Infection countermeasure method and infection countermeasure system

Abstract

The present invention estimates the infection status of a population at a lower cost or earlier, and uses this information to perform infection control of the population. The present invention is provided with: an observation unit for sampling a propagating substance (X) in a region (Ri) and measuring the amount thereof; and an analysis unit that calculates, on the basis of the measured value (VXi) of the amount of the propagated substance, the value of one or more indexes or an approximate value thereof for preventing infection over the entire area range. The analysis unit outputs, on the basis of the calculated value of the index or an approximate value thereof, a corresponding measure for countermeasures against infection over the entire area range.

Description

Technical Field

[0001] The present disclosure relates to methods and systems for implementing infection countermeasures. Background Art

[0002] Measures for protecting people or groups of people from infection with pathogens (hereinafter referred to as "infection countermeasures") are a common concern of mankind regardless of time and place. Infection countermeasures include infection countermeasures for individuals and infection countermeasures for groups, and various methods have been developed so far.

[0003] Prior Art Literature

[0004] Patent Literature

[0005] Patent Document 1: Japanese Patent Application Publication No. 2020-008969 Summary of the invention

[0006] Problems to be solved by the invention

[0007] Infection countermeasures for individuals include prevention and treatment. Among these countermeasures, it is important to prevent pathogens from entering the body as much as possible and to minimize the damage when they enter the body. On the other hand, infection countermeasures for groups include preventing pathogens from entering the body of the group through the spread of pathogens as much as possible and preparing a medical system in case the infection spreads within the group.

[0008] Various methods have been developed for infection countermeasures targeting individuals. On the other hand, for infection countermeasures targeting groups, there are many issues because there are fewer events such as epidemics that originally require such countermeasures. For example, in order to take countermeasures, it is necessary to understand the infection status within the group (how many infected people there are, at what rate the infection is expanding or shrinking, etc. Hereinafter, referred to as "infection status"). However, this requires huge costs (costs, labor, time, legal improvements, etc.). For example, even if everyone is checked for infection at a certain point in time, the information will become outdated the moment the inspection is completed. In other words, even if there are new infected people or people who have recovered from the infection later, they cannot be found without re-examination. In addition, there is also the problem that in the case of newly infected people, there are fewer pathogens in the body, so sometimes they will not be detected during the inspection.

[0009] The subject of the present disclosure is to infer the infection status of a group at a lower cost or earlier time (including inferences made by approximation), and to use this information to implement infection control of the group. Although infection control here typically refers to improving the infection status by reducing the number of infected people, it also includes strengthening the medical system or regulating economic activities, and taking measures corresponding to the infection status. In the following, the term "infection countermeasures" will be used in the sense of inference and control.

[0010] Methods for solving problems

[0011] In order to solve the above-mentioned problems, the present disclosure utilizes substances contained in the environment (although it is set to be in the air below, it can also be water, etc.) called transmission substances. Transmission substances refer to substances that indicate that there is a source of infection (infected person, etc.) nearby, and a typical example is the pathogen itself. For example, a person with a cold releases transmission substances into the environment by sneezing, coughing, talking, breathing, etc. Therefore, if there are transmission substances in the environment, there is a high possibility that there is a source of infection nearby. Not only that, this also suggests the possibility that someone will bring the pathogen into the body by contacting the source of infection, etc., thereby causing a new infection.

[0012] In the present disclosure, by observing (sampling and measuring) the transmission substances in the environment, the presence or absence of infected persons in the target area and the number of newly infected persons in the area are estimated, thereby contributing to infection control. In addition, not only the transmission substances are measured, but also the amount of people in the area is combined to make more detailed inferences.

[0013] In addition, although the present disclosure deals with a group of people, the method shown here can also be applied to a group of animals or plants, etc. In addition, as a special case, it can also be applied to a case where the group consists of one person.

[0014] Specifically, the infection countermeasure system according to the present disclosure is characterized by comprising:

[0015] An observation unit, which samples the propagation substance (X) in an arbitrary region Ri and measures its amount;

[0016] An analysis unit calculates the value of one or more indicators used for infection countermeasures in the entire area or their approximate values ​​based on the measured value (VXi) of the amount of the propagating substance.

[0017] In particular, it is characterized in that the analysis unit inputs the cumulative retention time of the group in the area Ri, that is, the retention amount (si), and infers the floating amount (qi) of the pathogen based on the measured value (VXi) of the amount of the propagating substance, and uses the floating amount (qi) and the retention amount (si) to calculate the release amount (ei) of the pathogen or the absorption amount (ai) of the pathogen, and outputs the corresponding measures for the infection countermeasures for the entire area based on the result of the calculation, which has a pre-established relationship with the area Ri. The release amount (ei) of the pathogen is obtained based on the result of dividing the floating amount (qi) by the retention amount (si). The absorption amount (ai) of the pathogen is obtained based on the result of multiplying the floating amount (qi) and the retention amount (si).

[0018] Here, the floating amount (qi) of the propagating substance includes the case where it is proportional to the amount of the propagating substance (VXi) (specific floating amount q'). Similarly, the retention amount, release amount, and absorption amount include the specific retention amount (si', si"), the specific release amount (ei'), and the specific absorption amount (ai'), respectively. In addition, the ratio also includes an approximate ratio.

[0019] In addition, the method for implementing infection countermeasures involved in the present disclosure is characterized in that the infection status is inferred and predicted by sampling the transmission substance in the environment and measuring its amount as the measurement value VXi. In addition, it can also be set to use the results obtained by inferring and predicting the infection status in control. The same is true in the following.

