Prediction method and prediction system

Abstract

To provide a prediction method and prediction system capable of predicting an increase / decrease tendency of the number of persons positive for an infectious disease.SOLUTION: A prediction method includes: a sampling step S1 for sampling a sample of sewage; an analysis step S2 for analyzing the sample and acquiring pathogen data on a pathogen of an infectious disease included in the sample; a collection step S3 for collecting positive person number data on the number of positive persons in a predetermined period before the date when the sample is sampled; and prediction steps S4 and S5 for predicting an increase / decrease tendency after the date when the sample is sampled on the basis of a ratio of the pathogen data acquired by the analysis step S2 and the positive person number data collected by the collection step S3.SELECTED DRAWING: Figure 6

Description

Technical Field

[0001] The present invention relates to a prediction method and a prediction system for predicting the increasing and decreasing trends of the number of positive cases of 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 detected in an infectious disease test, measures such as isolating positive cases and close contacts are being taken. However, among the positive cases, there are asymptomatic people, or people who do not undergo infectious disease tests even when they have symptoms due to mild cases, etc., so it is difficult to predict the increasing and decreasing trends of the number of positive cases of infectious diseases.

[0003] Patent Document 1 discloses an infectious disease test method including a sampling step of sampling a sample from sewage during a sampling period, an analysis step of obtaining information on the presence of infectious pathogens in the sample, and a notification step of notifying a facility of the information together with the sampling period. In the infectious disease test method described in Patent Document 1, the notified information and the sampling period indicate the possibility that there are infected persons with infectious pathogens among the residents of the facility at least during the sampling period.

[0004] However, while the infectious disease test method described in Patent Document 1 can indicate the possibility that there are infected persons with infectious pathogens among the residents of the facility, it is difficult to predict the increasing and decreasing trends of the number of positive cases of infectious diseases. That is, there is a difference in the timing when the results are revealed between the analysis result regarding the presence of infectious pathogens in the sample collected from sewage and the aggregation result regarding the number of positive cases aggregated through health administration, so it is difficult to predict the increasing and decreasing trends of the number of positive cases of infectious diseases.

[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 submitted by the collection company 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 positive cases of infectious diseases. Against this backdrop, there is a desire to predict trends in the increase or decrease of the number of positive cases of infectious diseases such as COVID-19. [Prior art documents] [Patent Documents]

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

[0007] This invention has been made in view of the above circumstances, and aims to provide a prediction method and prediction system that can predict the trend of increase or decrease in the number of people who test positive for infectious diseases. [Means for solving the problem]

[0008] A first aspect of the present invention is a prediction method for predicting the trend of increase or decrease in the number of people who test positive for an infectious disease, comprising: a sampling step of collecting a sample of sewage; an analysis step of analyzing the sample and obtaining pathogen data relating to the pathogen of the infectious disease contained in the sample; a collection step of collecting data relating to the number of people who test positive for a predetermined period prior to the day the sample was collected; and a prediction step of predicting the trend of increase or decrease after the day the sample was collected, based on the ratio of the pathogen data obtained by the analysis step and the data relating to the number of people who test positive collected by the collection step.

[0009] A second aspect of the present invention is a prediction system for predicting the trend of increase or decrease in the number of people who test positive for an infectious disease, comprising: a calculation unit that acquires pathogen data relating to the pathogen of the infectious disease contained in a collected sewage sample and collects data on the number of positive cases relating to the number of positive cases for a predetermined period prior to the day the sample was collected; a pathogen data storage unit that stores the pathogen data acquired by the calculation unit; a positive case data storage unit that stores the positive case data collected by the calculation unit; and a prediction unit that predicts the trend of increase or decrease after the day the sample was collected based on the ratio of the pathogen data stored in the pathogen data storage unit and the positive case data stored in the positive case data storage unit. [Effects of the Invention]

