3 collects a sample of sewage flowing through a wastewater pipe of a separate sewer system. Alternatively, for example, the sampling company 3 collects a sample of sewage flowing through a combined pipe of a combined sewer system. Alternatively, for example, the sampling company 3 collects a sample of sewage flowing through the inflow channel of the primary sedimentation tank of the sewage treatment plant 4. Next, the sampling company 3 provides the collected sewage sample to the inspection company 5 together with the date (i.e., collection date) and place (i.e., collection place) that the sewage sample was collected.

[0017] Next, the testing company 5 analyzes the sewage sample provided by the sampling company 3 by PCR testing or the like, and obtains pathogen data related to infectious disease pathogens contained in the sewage sample. The testing company 5 then 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 of obtaining the pathogen data may be, for example, a method of obtaining temporally discontinuous data such as PCR testing, or may be, for example, a method of obtaining temporally closely continuous data such as testing using a sensor that utilizes an antigen-antibody reaction.

[0018] As described above, the testing company 5 may be included in the management company 2. In this case, the management company 2 analyzes the sewage samples provided by the collection company 3 by PCR testing or the like, and obtains pathogen data on infectious disease pathogens contained in the sewage samples. The management company 2 then 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.

[0019] Meanwhile, the administrative agency 6 collects data on the number of people who have tested positive for an infectious disease, and stores the date and the data on the number of people who have tested positive for that date in association with each other. For the sake of convenience, the data on the number of people who have tested positive will be referred to simply as "data on the number of people who have tested positive" or "number of people who have tested positive." In addition, the medical institution 7 may collect data on the number of people who have tested positive for an infectious disease, and store the date and the data on the number of people who have tested positive for an infectious disease in association with each other.

[0020] Next, as indicated by arrow A5 in FIG. 1, management company 2 obtains pathogen data on the date the sewage sample was collected from testing company 5. Also, as indicated by arrows A1 and A2 in FIG. 1, management company 2 obtains data on the number of positive cases related to the number of positive cases in a predetermined period prior to the date the sewage sample was collected from at least one of administrative agencies 6 and medical institutions 7. Then, management company 2 predicts the increase / decrease trend in the number of people infected with the infectious disease after the date the sewage sample was collected based on the ratio between the pathogen data obtained from testing company 5 and the data on the number of positive cases obtained from at least one of administrative agencies 6 and medical institutions 7. Details of the method for predicting the increase / decrease trend will be described later.

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

[0022] Next, as indicated by arrows A11 and A12 in Fig. 1, at least one of the administrative agency 6 and the medical institution 7 notifies user terminals 8 of residents living near the location where the sewage sample was collected of the increase / decrease in the number of infected people, based on the information on the increase / decrease in the number of people infected with the infectious disease received from the management company 2. Examples of residents living near the location where the sewage sample was collected include residents living in an administrative district that includes the service area of ​​the public sewerage system and sewage treatment plant 4 from which the collection company 3 collected the sewage sample. The user terminals 8 are portable terminal devices, such as smartphones, tablet computers, and mobile phones, used by residents (i.e., users).

[0023] As shown by arrow A13 in Figure 1, the management company 2 may notify user terminals 8 of residents living near the location where the sewage sample was collected of the trend in the increase / decrease in the number of infected people of an infectious disease, based on information regarding the trend in the increase / decrease in the number of infected people predicted by the management company 2 itself.

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

[0025] The prediction management system mainly includes a prediction management device 21, a testing company terminal 51, at least one of an administrative agency terminal 61 and a medical institution terminal 71, and a user terminal 8. The number of user terminals 8 is not limited to one, and may be multiple. In FIG. 2, one user terminal 8 is shown.

[0026] The prediction management device 21 is a device owned by and used by the management company 2, and has 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 FIG. 3) and executes various calculations and processes. In this specification, the term "computer" is not limited to a personal computer, but also includes a calculation processing unit, a microcomputer, 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 a program.