[0020] Here, the target area range includes a distance of several tens of meters or more from the sampling location, or a distance greater than or equal to the target distance in the known technology at the time of filing the present disclosure.

[0021] By measuring the amount of substances in the environment that are both causes and consequences of deteriorating the functions of organisms (for example, viruses in sewage are excluded), the infection status can be inferred and predicted.

[0022] Specifically, the infection countermeasure method involved in the present disclosure is characterized by comprising:

[0023] The step of sampling the propagation material (X) in a certain area Ri and measuring its amount;

[0024] A step of calculating the value of one or more indicators or their approximate values ​​for infection countermeasures for the entire area based on the measured value (VXi) of the amount of the transmission substance;

[0025] The step of outputting countermeasures for infection control in the entire area based on the calculated value of the indicator or its approximate value.

[0026] In addition, the method involved in the present disclosure is characterized in that at least any one of VXi, si', and si" is used to infer and predict an indicator representing the infection status.

[0027] Furthermore, the method according to the present disclosure is characterized in that ei'=VXi / si' is calculated using VXi and si'.

[0028] In addition, the method involved in the present disclosure is characterized in that VXi, si" is used to calculate ai'=qi'*si".

[0029] Alternatively, the influence of the number of new infections pi and the like may be estimated by using ai' obtained by calculation.

[0030] Furthermore, the method according to the present disclosure is characterized in that ei', si', and si'' are used to calculate ai'=ei'*si'*si''.

[0031] Furthermore, the method according to the present disclosure is characterized in that e0" is calculated by integrating the measurement values ​​of a plurality of sampling units.

[0032] Furthermore, the method according to the present disclosure is characterized in that, even in a region R without a sampling unit, ei' or e0" of a place with a similar infection status is used to calculate a'=ei'*s'*s" or a'=e0"*s'*s".

[0033] In addition, the method of the present disclosure is characterized in that qi', ei', and ai' are measured to estimate the effect of the infection countermeasure. In addition, the infection countermeasure can also be optimized by using this method and performing feedback control.

[0034] According to the system and method for implementing infection countermeasures involved in the present disclosure, even when the number of new positives decreases, if ai' increases, a lockdown can be implemented, and conversely, even when the number of new positives increases, if ai' decreases, the lockdown can be lifted. Being able to use ai' to predict like this is a feature of the present disclosure.

[0035] The above-mentioned method can be realized as a system for implementing infection countermeasures, or can be realized as a computer program for operating the system or device.

[0036] Effects of the Invention

[0037] According to the present disclosure, it is possible to infer the infection status of a group at a lower cost or at an earlier time, and use this information to implement infection countermeasures for the group. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 This is a functional block diagram of the infection countermeasure system 1 in the present disclosure.

[0039] Figure 2 This is a structural diagram when the analysis unit 3 is realized by a program.

[0040] Figure 3 2 is an example of a region range 6 with one sampling portion 21 .

[0041] Figure 4 2 is an example of a region range 6 with a plurality of sampling portions 21 .

[0042] Figure 5 This is an explanatory diagram of the determination criterion 41 .

[0043] Figure 6 This is a flowchart of the first determination process (first embodiment).

[0044] Figure 7 This is a flow chart of the first infection quantification function (first embodiment).

[0045] Figure 8 This is a flow chart of the second infection quantification function (second embodiment).

[0046] Fig. 9 This is a flowchart of the third determination process (third embodiment).

[0047] Fig.10 This is a flow chart of the third infection quantification function (third embodiment).

[0048] Fig.11 This is an explanatory diagram of the update reference 42.

[0049] Fig.12 This is a flowchart of the update process (fourth embodiment).

[0050] Fig.13 This is a flowchart of the fourth infection quantification function (fourth embodiment).

[0051] Fig.14 is the relationship between the number of new infections and the number of new positives.

[0052] Fig.15 This is a flowchart of the infection limitation process (eighth embodiment).

[0053] Fig.16 This is a schematic diagram for explaining the advantages of the second embodiment. DETAILED DESCRIPTION

[0054] Hereinafter, various embodiments and implementations of the present disclosure will be described based on the drawings. In addition, although the present disclosure is mainly described as a method invention, it can also be easily applied to the production of a device, a program, and a recording medium storing the program having the same function.

[0055] [Example 1]

[0056] Although the core concept of the present disclosure is relatively simple, as far as the inventor knows, there is no country or region that implements infection countermeasures based on the same concept, which is considered to be evidence that it is difficult to achieve the concept. The inventor conducted research on his own body through observation and recording for about two years and found that the concept is effective. In addition, regarding the inventor's physique, please refer to the supplementary instructions at the end of this embodiment.