[0010] According to the present invention, it is possible to provide a prediction method and prediction system that can predict the trend of increase or decrease in the number of people who test positive for infectious diseases. [Brief explanation of the drawing]

[0011] [Figure 1] This is a schematic diagram illustrating the relationship between virus concentration and the number of positive cases in this embodiment. [Figure 2] 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 3] This is a schematic diagram illustrating the ratio between virus data and the number of positive cases. [Figure 4] This is a block diagram showing the main components of the prediction system according to this embodiment. [Figure 5] This is a block diagram showing the specific main components of the prediction system according to this embodiment. [Figure 6] This is a flowchart illustrating the prediction method according to this embodiment. [Figure 7] This graph illustrates the first specific example of a method for calculating a predetermined probability (threshold). [Figure 8]This graph illustrates a second specific example of a method for calculating a predetermined probability (threshold). [Modes for carrying out the invention]

[0012] Embodiments of the present invention will be described below with reference to the drawings. The embodiments described below are preferred examples of the present invention and are subject to various technically preferred limitations. However, the scope of the present invention is not limited to these embodiments unless otherwise specified in the following description. 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, but may be other viruses such as influenza virus or norovirus. Furthermore, the pathogen is not limited to viruses, but may be bacteria. Also, in each drawing, similar components are denoted by the same reference numerals, and detailed descriptions are omitted as appropriate.

[0013] Figure 1 is a schematic diagram illustrating the relationship between virus concentration and the number of positive cases in this embodiment. Figure 2 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 3 is a schematic diagram illustrating the ratio of virus data to the number of positive cases.

[0014] First, an overview of the prediction method according to this embodiment will be explained with reference to Figures 1 to 3. 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 person tests positive for an infectious disease, measures such as isolating the infected person and their close contacts are taken. However, it is difficult to predict the trend of increase or decrease in the number of positive cases of infectious diseases because some positive individuals are asymptomatic, and some people who develop symptoms do not undergo testing because their symptoms are mild.

[0015] Therefore, the inventor collected samples of general sewage such as domestic sewage from public sewers, and tried to predict the increasing and decreasing trends of the number of positive cases of infectious diseases in the administrative area including the service area of the public sewer by using the data on the viruses contained in the samples. In this specification, for convenience of explanation, the sewage sample is simply referred to as "sewage sample", and the data on the viruses contained in the sewage sample is simply referred to as "virus data". Here, the virus data is an example of the "pathogen data" of the present invention, and is the concentration of the virus obtained by analyzing the sewage sample in the combined sewer of the combined sewer or the sewage pipe of the separate sewer. In this specification, for convenience of explanation, this concentration of the virus is simply referred to as "virus concentration".

[0016] However, there is a difference in the time when the results are found between the results of the data on the viruses (i.e., virus data) contained in the sewage sample of a certain public sewer and the results of the data on the number of positive cases aggregated through the health administration in the administrative area including the service area of the public sewer. In this specification, for convenience of explanation, the data on the number of positive cases is simply referred to as "positive case number data" or "number of positive cases". Therefore, there is a time difference between the increasing and decreasing trends of the virus data and the increasing and decreasing trends of the positive case number data. For example, as shown in FIG. 1, the increasing and decreasing trend of the positive cases of the infectious disease test (i.e., the positive case number data) lags behind the increasing and decreasing trend of the virus concentration (i.e., the virus data) contained in the sewage sample.