[0027] The inspection company terminal 51 is a terminal owned by and used by the inspection company 5, and has 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 FIG. 4) and executes various calculations and processes. As described 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 by and used by the management company 2, and may be integrated with the prediction management device 21.

[0028] The administrative agency terminal 61 is a terminal owned by the administrative agency 6 and used by the administrative agency 6, and has 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 FIG. 5) and executes various calculations and processes. The medical institution terminal 71 is a terminal owned by the medical institution 7 and used by the medical institution 7, and has 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 FIG. 5) and executes various calculations and processes. Each of the administrative agency terminal 61 and the medical institution terminal 71 is an example of the "agency terminal" of the present invention.

[0029] The user terminal 8 is a portable terminal device such as a smartphone, tablet computer, or mobile phone used by a resident (i.e., a user), 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 includes a control unit 85 (see FIG. 6) and executes various calculations and processes. The user terminal 8 may have a display including a touch panel capable of detecting, for example, the touch of a user's finger. The user terminal 8 may also have a function of detecting the user's current location (the location where the user terminal 8 is located). The user's current location can be detected, for example, by a Global Positioning System (GPS).

[0030] As shown in Figure 2, the predictive management device 21, the testing company terminal 51, the administrative agency terminal 61, the medical institution terminal 71, and the user terminal 8 are connected to each other so as to be able to communicate with each other via a network 9, such as the Internet, by means of their respective communication units 24, 54, 64, 74, and 84.

[0031] FIG. 3 is a block diagram showing the configuration of a main part of the prediction management device of this embodiment. As shown in FIG. 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 out the program 231 stored in the storage device 23 to execute various calculations and processes. The control unit 25 has a prediction unit 251, a transmission processing unit 252, and a notification processing unit 253. Note that the control unit 25 does not necessarily have to have the 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 FIG. 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 may be realized by a combination of hardware and software.

[0033] The prediction unit 251 predicts the increase / decrease trend of the number of people infected with an infectious disease after the date of collection of the sewage sample based on the ratio of the pathogen data 532 (see FIG. 4) acquired from the storage device 53 of the testing company terminal 51 via the communication unit 24 and the network 9 to the positive case count data 632, 732 (see FIG. 5) acquired from at least one of the storage device 63 of the administrative agency 6 and the storage device 73 of the medical institution 7 via the communication unit 24 and the network 9. Then, the prediction unit 251 stores the predicted increase / decrease trend as increase / decrease trend data 232 in the storage device 23. Details of the method by which the prediction unit 251 predicts the increase / decrease trend of the number of people infected with an infectious disease will be described later.

[0034] The transmission processing unit 252 transmits information regarding the increase / decrease trend in the number of people infected with the infectious disease predicted by the prediction unit 251, i.e., the increase / decrease trend data 232 stored in the memory 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 the user terminals 8 of residents living near the location where the sewage sample was collected of the increase / decrease trend in the number of infected people via the communication unit 24 and the network 9 based on information regarding the increase / decrease trend in the number of infected people of the infectious disease predicted by the prediction unit 251, i.e., the increase / decrease trend data 232 stored in the memory device 23.

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

[0037] The program 231 includes a calculation program for predicting the increase or decrease in the number of infected people of an infectious disease. The program 231 is not limited to being stored in the storage device 23, and may be stored in advance in a computer-readable storage medium and distributed, or may be downloaded to the prediction management device 21 via the network 9.

[0038] FIG. 4 is a block diagram showing the configuration of the main part of the inspection company terminal of this embodiment. 4, the inspection company terminal 51 includes a control unit 55, a storage device 53, a communication unit 54, and a display unit 56. It is to be noted that the inspection company terminal 51 does not necessarily have to include the display unit 56.

[0039] The control unit 55 is, for example, a CPU, and executes various calculations and processes by reading out a program 531 stored in the storage device 53. The control unit 55 stores in the storage device 53 pathogen data 532 acquired by a PCR test or the like, the collection date of the sewage sample, and the collection location of the sewage sample in association with each other.