[0057] In the following, the spread of the new coronavirus SARS-CoV-2 (hereinafter referred to as the "virus". However, it is referred to as the "coronavirus" when it is easily confused with the virus as a whole) in Japan from the beginning of 2020 is divided into the first wave to the seventh wave, but it is assumed to refer to the following periods, namely: the first wave is from early January to early May 2020, the second wave is from mid-May to mid-October 2020, the third wave is from late October 2020 to mid-February 2021, the fourth wave is from late February to late May 2021, the fifth wave is from early June to late November 2021, the sixth wave is from early December 2021 to mid-June 2022, and the seventh wave is from late June to August 7, 2022 (in the embodiment of the present disclosure, the observation results up to August 7, 2022 (Sunday) are shown). In addition, although the division of each wave may vary depending on different views, it is not particularly important in the following discussion. In addition, although the above division seems to be slightly earlier than the fluctuation of the number of new positives, the reason is that the division is not based on the statistics of the number of new positives, but on the fluctuation of the number of new infections (as described later, this fluctuation will occur 1 to 2 weeks in advance). In addition, the so-called "newly infected person" refers to a person who is newly infected by bringing in pathogens on a certain day, and the "newly positive person" refers to a person who is newly diagnosed as positive and announced as statistical data on a certain day (please note that not all newly infected people will appear as new positives in future statistics. Because there are also people who have been cured before being examined, etc.).

[0058] In addition, since it is ideal to know the holidays in advance when interpreting the observation results, the following days are listed. The holidays in 2020, including Sundays, are as follows: January 1 (Wednesday), January 13 (Monday), February 11 (Tuesday), February 23 (Sunday), February 24 (Monday), March 20 (Friday), April 29 (Wednesday), May 3 (Sunday), May 4 (Monday), May 5 (Tuesday), May 6 (Wednesday), July 23 (Thursday), July 24 (Friday), August 10 (Monday), September 21 (Monday), September 22 (Tuesday), November 3 (Tuesday), November 23 (Monday).

[0059] In addition, the holidays in 2021, including Sundays, are as follows: January 1 (Friday), January 11 (Monday), February 11 (Thursday), February 23 (Tuesday), March 20 (Saturday), April 29 (Thursday), May 3 (Monday), May 4 (Tuesday), May 5 (Wednesday), July 22 (Thursday), July 23 (Friday), August 8 (Sunday), August 9 (Monday), September 20 (Monday), September 23 (Thursday), November 3 (Wednesday), November 23 (Tuesday).

[0060] Furthermore, the holidays in 2022, including Sundays, are as follows: January 1 (Saturday), January 10 (Monday), February 11 (Friday), February 23 (Wednesday), March 21 (Monday), April 29 (Friday), May 3 (Tuesday), May 4 (Wednesday), May 5 (Thursday), July 18 (Monday), August 11 (Thursday), September 19 (Monday), September 23 (Friday), October 10 (Monday), November 3 (Thursday), November 23 (Wednesday).

[0061] In addition, when referring to the "7-day average" of the number of new positive cases, it is assumed that it does not refer to the usual "average of the day and the previous 6 days", but to the "average of the day and the 3 days before and after". The reason is that the former is the value of the number of new positive cases after smoothing and delaying it by 3 days, and the present disclosure only wants to see the smoothed value without delay.

[0062] <Core Concept of This Publication>

[0063] The concept of the present disclosure is based on two discoveries. Although the details will be described later, a brief summary is described here. First, the first discovery is that the coronavirus can be sensed in a wider range than generally believed. This means that by simply investigating the amount of virus in the air in a smaller area, the amount of virus released by more people as a group can be understood. So, what can be done after knowing the amount of virus released by the group in total? It is natural to think that "the amount of virus is associated with the current total number of infected people in the group. Therefore, it can be inferred." On the other hand, the claim that "the amount of virus is associated with the number of new infections generated from the group (most likely on the same day). Therefore, future statistical predictions can be implemented" seems absurd at first glance (at least to the inventor). However, in fact, the latter is correct, which is the second discovery.

[0064] Another point, which is more of a simple criticism than a discovery, is that the virus infection status in adjacent areas (such as adjacent prefectures or adjacent towns, etc.) tends to be similar (the reason may be that people traveling between these areas spread the virus to each other, and the activities of people or weather conditions are similar). For example, the trend of the number of new positive cases in Tokyo and Kanagawa Prefecture is very similar.

[0065] Based on the above three points, the following concept is reached. That is, "only by investigating the amount of virus in the air in a smaller area, it is possible to infer the number of new infections (possibly on that day) in a larger area, and predict the number of new positives in the future". In addition, the so-called inference and prediction of the number of new infections and new positives includes not only the numbers themselves, but also, in a broader sense, the inference and prediction of the increase and decrease of the numbers (to be precise, the comparison of the size of the numbers at two points in time). In addition, in infection countermeasures, it is not necessarily necessary to infer and predict both the number of new infections and the number of new positives. For example, the prediction of the number of new positives is useful for purposes such as securing hospital beds in advance based on this information, but on the other hand, for example, in order to make a decision to blockade at an earlier time, the inference of the number of new infections is more important.

[0066] In addition, in this embodiment, since the statistical data of the number of new infections is not available, only the prediction of the number of new positives is improved. However, since the number of new positives is a secondary quantity generated by the surface presentation of the number of new infections in the past, it can essentially be interpreted as an inference of the number of new infections (it may be considered to be the number of new infections on that day, but it is uncertain. There may also be deviations for several days). Of course, although the fluctuations of the number of new infections and the number of new positives are very similar, it will be explained in a little more detail here. Fig.14As schematically shown in , the number of new positives has the following distribution, namely "the proportion of people who are judged to be positive on the tth day after infection is the percentage of y(t) in the number of new infections." (The figure shows the cases where y(4) = 10%, y(5) = 40%, y(6) = 20%, y(7) = 5%, and y(t) = 0 otherwise. In addition, as mentioned above, even if the sum of y(t) relative to all t is taken, it does not necessarily become 100%, and in this case it is set to 75%. In addition, the actual statistics related to the coronavirus are published by the National Institute of Infectious Diseases, etc.). In other words, although the number of new positives is the result of the delay in the number of new infections in the past, at this time, smoothing is achieved by weighted summing the data of the previous and subsequent days (however, in reality, as described later, a more complex relationship is formed due to the further increase in statistical fluctuations caused by whether it is a day of the week or a holiday). The following will describe the details of the above two findings.