[0017] Figure 2 is a graph summarizing the number of positive cases collected by the inventor in a certain administrative region, the moving average number of positive cases over the last 7 intervals for this number of positive cases, the virus concentration in the combined sewer system (System 1) included in that administrative region, and also the virus concentration in the separate sewer system (System 2) included in that administrative region. According to Figure 2, 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 of System 2 on February 24, followed by the virus concentration of System 1 on March 3, and finally the 7-interval moving average number of positive cases on March 5. In the subsequent upward trend of the number of positive cases after May 5, the peaks on the vertical axis of the graph are, first, the virus concentration of System 1 on May 12, followed by the virus concentration of System 2 on May 19, and finally the 7-interval moving average number of positive cases on May 26. That is, for the vertical axis of the graph to reach a peak, for the virus concentration, either System 1 may come first or System 2 may come first. However, for the 7-interval moving average number of positive cases, it comes last in both cases after January 6 and after May 5. From this, it can also be seen that the increasing and decreasing trend of the positive case data lags behind the increasing and decreasing trend of the virus data (that is, the virus concentration in Figure 2).

[0018] Thus, a time difference occurs between the increasing and decreasing trend of the virus data and the increasing and decreasing trend of the positive case data. Therefore, it is difficult to predict the increasing and decreasing trend of the number of positive cases of an infectious disease simply by obtaining data on the virus contained in sewage samples. For example, as shown in Figure 1, for a certain value W of the virus concentration (that is, the virus concentration) in the sewage sample, there are two values, the virus concentration W1 in the first period T1 of the increasing trend and the virus concentration W2 in the second period T2 of the decreasing trend. Therefore, simply by obtaining data on the virus contained in the sewage sample, it is not known whether it is from the period of the increasing trend or the period of the decreasing trend. Therefore, it is difficult to predict the increasing and decreasing trend of the number of positive cases of an infectious disease.

[0019] Furthermore, as shown in Figure 2, when analyzing sewage samples using methods such as PCR testing to obtain virus concentration, the analysis using PCR testing is not a continuous analysis. In other words, in the example in Figure 2, sewage samples are collected from the public sewer system, delivered to another location, and analyzed there to obtain virus concentration. Therefore, the obtained virus concentration data does not represent closely spaced, 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, it is not possible to accurately determine, for example, 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 2 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 positive cases of infectious diseases when continuous analysis is not performed.

[0020] Therefore, the prediction method according to 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 positive cases of infectious diseases 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 1, 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 it is not limited to this, and the opposite (i.e., C / W) may also be used.

[0021] The prediction method according to this embodiment utilizes these properties and, as shown in Figure 3, 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 positive cases may increase in the future, or that a large proportion of positive cases are not yet identified. On the other hand, the prediction method according to 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 positive cases may decrease in the future, or that a small proportion of positive cases are not yet identified. Alternatively, the prediction method according to this embodiment sets a threshold for the ratio (W / C) that distinguishes between periods when the number of positive cases 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 positive cases by comparing the ratio of virus concentration W to the number of positive cases C (W / C) on a predetermined day with the threshold.

[0022] The prediction method according to this embodiment will be further described below with reference to the drawings. Figure 4 is a block diagram showing the main components of the prediction system according to this embodiment.

[0023] The prediction system 2 shown in Figure 4 comprises a computer 21 and a memory unit 22. The computer 21 has a control unit 23 (see Figure 5) and reads the program 221 stored in the memory unit 22 to perform various calculations and processes. The term "computer" here is not limited to personal computers, but also includes arithmetic processing units, microcontrollers, etc., which are included in information processing equipment, and is a general term for equipment and devices that can realize the functions of the present invention through a program.

[0024] The memory unit 22 stores the program 221 executed by the computer 21. Examples of the memory unit 22 include semiconductor memory or a hard disk drive (HDD) built into the prediction system 2. Alternatively, the memory unit 22 may be an external storage device connected to the computer 21.

[0025] Program 221 includes computational programs for analyzing and processing virus data, computational programs for collecting and processing positive case data, and computational programs for predicting trends in the increase or decrease of the number of positive cases of infectious diseases. Program 221 is not limited to being stored in the storage unit 22; it may also be pre-stored and distributed on a computer-readable storage medium, or downloaded to the prediction system 2 via a network.