[0040] The storage device 53 stores a program 531 and pathogen data 532. The storage device 53 of this embodiment is an example of the "pathogen data storage device" of the present invention. Examples of the storage device 53 include a semiconductor memory or a 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 an arithmetic program for analyzing and processing the pathogen data 532, and the like.

[0041] The pathogen data 532 is data obtained by analyzing a sewage sample, and is data related to infectious disease pathogens contained in the sewage sample. The pathogen data 532 may be the data obtained by analyzing the sewage sample as is, or may be data obtained by performing a predetermined process on the data obtained by analyzing the sewage sample.

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

[0043] As described above, the pathogen data 532 may be data obtained by performing a predetermined process on data obtained by analyzing a sewage sample. The pathogen data 532 stored in the storage device 53 is, for example, a pathogen load calculated by multiplying a pathogen concentration obtained by analyzing a sewage sample flowing through a sewage 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, a pathogen load calculated by multiplying a pathogen concentration obtained by analyzing a sewage sample flowing through a combined 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, contact with a human finger. In this case, the worker or the like can input various information such as necessary information by operating the display unit 56.

[0045] FIG. 5 is a block diagram showing the configuration of the main parts of the administrative agency terminal and the medical institution terminal of 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 described by taking the administrative agency terminal 61 as an example.

[0046] 5, the administrative agency terminal 61 has a control unit 65, a storage device 63, a communication unit 64, and a display unit 66. It is to be noted that the administrative agency terminal 61 does not necessarily have to have the display unit 66.

[0047] The control unit 65 is, for example, a CPU, and reads out the program 631 stored in the storage device 63 to execute 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 FIG. 2) executing the program 631 stored in the storage device 63. The notification processing unit 653 may be realized by hardware, or may be realized by a combination of hardware and software.

[0048] The control unit 65 stores in the storage device 63 the date and the data 632 on the number of positive cases of an infectious disease on that date in association with each other. The notification processing unit 653 notifies user terminals 8 of residents living in the vicinity of the sewage sample collection location of the increase / decrease in the number of infected people via the communication unit 24 and the network 9, based on information regarding the increase / decrease in the number of infected people of an infectious disease received from the predictive management device 21 via the communication unit 24 and the network 9.

[0049] The storage device 63 stores a program 631 and data 632 on the number of positive cases. The storage device 63 of this embodiment is an example of a "data storage device for the number of positive cases" of the present invention. Examples of the storage device 63 include a semiconductor memory or a 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 an arithmetic program for collecting and processing data 632 on the number of positive cases.

[0050] The positive case number data 632 is data on the number of positive cases of an infectious disease collected by the administrative agency 6. The positive case number data 632 may be collected data as is, or may be collected data subjected to a predetermined process. The positive case number data 632 stored in the storage device 63 is, for example, a total or average value of the number of positive cases in a predetermined period before the date on which a sewage sample was collected. The "predetermined period" here is, for example, about one week. Alternatively, the positive case number data 632 stored in the storage device 63 is, for example, a total or average value of the number of hospitalized cases among the number of positive cases in a predetermined period before the date on which a sewage sample was collected. Alternatively, the positive case number data 632 stored in the storage device 63 is, for example, a total or average value of the number of deaths among the number of positive cases in a predetermined period before the date on which a 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, contact with a human finger. In this case, the worker or the like can input various information such as necessary information by operating the display unit 66.

[0052] Like the administrative agency terminal 61 described above with reference to Fig. 5, the medical institution terminal 71 has a control unit 75, a storage device 73, a communication unit 74, and a display unit 76. It should be noted that the medical institution terminal 71 does not necessarily have to have the display unit 76. The control unit 75 has a notification processing unit 753. The storage device 73 is an example of the "data storage device for the number of positive cases" of the present invention, and stores a program 731 and data 732 on the number of positive cases.