[0067] <Details of the first discovery>

[0068] First, about the first discovery. Generally speaking, people think that there is a risk of infection with the coronavirus in places such as indoors or on trains where there are infected people, in places adjacent to the room where the infected person is, where there is air circulation through the door gap, etc., or if it is outdoors, when close to the infected person. Of course, although the virus (whether it is infectious or not is another question. For example, it may also be the remains or fragments of the virus. Even in the following text, the word "virus" is used in the same sense) can spread to places farther than these places, for example, at the seaside, there is usually no need to worry about the presence of the coronavirus in places where there are only a few people within a radius of 30 meters at a distance of about 10 meters from the sea surface. However, in fact, even in places like this, if it is a period of infection, it may sometimes be perceived by the human body. The detected viruses are believed to be a mixture of the following viruses, such as viruses released by people who are currently or have been in the place, viruses released and spread by people who are far away, and viruses released and leaked by people in nearby buildings or cars. (Even in a room with closed windows, trace amounts of viruses can enter from the outside. It is believed that they can also leak in the opposite direction, from indoors to outdoors.)

[0069] <Details of the second finding: Relationship between the date when the number of new positive cases reached a maximum and the amount of virus>

[0070] Next is the second discovery. The inventor actually walked and moved in Area A in Kanagawa Prefecture almost every day during the 3rd, 6th, and 7th waves, observing and recording the amount of coronavirus in the air. It was found that there were 5 days with a high amount of virus (therefore, considering the burden on the body, the observation was interrupted halfway) and the amount of virus was significantly higher than in recent days. The following is an explanation of each. First, the amount of virus in the 3rd wave peaked on December 24, 2020 (Thursday), which was significantly higher. As described later, in the 3rd wave, it is believed that there is a correlation between the amount of virus in the air and the number of new positives in Tokyo 14 days later. 14 days after December 24, that is, on January 7, 2021 (Thursday), the number of new positives in Tokyo on that day was actually the maximum value of the 3rd wave, 2520 (2447 when it was first announced, and later revised to 2520). In addition, the reasons for comparing with Tokyo instead of Kanagawa Prefecture are: prefectural boundaries are artificial; geographically, there is a lot of traffic between area A and Tokyo; as mentioned above, the infection status of the two places is similar; Tokyo has a large number of samples and a relatively complete system as a capital, so data such as changes in one day are the most reliable. Next, in the 6th wave, the virus amount peaked on January 26, 2022 (Wednesday), which was significantly higher. As described later, in the 6th and 7th waves, there is a high possibility that there is a correlation between the amount of virus in the air and the number of new positives in Tokyo 8 days later, and the maximum number of new positives in Tokyo was 21,562 on February 2 (Wednesday), 7 days later (21,576 when initially announced, but later revised). Finally, in the seventh wave, there were several days with significantly higher viral loads, in descending order: (1) July 26, 2022 (Tuesday), (2) July 21 (Thursday), and (3) July 25 (Monday) (where the difference between (2) and (3) is small). Subsequently, eight days after (1), on August 3 (Wednesday), the number of new positive cases in Tokyo was 38,940, showing a maximum value, but it was not the maximum value of the seventh wave, but the second largest value. The actual maximum number of new positive cases was 40,406 on July 28 (Thursday), which was seven days after (2).

[0071] In addition, although the word "strong" is used in the above, the situation on December 24, 2020, during the third wave, was very strong at the time, but it may be less than the situation in the sixth and seventh waves (however, it is difficult to compare because the observations were made a long time apart).

[0072] In addition, in this disclosure, place names are represented by symbols. First, A and A1 are respective areas in Kanagawa Prefecture (A1 area is located in A area). Next, P1 to P10 are respective points in A area.

[0073] On the other hand, in the fourth wave, the peak could not be determined due to the following reasons: observations were simplified or observed every other day due to concerns about the more contagious alpha variant; the increase and decrease in the amount of virus showed complex changes (although the correlation is not yet clear, the fourth wave overlapped with the increase in the proportion of alpha variants or the start of domestic vaccination. In addition, the changing trends in the proportion of variants in Tokyo and Japan can be found at: https: / / www.bousai.metro.tokyo.lg.jp / _res / proje cts / default_project / _page_ / 001 / 013 / 947 / 49kai / 2021061008.pdf, https: / / www.nikkei-science.com / ?p=64775. In addition, although it is not the case in Tokyo, the vaccination status of Japan can be found at https: / / info.vrs.digital.go.jp / dashboard d / confirmed); days with significantly higher viral loads did not appear in the observation (although it cannot be said to be very significant, during the observation period, the date with the highest viral load in the 4th wave was April 6, 2021 (Tuesday), followed by May 11 (Tuesday) to May 13 (Thursday)). Next, in the 1st wave, no observations were made because I was not yet aware of my own constitution. Furthermore, in the 2nd wave, although it was predicted at the time point of August 1, 2020 (Saturday) that the peak of the number of new positives would pass within two weeks, the concept of the present disclosure had not yet been derived at that time point, and no active observations were conducted, so it is for reference only. Finally, in the 5th wave, due to concerns about the more contagious Delta variant, in addition, on the contrary, the virus was almost not sensed from around October 2021 after the peak (until late December when the 6th wave entered), so only intermittent observations were made and no predictions were made.