[0026] Figure 5 is a block diagram showing the specific main components of the prediction system according to this embodiment. The prediction system 2 shown in Figure 5 includes a control unit 23, a storage unit 22, and a communication unit 24. The control unit 23 is, for example, a CPU (central processing unit), and reads the program 221 (see Figure 4) stored in the storage unit 22 and performs various calculations and processes. The control unit 23 includes a calculation unit 231 and a prediction unit 232. The calculation unit 231 and the prediction unit 232 are realized by the computer 21 executing the program 221 stored in the storage unit 22. The calculation unit 231 and the prediction unit 232 may be realized by hardware, or by a combination of hardware and software. The storage unit 22 includes a virus data storage unit 222 and a positive case count data storage unit 223. The virus data storage unit 222 in this embodiment is an example of the "pathogen data storage unit" of the present invention.

[0027] The calculation unit 231 acquires data (i.e., virus data) regarding viruses contained in the sewage sample from a management server (not shown) via the communication unit 24 and an internet line, and stores it in the virus data storage unit 222. The calculation unit 231 may store the acquired virus data in the virus data storage unit 222 as is, or it may perform predetermined processing on the acquired virus data and store the processed virus data in the virus data storage unit 222.

[0028] The virus data stored in the virus data storage unit 222 is, for example, the concentration of a virus obtained by analyzing a sample of sewage flowing through a sewer pipe in a separate sewer system. Alternatively, the virus data stored in the virus data storage unit 222 is, for example, the concentration of a virus obtained by analyzing a sample of sewage flowing through a combined sewer pipe in a combined sewer system.

[0029] As mentioned above, the processed virus data may be stored in the virus data storage unit 222. For example, the virus data stored in the virus data storage unit 222 is the virus load calculated by multiplying the virus concentration obtained by analyzing a sample of sewage flowing through a sewer pipe of a separate sewer system by the sewage flow rate. Alternatively, for example, the virus data stored in the virus data storage unit 222 is the virus load calculated by multiplying the virus concentration obtained by analyzing a sample of sewage flowing through a combined sewer pipe of a combined sewer system by the sewage flow rate.

[0030] Furthermore, the calculation unit 231 collects data related to the number of positive cases (i.e., positive case data) from a management server (not shown) via the communication unit 24 and an internet line, and stores it in the positive case data storage unit 223. The calculation unit 231 may store the collected positive case data as is in the positive case data storage unit 223, or it may perform a predetermined process on the collected positive case data and store the processed positive case data in the positive case data storage unit 223.

[0031] The positive case data stored in the positive case data storage unit 223 is, for example, the total or average number of positive cases during a predetermined period prior to the day the sewage sample was collected. Here, "predetermined period" refers to, for example, about one week. Alternatively, the positive case data stored in the positive case data storage unit 223 is, for example, the total or average number of hospitalized patients among the positive cases during a predetermined period prior to the day the sewage sample was collected. Alternatively, the positive case data stored in the positive case data storage unit 223 is, for example, the total or average number of deaths among the positive cases during a predetermined period prior to the day the sewage sample was collected.

[0032] The prediction unit 232 predicts the trend of increase or decrease in the number of positive cases after the day the sewage sample was collected, based on the ratio of the virus data stored in the virus data storage unit 222 to the number of positive cases data stored in the number of positive cases data storage unit 223. An example of the virus data stored in the virus data storage unit 222 is as described above. Similarly, an example of the number of positive cases data stored in the number of positive cases data storage unit 223 is as described above. For example, as described above with respect to Figures 1 to 3, the prediction unit 232 predicts the trend of increase or decrease in the number of positive cases after the day the sewage sample was collected, based on the ratio of the virus concentration W to the number of positive cases C (W / C).

[0033] Furthermore, the prediction unit 232 predicts the trend of increase or decrease in the number of positive cases based on the ratio of the probability that the number of positive cases one week after a predetermined date has increased compared to the number of positive cases on a predetermined date, and that this probability is greater than or equal to a predetermined probability (threshold). Details of this will be described later.