[0053] FIG. 6 is a block diagram showing the main configuration of a user terminal of this embodiment. As shown in FIG. 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 executes various calculations and processes by reading out a program (not shown) stored in the storage device 83. For example, the control unit 85 executes a process of receiving a notification regarding the increase / decrease trend in the number of people infected with an infectious disease from at least one of the administrative agency terminal 61, the medical institution terminal 71, and the prediction management device 21 via the communication unit 24 and the network 9, and displaying the notification regarding the increase / decrease trend in the number of people infected with an infectious disease 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, for example, a display including a touch panel capable of detecting touch by a human finger. In this case, the user can input various information such as necessary information by operating the display unit 86.

[0056] Next, a method for predicting the increase / decrease trend in the number of people infected with an infectious disease by the prediction management device 21 of this embodiment will be described with reference to the drawings. In the following description, an example will be taken in which the pathogen is a virus. For convenience of explanation, in this specification, data on viruses contained in sewage samples will also be referred to simply as "virus data." Here, the virus data is an example of the "pathogen data" of the present invention, and is the concentration of viruses obtained by analyzing sewage samples in the combined pipe of a combined sewer system or the sewage pipe of a separate sewer system. For convenience of explanation, in this specification, the concentration of the virus will also be referred to simply as "virus concentration."

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

[0058] For example, in order to prevent the spread of infectious diseases such as the new coronavirus, infectious disease tests such as PCR tests and antigen tests are conducted on people who show symptoms such as fever. If a positive reaction is found in an infectious disease test, measures such as isolating the positive person and close contacts are taken. However, some positive people are asymptomatic, and some people do not undergo infectious disease tests even if they show symptoms because they are mild, making it difficult to predict the increase or decrease in the number of infected people.

[0059] Therefore, the inventors attempted to collect samples of general sewage, such as domestic wastewater, from public sewerage systems and sewage treatment plants 4, and use data on viruses contained in the samples to predict the increase / decrease in the number of people infected with infectious diseases in administrative districts that include the areas served by the public sewerage systems and sewage treatment plants 4.

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

[0061] FIG. 8 is a graph summarizing the number of positive cases collected by the inventor in a certain administrative district, the most recent seven-section moving average number of positive cases related to the number of positive cases, the virus concentration in the combined sewer system (system 1) included in the administrative district, and the virus concentration in the separate sewer system (system 2) also included in the administrative district. According to FIG. 8, the peaks on the vertical axis of the graph after January 6, when the number of positive cases began to increase, are first the virus concentration in system 2 on February 24, then the virus concentration in system 1 on March 3, and finally the seven-section moving average number of positive cases on March 5. In the subsequent increase in the number of positive cases after May 5, the peaks on the vertical axis of the graph are first the virus concentration in system 1 on May 12, then the virus concentration in system 2 on May 19, and finally the seven-section moving average number of positive cases on May 26. In other words, when the virus concentration on the vertical axis of the graph peaks, system 1 may be first, or system 2 may be first. However, the seven-section moving average number of positive cases is the last one both after January 6 and after May 5. This also shows that the increase / decrease trend in the number of positive cases data lags behind the increase / decrease trend in the virus data (i.e., the virus concentration in Figure 8).

[0062] In this way, there is a time difference between the increase / decrease trend of virus data and the increase / decrease trend of positive case data. Therefore, it is difficult to predict the increase / decrease trend of the number of infected people of an infectious disease by simply obtaining data on the virus contained in a sewage sample. For example, as shown in FIG. 7, for a certain value W of the concentration of the virus contained in a sewage sample (i.e., the virus concentration), there are two values: virus concentration W1 in a first period T1 when the virus is increasing, and virus concentration W2 in a second period T2 when the virus is decreasing. Therefore, simply obtaining data on the virus contained in a sewage sample does not tell whether the data is from a period when the virus is increasing or decreasing. Therefore, it is difficult to predict the increase / decrease trend of the number of infected people of an infectious disease.