[0074] <Details of the second finding: The relationship between more subtle fluctuations in the virus and the number of new positive cases>

[0075] The details of the more subtle fluctuations of the 2nd and 3rd waves and the early stage of the 4th wave will be described later in the <Details of this Example>. In addition, the period from the middle of the 4th wave to the 7th wave will be described in Examples 2 to 4.

[0076] <Details of the Second Discovery: Summary>

[0077] According to the above and the results described later, it can be said that the increase or decrease of the virus amount has a tendency to be delayed and manifested as an increase or decrease in the number of new positives. In addition, if the virus amount is at the same level, the number of new positives that are delayed is also at the same level, which implies this approximate relationship. If this property is used, the number of new positives in the future can be predicted based on the virus amount, or the current number of new infections can be inferred, thereby achieving early infection control. In addition, the word "approximately" is used here, and the reasons are as follows, for example. First, in addition to the virus amount, the number of people is also considered, which is more accurate (for example, according to the explanation in the second embodiment, the value obtained by multiplying the virus amount by the number of people has a strong correlation with the number of new positives). Second, in addition to the number of people, it also involves the proportion of people with antibodies or infected people, the proportion of mutant strains in the virus amount, the weather of the day (temperature, humidity, wind direction, etc.), etc. Third, as mentioned above, when the virus amount is manifested as the number of new positives, it also involves factors such as Fig.14 Fourth, there are deviations or errors in statistics and data aggregation (such as fewer inspections during holidays).

[0078] Here, let me add a few words about the first discovery. It can be considered that, for the virus, even in the early stages when the number of new positives is extremely small, the amount that can be sensed by the human body has spread in the air in urban areas. For example, the inventor first sensed the mutant strain (believed to be the Delta mutant or the B.1.617 lineage strain to which it belongs) that was more common in the 5th wave on Monday, May 24, 2021, outdoors during observation according to the second plan described later. While moving that day, the inventor noticed that the physical sensation when inhaling the virus was different from usual, but he inhaled a lot without thinking much, and in the evening he had diarrhea and frequent urination (the inventor had never experienced frequent urination before, but had frequent urination several times from then until around September 2021, and it has not appeared since then). At this point in time, the number of people infected with the B.1.617 lineage in Japan was still relatively small. On May 21, 2021, the Ministry of Health, Labor and Welfare announced that the number of people infected with this lineage from overseas discovered by quarantine stations as of May 7 was 160 (https: / / www.mhlw.go.jp / stf / newpage_18805.html). In addition, on May 26, 2021, the Ministry of Health, Labor and Welfare announced that the number of people infected with this lineage in Japan as of May 24 was only 29 (https: / / scienceportal.jst.go.jp / newsflash / 20210527_n01 / and https: / / www.fukuishimbun.co.jp / articl es / - / 1325735). In other words, it can be considered that even if there is a lack or inadequate statistical data on the number of new positive cases, it is possible to estimate the infection status at an early stage and implement infection control simply by measuring the amount of virus in the air.

[0079] The details of this embodiment will now be described starting from a method of specifically observing (sampling, and determining or measuring the amount of) the virus.

[0080] <Details of this Example: Method for observing the amount of virus (sampling)>

[0081] First, generally speaking, the amount of coronavirus in the air tends to be more in places with more people than in places with fewer people. In addition, its amount not only changes every day, but also fluctuates within a day. The most notable feature is that it is less in the morning and more in the evening. The reason is not clear, but for example, the virus in the group may decrease due to physical recovery during sleep, but it may multiply due to fatigue after getting up, and further, it may spread to each other during the day due to contact between people. The inventor observed for about 60 minutes a day (in fact, the amount of virus on the day can be roughly judged in about 10 seconds to 30 minutes, but in order to improve the accuracy, the observation was lengthened. In addition, even if it is not actively observed, the amount of virus in the air will be automatically sensed by breathing during simple movement or handling other affairs, so adding that time, it reaches 2 hours or more). Based on the above situation, the observation location and time period are divided into three plans (hereinafter referred to as the 1st to 3rd plan) according to the prevalence of the epidemic. The reason why a single plan was not adopted is that when the number of infected people is small, the virus can only be detected in crowded places or closed spaces, and when the number of infected people is large, it is difficult to judge the fluctuation of the virus amount in such places. It is easier to judge by investigating the places and time periods that can only be detected when the number of infected people is large. Below, it will be explained in sequence starting from the period when the number of infected people is small (in addition, for the sake of caution, this does not mean that the virus amount in these places is particularly large. If the number of people is the same, the virus amount in any place is considered to be the same). The first plan is from 18:00 to 19:00 in various buildings or facilities in the A1 area, and outdoors. The second plan is from 18:00 to 19:00, in addition to the places in the first plan, there are also roads from P1 to P2 to P3 and their neighboring roads (in addition, it is not necessary to go to all these places every time. However, as described later, the observation results do not change much). The third plan is to go to P4~P5 (turn right and walk on the sidewalk on the south-west side of the street)~P6 (here, cross the northeast side of the street)~P7~the three-way intersection in front (turn left once at P7, walk around the neighborhood for about 10 minutes, and then come out to the three-way intersection)~P8~P9~P3~P10, and the roads adjacent to them (not necessarily all of these places must be walked every time). In addition, although the above three plans are common, reference is also made to places that are appropriately visited regardless of other time periods, the inside of the train when taking other areas in Kanagawa Prefecture or Tokyo-bound trains, and the amount of virus in the home that comes in from the outside when at home during the spread of infection.