[0034] Figure 6 is a flowchart illustrating the prediction method according to this embodiment. First, in step S1, a sewage sample is collected. Step S1 in this embodiment is an example of the "sampling step" of the present invention. For example, the sewage sample is collected from the sewage flowing through the wastewater pipe of a separate sewer system. Alternatively, for example, the sewage sample is collected from the sewage flowing through the combined sewer pipe of a combined sewer system.

[0035] Next, in step S2, the collected sewage sample is analyzed to obtain data on viruses contained in the sewage sample (i.e., virus data). Step S2 in this embodiment is an example of the "analysis step" of the present invention. An example of the virus data obtained in step S2 is as described above with respect to Figure 5. For example, the calculation unit 231 of the prediction system 2 (see Figure 5) obtains virus data from a management server (not shown) via the communication unit 24 and an internet line, and either stores the obtained virus data as is in the virus data storage unit 222, or stores the obtained virus data after performing a predetermined process in the virus data storage unit 222.

[0036] Next, in step S3, positive case data is collected regarding the number of people who tested positive for infectious diseases during a predetermined period prior to the day the sewage sample was collected. Step S3 in this embodiment is an example of the "collection step" of the present invention. For example, the calculation unit 231 of the prediction system 2 collects data on the number of positive cases (i.e., positive case data) from a management server (not shown) via the communication unit 24 and an internet line, and either stores the collected positive case data as is in the positive case data storage unit 223, or stores the collected positive case data after performing a predetermined process in the positive case data storage unit 223.

[0037] Next, in step S4, the ratio of the virus data obtained in the analysis step (step S2) to the number of positive cases data collected in the collection step (step S3) is calculated. For example, in step S4, the prediction unit 232 (see Figure 5) of the prediction system 2 calculates the ratio of virus concentration W to the number of positive cases C (W / C) based on the virus data stored in the virus data storage unit 222 and the number of positive cases data stored in the number of positive cases data storage unit 223.

[0038] Next, in step S5, the trend of increase or decrease in the number of positive cases after the day the sewage sample was collected is predicted based on the calculated ratio. Steps S4 and S5 of this embodiment are examples of the "prediction steps" of the present invention. For example, in step S5, the trend of increase or decrease in the number of positive cases is predicted based on the ratio of the probability that the number of positive cases data one week after a predetermined day has increased compared to the number of positive cases data on a predetermined day, which is equal to or greater than a predetermined probability (threshold).

[0039] The following is an example of a method for calculating a predetermined probability (threshold), with reference to the diagram. Figure 7 is a graph illustrating the first specific example of a method for calculating a predetermined probability (threshold). Figure 7 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 7) 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 7). The W / C ratio shown on the right vertical axis of Figure 7 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 8, described later, is similar). When the value on the horizontal axis of Figure 7 (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 7 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.

[0040] 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 7 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 7 (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 7 is less than "1.00".

[0041] Therefore, in the prediction step of the prediction method according to this embodiment, for example, "400" is set as the threshold for the W / C ratio, where 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. Then, 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, it is predicted that the number of positive cases is on an increasing trend. On the other hand, 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, it is predicted that the number of positive cases is on a decreasing trend. However, "400" is just one example of a threshold for the W / C ratio, and the threshold for the W / C ratio is not limited to "400".

[0042] Figure 8 is a graph illustrating a second specific example of the method for calculating a predetermined probability (threshold). Figure 8, similar to the graph described above for Figure 7, is a graph showing 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 8 (weekly increase rate of the number of positive PCR test results) are as described above for Figure 7.

[0043] 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 8 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 8 (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 8 is less than "1.00".