[0063] Furthermore, as shown in FIG. 8, for example, when a sewage sample is analyzed by a PCR test or the like to obtain a virus concentration, the analysis by the PCR test or the like is not a continuous analysis. That is, in the example of FIG. 8, a collection company 3 collects a sewage sample from a public sewer system and a sewage treatment plant 4, etc., and delivers it to another location, such as a testing company 5. Then, the testing company 5 analyzes the sewage sample to obtain the virus concentration. Therefore, the obtained virus concentration is not a closely continuous data in terms of time. Specifically, the analysis of the sewage sample to obtain the virus concentration is performed once a week for both the combined sewer system (system 1) and the separate sewer system (system 2). Therefore, for example, whether the virus concentration obtained today is on the rise or on the fall cannot be accurately determined until the virus concentration is obtained one week later. In this regard, for example, the virus concentration (system 2) in FIG. 8 had a small peak once on February 3, then had a decreasing trend until February 17, but had a very large peak the following week on February 24. According to this example, the virus concentration (2 system) as of February 17th has been on a downward trend since February 3rd in the past, and if the next week were to be judged based on the increase / decrease trend from the past to the present, the virus concentration on February 24th the following week should have been lower than the current level (February 17th). However, this was not the case, and the virus concentration (2 system) became very high. When continuous analysis is not used as in this example, it is difficult to predict the increase / decrease trend in the number of infected people of an infectious disease.

[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 increase / decrease trend of the virus data and the increase / decrease trend of the positive case number data, and predicts the increase / decrease trend of the number of infected people of an infectious disease after the date of collection of a sewage sample based on the ratio of the virus data to the positive case number data. To explain this specifically with reference to FIG. 7, the ratio (W1 / C1) of the virus concentration W1 to the positive case number C1 in the first time period T1 is greater than the ratio (W2 / C2) of the virus concentration W2 to the positive case number C2 in the second time period T2. Here, the virus concentration W1 is equal to the virus concentration W2. In this specification, the ratio of virus data to the number of positive cases data is expressed as the virus concentration W relative to the number of positive cases C (i.e., W / C), but this is not limited to this and may be the opposite (i.e., C / W).

[0065] The prediction unit 251 of the predictive management device 21 of this embodiment utilizes such a property, and predicts that, for example, in the third period T3 where the ratio (W / C) between the virus concentration W and the number of positive cases C is relatively high, as shown in FIG. 9, the number of infected people may increase in the future, or the proportion of infected people who have not been identified is high. On the other hand, the prediction unit 251 of the predictive management device 21 of this embodiment predicts that, for example, in the fourth period T4 where the ratio (W / C) between the virus concentration W and the number of positive cases C is relatively low, the number of infected people may decrease in the future, or the proportion of infected people who have not been identified is low. Alternatively, the prediction unit 251 of the predictive management device 21 of this embodiment sets a threshold value for the ratio (W / C) that distinguishes between periods when the number of infected people is increasing and periods when the number of infected people is decreasing based on past statistics, and predicts the increase / decrease tendency of the number of infected people by comparing the ratio (W / C) between the virus concentration W and the number of positive cases C on a specified day with the threshold value.

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

[0067] 10, in step S11, the testing company terminal 51 associates the virus data acquired by PCR testing or the like (i.e., pathogen data 532), the collection date of the sewage sample, and the collection location of the sewage sample with each other and stores them in the storage device 53. Meanwhile, in step S12, the administrative agency terminal 61 collects data on the number of people who tested positive for an infectious disease, and stores in the storage device 63 the date and the number of people who tested positive for the infectious disease on that date in association with each other.

[0068] Note that step S11 may be executed before step S12 or after step S12. Also, step S11 and step S12 may be executed in parallel with each other. Step S11 and step S12 are executed independently of each other.

[0069] Next, in step S13, the predictive management device 21 acquires virus data on the date 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 count data 632 regarding the number of positive cases in a predetermined period before the date 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) between the virus concentration W and 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 data 632 of the number of positive cases acquired from the storage device 63 of the administrative agency 6. Then, the prediction unit 251 of the prediction management device 21 predicts the tendency of increase or decrease in the number of infected people after the date of collection of the sewage sample based on the calculated ratio. 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 tendency of increase or decrease in the number of infected people based on the ratio when the probability of an increase in the data 632 of the number of positive cases one week after the specified date relative to the data 632 of the number of positive cases on a specified date is equal to or greater than a specified probability (threshold). Hereinafter, an example of a method of calculating the specified probability (threshold) will be described with reference to the drawings.