[0082] When observing under the same plan, the amount of virus will fluctuate every day, but it will also vary for each location. However, the distribution of the concentration of the virus has a tendency to remain roughly the same even if the day changes, and it does not change randomly every day. Therefore, even if the observation time is shortened to 30 minutes, for example, the amount of virus in the remaining places can be roughly estimated, so the observation results will not change significantly. Even if there is a change, it will only be one level for the levels (L0~L4) described later. In addition, the scale of the distribution of the amount of virus in each place varies, but when it is small, if it is outdoors, it will become a scale such as "the amount of virus at a certain intersection is higher than its surroundings."

[0083] Although it is a very rough reference indicator, when the number of new positives (predicted in the future) reaches hundreds of people, the first plan observation is easy to implement; when the number reaches hundreds to thousands of people, the second plan observation is easy to implement; when the number reaches more than thousands of people, the third plan observation is easy to implement. In the present embodiment, the first plan is used for observation in the second wave (wherein, active observation starts from about early August). In the third wave, until the virus was first perceived outdoors in late November 2020, the first plan was used, and it took about two months to gradually expand the scope to the second plan and implement it. In the fourth wave, the second plan is the main body, and the first plan is also used for observation when the amount of virus is small. In the fifth wave, the second plan is the main body, and observations under the third plan are also implemented in mid-August 2021, which is close to the peak. On the contrary, from about October 2021, when the virus can hardly be detected outdoors, the first plan is used for observation. In the sixth wave, observations will be conducted under the first or second plan in the early stage, but will be conducted under the third plan after January 18, 2022. In the seventh wave, observations will be conducted only under the third plan.

[0084] In addition, although it has not yet been implemented, in the case of measuring by machine, it is sufficient to use a sampling device such as an air sampler using a gelatin filter and follow any of the above plans to move along the same route for about 30 minutes at a fixed time every day while sampling the air. For example, start from P2 at 18:30, slow down a little, and arrive at P3 at 19:00. If the amount of virus that can be sampled is insufficient, it is sufficient to extend the time, extend the route, run multiple sampling devices in parallel, change the plan (or, sample and measure in parallel in all three plans, and select the measurement results of the appropriate plan according to the infection situation). Then, the RNA of the virus attached to the filter is extracted and the copy number is calculated by PCR. However, since the amount of virus sampled in different plans cannot be compared with each other, it is necessary to be careful. In addition, the amount of virus may change simply by moving to an adjacent street in the street. In addition, on a large street, the situation may change whether you walk on the left or right side. Therefore, when walking on the "same route", it is best to walk on the "same side of the same street". In addition, it would be better to keep the number of people in the route as uniform as possible. The reason is that if you pass through a place with a lot of people, the amount of virus sampled there will dominate, so that the fluctuation of the amount of virus sampled in other places will be relatively small, and it will be difficult to reflect in the measurement results. Similarly, since there is a tendency for the amount of virus to be higher indoors than outdoors, you need to be careful when passing through both in one sampling. For example, if the average amount of virus is measured for each place in advance, and the route is formulated in a way that passes through places where the difference in the amount of virus is not large, it is possible to prevent the amount of virus in a specific place from dominating. In addition, it is also considered effective to use equipment fixed at a place with a large flow of people for sampling (for example, set on the ceiling of the ticket gate of a subway station in the A1 area).

[0085] <Details of this Example: Method for observing the amount of virus (determination of the amount)>

[0086] The determination of the amount of virus is implemented using two methods (hereinafter referred to as the "ranking method" and the "comparison method" respectively). Before the middle of the 4th wave (before April 19, 2021), the rank method was basically used for determination, but the comparison method was sometimes used in combination, and in rare cases, only the comparison method was used for determination. Since then, the comparison method has been used for determination. The change in the determination method is due to the fact that the main purpose of the observation has been changed to capture the maximum and minimum values ​​of the fluctuation of the amount of virus, and the comparison method that tracks more subtle fluctuations is more suitable. In addition, there is also the following reason, that is, in the 5th, 6th, and 7th waves, since the 3rd plan, which was significantly different from the previous situation in terms of location and time period, was also used for observation, it would be confusing to record it on the same scale. The following explains them separately.

[0087] First, in the "level method", the amount of virus on the day is determined in five levels from 0 to 4 (hereinafter written as L0 to L4). L0 means that the virus is not felt at all or is extremely small. L1 means that a small amount of virus is felt, and as the number increases, more virus is felt. In addition, since these levels are based on subjective judgment of the body's reaction, the benchmark may fluctuate in the long term.

[0088] Next, in the "comparison method", the amount of virus is compared with the amount at a past time point to determine whether it is more, less, or the same degree. Here, the past "time point" refers to a specific day or a specific week, typically the previous day, 7 days ago (the same day a week ago), and the previous week. In addition, as an application, it is compared with two different time points (for example, set as A and B). When the amount of virus is more than time point A but less than time point B, it is sometimes judged to be the amount between the two. The records in the comparison method do not use symbols like the ranking method, but are left in free-format text. In addition, since it is meaningless to simply compare the amount of virus perceived in different plans, such a comparison is not performed. However, even if the plans are different, the amount perceived under partially overlapping conditions (the same time and place, or adjacent time periods and places where the same degree of virus amount can be perceived even if they are not exactly the same) can be compared if the overlapping range is large enough, so it will also be implemented.