[0044] Therefore, in the prediction step of the prediction method according to this embodiment, for example, "400" is set as the threshold for the W / C ratio, where 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. Then, if the ratio of the virus concentration W to the number of positive cases C (W / C ratio; system concentration 2) on a predetermined day is 400 or more, it is predicted that the number of positive cases is on an increasing trend. On the other hand, if the ratio of the virus concentration W to the number of positive cases C (W / C ratio; system concentration 2) on a predetermined day is less than 400, it is predicted that the number of positive cases is on a decreasing trend. However, as mentioned above with respect to Figure 7, "400" is just one example of a threshold for the W / C ratio, and the threshold for the W / C ratio is not limited to "400".

[0045] According to the prediction method of this embodiment, the prediction is not simply based on viral data (e.g., viral concentration) regarding viruses contained in the sewage sample to predict the trend of increase or decrease in the number of positive cases after the day the sewage sample was collected, but rather based on the ratio (W / C ratio) of viral data (e.g., viral concentration W) obtained in the analysis step to the number of positive cases data (e.g., number of positive cases C) collected in the collection step. Thus, the prediction method of this embodiment can predict the trend of increase or decrease in the number of positive cases of infectious diseases.

[0046] Furthermore, when predicting the trend of increase or decrease in the number of positive cases based on the ratio of the probability that the number of positive cases one week after a given date increases compared to the number of positive cases on a given date is greater than or equal to a predetermined probability, it is possible to predict the trend of increase or decrease in the number of positive cases of infectious diseases with higher accuracy.

[0047] Furthermore, by using data such as the total or average number of positive cases during a predetermined period prior to the day the sample was collected, the total or average number of hospitalized patients among those who tested positive, or the total or average number of deaths among those who tested positive, it is possible to predict the trend of increase or decrease in the number of positive cases of infectious diseases with greater accuracy.

[0048] 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]

[0049] 2: Prediction system, 21: Computer, 22: Memory unit, 23: Control unit, 24: Communication unit, 221: Program, 222: Virus data storage unit, 223: Positive case count data storage unit, 231: Calculation unit, 232: Prediction unit, C: Number of positive cases, C1: Number of positive cases, C2: Number of positive cases, T1: First period, T2: Second period, T3: Third period, T4: Fourth period, W: Virus concentration, W1: Virus concentration, W2: Virus concentration

Claims

1. A prediction method for forecasting the trend of increase or decrease in the number of people who test positive for infectious diseases, The sampling step involves collecting a sewage sample, An analysis step of analyzing the sample and obtaining pathogen data relating to the pathogen of the infectious disease contained in the sample, A collection step of collecting data on the number of positive cases during a predetermined period prior to the day the sample was collected, A prediction step predicts the trend of increase or decrease after the day the sample was collected, based on the ratio of the pathogen data obtained by the analysis step and the number of positive cases data collected by the collection step. A prediction method characterized by comprising the following features.

2. The prediction method according to claim 1, characterized in that the prediction step predicts the trend of increase or decrease based on the ratio at which the probability of the number of positive cases one week after a predetermined day increasing relative to the number of positive cases on a predetermined day is greater than or equal to a predetermined probability.

3. The prediction method 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 pipe of a separate sewer system.

4. The prediction method 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.

5. The prediction method 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.

6. The prediction method 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.

7. The prediction method 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.

8. The prediction method 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.

9. The prediction method 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.

10. A prediction system that predicts the trend of increase or decrease in the number of people who test positive for infectious diseases, A calculation unit that acquires pathogen data relating to the pathogen of the infectious disease contained in the collected sewage sample, and collects data on the number of positive cases relating to the number of positive cases during a predetermined period prior to the day the sample was collected, A pathogen data storage unit that stores the pathogen data acquired by the calculation unit, A positive case count data storage unit stores the positive case count data collected by the calculation unit, A prediction unit predicts the increase or decrease trend after the day the sample was collected, based on the ratio of the pathogen data stored in the pathogen data storage unit to the number of positive cases data stored in the number of positive cases data storage unit. A prediction system characterized by having the following features.

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