[0071] FIG. 12 is a graph showing an example of the relationship between the increase rate of the number of positive PCR test cases (i.e., the number of positive PCR test cases shown on the horizontal axis of FIG. 12) the following week and the ratio of the virus concentration W to the number of positive test cases C (i.e., the W / C ratio shown on the vertical axis on the left side of FIG. 12). The W / C ratio shown on the vertical axis on the right side of FIG. 12 is the ratio of the virus load W calculated by multiplying the virus concentration by the sewage flow rate to the number of positive test cases C (the same applies to FIG. 13 described below). When the value on the horizontal axis shown in FIG. 12 (the increase rate of the number of positive PCR test cases the following week) is greater than "1.00", the number of positive test cases one week after the specified date increases relative to the number of positive test cases on a given day. When the value on the horizontal axis shown in FIG. 12 is less than "1.00", the number of positive test cases one week after the specified date decreases relative to the number of positive test cases on a given day.

[0072] Here, if we focus on the ratio of the virus concentration W of the sewage sample flowing through the combined sewer pipe of a combined sewer system to the number of positive cases C (W / C ratio; 1-system concentration), when the W / C ratio (virus concentration) shown on the vertical axis on the left side of Figure 12 is 400, of the total number of data points (5) with a W / C ratio of 400 or more, the number of data points whose value on the horizontal axis shown in Figure 12 (the increase rate of the number of positive cases in PCR tests the following week) is greater than "1.00" is 4, and the number of data points whose value on the horizontal axis shown in Figure 12 is less than "1.00" is 1.

[0073] Therefore, in step S15, the prediction unit 251 of the prediction management device 21 sets, for example, "400" as a threshold value of the W / C ratio at which the probability that the positive case count data one week after the specified day increases with respect to the positive case count data on the specified day is 80% (=4 / 5×100%) or more. Then, the prediction unit 251 of the prediction management device 21 predicts that the number of infected people is on the rise when the ratio (W / C ratio; 1-system concentration) between the virus concentration W and the positive case count C on the specified 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 the decline when the ratio (W / C ratio; 1-system concentration) between the virus concentration W and the positive case count C on the specified day is less than 400. However, "400" is an example of a threshold value of the W / C ratio, and the threshold value of the W / C ratio is not limited to only "400".

[0074] 13 is a graph showing an example of the relationship between the rate of increase in the number of positive PCR test cases the following week and the ratio (W / C ratio) between the virus concentration W and the number of positive PCR test cases C, similar to the graph described above with reference to FIG. 12. The values ​​on the horizontal axis shown in FIG. 13 (rate of increase in the number of positive PCR test cases the following week) are as described above with reference to FIG.

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

[0076] Therefore, in step S15, the prediction unit 251 of the prediction management device 21 sets, for example, "400" as a threshold value of the W / C ratio at which the probability that the positive case count data one week after the specified day increases with respect to the positive case count data on the specified day is 75% (=9 / 12×100%) or more. Then, the prediction unit 251 of the prediction management device 21 predicts that the number of infected people is on the rise when the ratio of the virus concentration W to the number of positive cases C on the specified day (W / C ratio; 2-system concentration) 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 the decline when the ratio of the virus concentration W to the number of positive cases C on the specified day (W / C ratio; 2-system concentration) is less than 400. However, as described above with reference to FIG. 12, "400" is an example of the threshold value of the W / C ratio, and the threshold value of the W / C ratio is not limited to only "400".

[0077] In step S16 following step S15, the transmission processing unit 252 of the prediction management device 21 transmits information on the increase / decrease trend of the number of infected people 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 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 / decrease trend of the number of infected people of the infectious disease according to the W / C ratio, and transmits information on the classified increase / decrease trend to the administrative agency terminal 61.