[0089] The advantage of the comparative method is that it can track more subtle fluctuations than the hierarchical method. For example, sometimes the viral load is L2 on one day, and the viral load on the next day is also L2, but slightly more than the previous day. In the hierarchical method, both days are recorded as L2, but in the comparative method, this difference can also be recorded. On the other hand, the advantage of the hierarchical method is that, although it is not as detailed as the comparative method, it can compare the viral load of two appropriate days (however, it does not apply when the two days are far apart and it is considered that the baseline has fluctuated).

[0090] In addition, in the grading method, the determination results such as D1, D2, and F are recorded as exceptions. The inventors initially understood them as the "grading" of the amount of virus, but gradually became clear that they should not be treated equally. However, due to the above process, they are recorded as grades. The following will be explained in detail.

[0091] First of all, D1 and D2 mean that "particularly concentrated viruses" (the throat will suddenly react strongly and become hoarse. There is no such situation in L0~L4) were sensed once and twice on the same day. When this "particularly concentrated virus" is sensed, it is recorded as D1 or D2 regardless of the amount of virus in other places on the same day (L0~L4 or F described later). The characteristics of D1 and D2 are that the amount of virus usually does not change much when moving a few meters. In contrast, the perception of D1 and D2 is sudden, and it can no longer be sensed as long as you leave the scene for a few meters. In addition, it has only been sensed indoors or on the train. The reason is believed to be that the infected person was nearby at that time or in the near past, and the inventor inhaled the exhaled breath of the infected person. In addition, as described later, from August 1, 2020, when recording began, to August 7, 2022, D1 was perceived 4 times, D2 was perceived 7 times, and D3 or above (i.e., "particularly concentrated virus" was perceived more than 3 times in one day) did not occur once.

[0092] Next, F is a classification used only in the early stage of observation (from August 1, 2020, when recording began, to December 2020), and was recorded 9 times, but has not been used since then. In the early stage of observation, the virus was generally detected only in limited places with high population density indoors or on trains, and the amount determined comprehensively was recorded as L0 to L4 levels by visiting those scattered places. However, there were occasional days when a trace amount of the virus was unexpectedly detected even in places with few people indoors, and it was not clear at first what that meant, so it was recorded as F in particular. However, it was later realized that F was just a form of daily fluctuation in the amount of virus in the same place, so it was included in the above-mentioned L0 to L4 levels and was no longer used as a separate record. In other words, the virus can be detected not only in places with high population density, but also in all places (even this was not obvious at first), and just as its amount fluctuates daily in places with high population density, it also fluctuates daily in places with few people. Also, in places where there are few people, the virus is usually too little to be felt, and the days when it increases to a noticeable level are simply identified as F, and should not be treated specially just for this reason. In addition, if such a perception is present from the beginning, it will be recorded as F regardless of the amount of virus in other places. However, in December 2020, there were two times when the level felt in other places and F were recorded at the same time, and the standard fluctuated a little. However, in the following forecast, F is not that important.

[0093] To ensure accuracy, the detailed process is further explained. Initially, since the only physical reaction stronger than L4 was the perception of "particularly concentrated virus", D1 and D2 were processed as "levels" such as L5 and L6, respectively. However, in the third wave, the largest amount of virus on December 24, 2020 exceeded the previous L4 amount, and "particularly concentrated virus" was not perceived. In other words, it means that it is not appropriate to mark D1 as L5, but since the past records cannot be modified, it was recorded as L4 with the annotation "strong virus amount" as a last resort. However, the same situation did not occur when observing using the grade method later (it can be considered that the observation location was changed to an area with less virus amount when the infection spread. In addition, although the virus amount was also strong at the peak of the 6th and 7th waves mentioned above, that is, January 26, July 21, July 25, and July 26, 2022, it was recorded only using the comparison method), so D1 and D2 were processed as levels such as L5 and L6 in the records.

[0094] <Details of this Example: Details of Observation and Prediction (Introduction)>

[0095] In the following, the results of each observation and prediction are described. For the sake of prudence, the inventor did not conduct statistical analysis of the effective reproduction number, etc. In addition, for those who look back on the changes in the infection status since January 2020, when the first infected person was found in Japan, as a determined past, perhaps the prediction of this embodiment seems to be just depicting fluctuations. However, of course, the following situation is emphasized in advance, that is, the inventor is not speculating after the fact, but predicting only the number of new positives up to that day, and even not knowing whether it will increase or decrease afterwards (according to the chart, there is no right side compared to that day). In addition, the prediction in this embodiment is also recorded after 24 o'clock in the evening of the day, but the content is determined on the same day, so it will be explained as the content recorded on the same day. In addition, the prediction written below includes content that is more meaningful than the prediction, as well as content that is predicted incorrectly, and all are recorded. However, in addition to the content written here, observations and records of subtle fluctuations are also made, but they are not read as predictions because they cannot be distinguished from errors, etc., and have been excluded.