[0078] For example, the transmission processing unit 252 of the prediction management device 21 classifies the increase / decrease trend of the number of infected persons of an infectious disease into three categories according to the W / C ratio, and transmits information to the administrative agency terminal 61 in which the increase / decrease trend is marked with red when the W / C ratio is relatively high, transmits information to the administrative agency terminal 61 in which the increase / decrease trend is marked with yellow when the W / C ratio is relatively medium, and transmits information to the administrative agency terminal 61 in which the increase / decrease trend is marked with blue when the W / C ratio is relatively low. However, the number of categories into which the transmission processing unit 252 of the prediction management device 21 classifies the increase / decrease trend of the number of infected persons of an infectious disease is not limited to three, and may be two, four or more. Furthermore, the manner in which the transmission processing unit 252 of the prediction management device 21 classifies the increase / decrease trend of the number of infected persons of an infectious disease is not limited to color, and may be a category based on figures, patterns, or the like.

[0079] Next, in step S17, the notification processing unit 653 of the administrative agency terminal 61 notifies the user terminals 8 of residents living in the vicinity of the sewage sample collection location of the increase / decrease in the number of infected people via the communication unit 24 and the network 9 based on information regarding the increase / decrease in the number of infected people of the infectious disease received from the predictive management device 21 via the communication unit 24 and the network 9.

[0080] 11, the notification processing unit 253 of the prediction management device 21 may notify the user terminal 8 of the resident living near the location where the sewage sample was collected of the increase / decrease trend in the number of infected people via the communication unit 24 and the network 9, based on information relating to the increase / decrease trend in the number of infected people of an infectious disease predicted by the prediction unit 251 of the prediction management device 21, i.e., the increase / decrease trend data 232 stored in the storage device 23. Note that steps S12 to S26 shown in FIG. 11 are similar to steps S11 to S16 described above with reference to FIG. 10.

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

[0082] As described above, according to the predictive management system and predictive management program of this embodiment, the predictive management device 21 acquires virus data on 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. The predictive management device 21 also acquires positive case count data 632 relating to the number of positive cases in a predetermined period before 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. The prediction unit 251 of the predictive management device 21 then calculates the ratio (W / C) between the virus concentration W and 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 count data 632 acquired from the storage device 63 of the administrative agency 6, and predicts the increase / decrease trend in the number of infected people after the day the sewage sample was collected based on the calculated ratio. Furthermore, the transmission processing unit 252 of the prediction management device 21 transmits information relating to the increase / decrease trend in the number of people infected with an infectious disease 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. This allows the prediction management system and predictive management program according to this embodiment to more reliably predict the increase / decrease trend in the number of people infected with an infectious disease.

[0083] Further, the notification processing unit 653 of the administrative agency terminal 61 notifies the user terminal 8 of the resident living near the collection site of the sewage sample of the increase / decrease trend in the number of infected persons via the communication unit 24 and the network 9 based on the information on the increase / decrease trend in the number of infected persons of the infectious disease received from the prediction management device 21 via the communication unit 24 and the network 9. Alternatively, the notification processing unit 253 of the prediction management device 21 notifies the user terminal 8 of the resident living near the collection site of the sewage sample of the increase / decrease trend in the number of infected persons via the communication unit 24 and the network 9 based on the information on the increase / decrease trend in the number of infected persons of the infectious disease predicted by the prediction unit 251 of the prediction management device 21, i.e., the increase / decrease trend data 232 stored in the storage device 23. In this way, the resident living near the collection site of the sewage sample can grasp the increase / decrease trend in the number of infected persons of the infectious disease in the vicinity of the collection site of the sewage sample by checking the contents notified to the user terminal 8.

[0084] Furthermore, if the trends in the increase and decrease in the number of people infected with an infectious disease are categorized and displayed on the display unit 86 of the user terminal 8, residents living near the location where the sewage sample was collected can more easily understand the trends in the increase and decrease in the number of people infected with an infectious disease in the vicinity of the location where the sewage sample was collected.

[0085] The above describes the embodiments of the present invention. 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 arbitrarily combined in a manner different from the above. [Explanation of symbols]

[0086] 2: management company, 3: collection company, 4: sewage treatment plant, 5: inspection company, 6: government agency, 7: medical institution, 8: user terminal, 9: network, 21: prediction management device, 22: computer, 23: storage device, 24: communication unit, 25: control unit, 51: inspection 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: Positive number data, 653: Notification processing unit, 731: Program, 732: Positive number data, 753: Notification processing unit

Claims

1. A prediction management system that predicts the increase / decrease trend in the number of people infected with an infectious disease, a pathogen data storage device that stores a collection date of a sewage sample and pathogen data related to a pathogen of the infectious disease contained in the sample, the pathogen data being obtained by analyzing the sample and associated with each other; A data storage device for storing a date and data on the number of positive cases of the infectious disease on the date in association with each other; a prediction management device that is communicatively connected to the pathogen data storage device and the number of positive cases data storage device, acquires the pathogen data on the collection date from the pathogen data storage device, acquires data on the number of positive cases related to the number of positive cases during a predetermined period prior to the collection date from the number of positive cases data storage device, predicts the increase / decrease trend after the collection date based on a ratio between the acquired pathogen data and the acquired number of positive cases data, and transmits information related to the predicted increase / decrease trend; A prediction management system comprising:

2. An institution terminal used by an institution that collects the data on the number of positive cases and having the data storage device for the number of positive cases; A user terminal used by a resident; Further equipped with The pathogen data storage device further stores the location where the sample was collected in association with the collection date and the pathogen data; The prediction management device transmits information regarding the increase / decrease trend to the institution terminal, The prediction management system described in claim 1, characterized in that the institution terminal notifies the user terminal of the resident living in the vicinity of the collection site of the increase / decrease trend based on the information regarding the increase / decrease trend received from the prediction management device.

3. A user terminal used by a resident; Further equipped with The pathogen data storage device further stores the location where the sample was collected in association with the collection date and the pathogen data; The prediction management system according to claim 1 , wherein the prediction management device notifies the user terminal of the resident living near the collection site of the increase / decrease trend based on information about the increase / decrease trend.

4. The prediction management system according to claim 1 , wherein the prediction management device classifies the increase / decrease trends according to the ratio, and transmits information about the classified increase / decrease trends.

5. The predictive management system described in claim 1, characterized in that the predictive management device predicts the increase / decrease trend based on the ratio when the probability that the number of positive cases data one week after a specified date has increased compared to the number of positive cases data on the specified date is greater than or equal to a specified probability.

6. The predictive management system of claim 1 , wherein the pathogen data is a concentration of the pathogen obtained by analyzing the sample of the sewage flowing through a sewage pipe of a separate sewer system.

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

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

10. The prediction management system according to claim 1, characterized in that the data on the number of positive cases is a total or average value of the number of positive cases in the specified period.

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

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

13. A predictive management program executed by a computer of a predictive management device communicably connected to a pathogen data storage device that stores, in association with each other, a collection date of a sewage sample and pathogen data relating to an infectious disease pathogen contained in the sample, which is obtained by analyzing the sample, and a positive case number data storage device that stores, in association with each other, a date and positive case number data relating to the number of positive cases of the infectious disease on the date, the predictive management program predicting an increase / decrease trend in the number of infected people of the infectious disease, The computer includes: A first acquisition step of acquiring the pathogen data on the collection date from the pathogen data storage device; A second acquisition step of data on the number of positive cases of the infectious disease during a predetermined period before the sampling date from the data storage device; a step of predicting the increase / decrease tendency after the sampling date based on a ratio between the pathogen data acquired in the first acquisition step and the positive case number data acquired in the second acquisition step; a transmission step of transmitting information regarding the predicted increase / decrease tendency; A prediction management program characterized by executing the above.

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