[0096] <Details of this example: Details of observation and prediction (delay in the number of new positive cases in the 2nd and 3rd waves)>

[0097] The correlation between the amount of virus in the aforementioned third wave and the number of new positives after 14 days is described. First, although it is not strictly the same as the core concept of the present disclosure, that is, the prediction based on the "amount of virus" in the air (L0~L4 in the hierarchical method), in order to illustrate the delay of 14 days, the prediction based on "particularly concentrated viruses" D1 and D2 is described. However, the perception of D1 and D2 cannot be expected to be accurate as described below, so the prediction is for reference only. Furthermore, it should be noted in advance that the value of 14 days is not universal, and may change due to the existence of mutants with different incubation periods, or the ease of receiving inspections (for example, in Japan, until the situation changed around May 2020, there were many people who did not receive inspections if the fever did not last for more than four days).

[0098] Although the inventor has been recording the amount of virus in the air since August 1, 2020, the second wave (but not every day), it is not believed from the beginning that the number of new positives can be predicted by the amount of virus. About two weeks after D2 was perceived, the number of new positives was noticed to be increasing. Whether the inventor passes by the infected person during the movement depends on chance, so whether D1 and D2 are perceived is largely affected by probability factors (however, it can be considered that the possibility of spreading in places where "particularly concentrated viruses" appear in D2 is higher than that of D1). In addition, for the perception of "particularly concentrated viruses", if the time indoors or in the tram is doubled, the number of times is also about twice, but since such high accuracy was not initially considered necessary, only the number of times was recorded regardless of whether the time out was long or short (on the other hand, if it is the perception of "virus amount", even if the inventor's time out is doubled, the amount will not become twice only in the change of judgment accuracy). For the above reasons, it is stated in advance that the accuracy of the prediction is not high. In addition, on the second day after D2 was perceived, for some reason, the amount of virus was mostly not that much.

[0099] Specifically, D2 was perceived on August 9 (Sunday), September 2 (Wednesday), September 9 (Wednesday), September 10 (Thursday), October 1 (Thursday), October 22 (Thursday), and December 7 (Monday) in the 2nd and 3rd waves of 2020. On the other hand, after the 4th wave, the perception of D1 and D2 did not appear (perhaps this is related to the increase in outdoor observation time after the 4th wave and the increase in vaccinations in Japan. In addition, for reference only, the days when D1 was perceived were December 29 (Tuesday), 2020, February 23 (Tuesday), May 16 (Sunday), and September 5 (Sunday). Although it was initially expected that the number of new positive cases would also increase about two weeks after D1, it can be considered that the perception of D1 alone has a strong random factor and is not helpful for prediction). Not only the date but also the day of the week is written here, because the number of new positive people has characteristics every week. It is said that since the number of tests on Sundays and holidays is low, there is a tendency for fewer new positives on the next day (Monday, etc.), so some of the new positives will be delayed after the third day, or they will recover while waiting for the test and not appear in the statistics. Therefore, when the perceived 14 days after D2 overlaps with these dates, it is difficult to know whether the number of new positives has increased.

[0100] It is difficult to conclude that the perception of D2 is effective as a prediction based on the above records alone. However, from the perspective of the inventor, since it is believed that the second wave will end at the time point of September 2, it is of interest that it will start to expand again about a week later, and it is believed that the perception of D2 is effective in predicting the increase in the number of new positive cases. In particular, in the perceptions of October 1 and October 22 mentioned above, it showed a surge of more than 250 people just 14 days later (October 15 (Thursday) and November 5 (Thursday)), which left a very deep impression, and thus began to pay attention to the number of "14 days" (although it will be repeated, if you look back at the established past, since the number of new positives during this period will fluctuate periodically, if you track its fluctuations, it may seem that prediction will be easy. However, that was not known until later, and at the time of perception, all futures are uncertain. In addition, since the results of the perceptions on September 9 and September 10 were September 23 (Wednesday) and September 24 (Thursday), which ended the consecutive holidays 14 days later, it can be considered that it is difficult to appear in the statistics. It may be reflected in the sharp increase in the number of new positives on September 26 (Saturday), but it is not clear yet). In addition, in the perception on December 7, 2020, the infection had spread and the number of new positive cases exceeded 300 people. Since the situation was different from the period when D2 was perceived previously (from August 9 to October 22, 2020), it was difficult to judge what the number of new positive cases would be like on December 21, 2020 (Monday), 14 days later. As a result, although an increase was predicted, it was incorrect.

[0101] However, in the observations of the 2nd, 3rd, and 4th waves, the perception of a sharp increase in D2 or the amount of virus, and the sharp increase in the number of new positives 14 days later, for some reason, mostly occurred on Thursdays. According to the literature, there is also an explanation that "there are fixed days in a week for the total processing of the number of new positives, and as a result, the number of new positives on Thursdays tends to increase" (for example, https: / / www.asahi.com / articles / ASNBY7316NBPUTIL01B.html). However, this explanation alone cannot explain that even on the same Thursday, there are days with significant numbers and days with insignificant numbers. In addition, in this embodiment, a sharp increase other than Thursday is also predicted, as described later on March 3, 2021 (Wednesday). However, if the explanation of this document is also a major reason, if there is no fixed day for the total processing, then there is a possibility that a delay such as "14 days" will be confused before and after, or it will become a value with a range such as "13 to 15 days".

[0102] <Details of this Example: Details of Observation and Prediction (Wave 3)>

[0103] After that, we return to the prediction based on "virus amount". The inventors could not predict the increase in the number of new positives in the approximately two weeks starting from November 7, 2020 (Saturday) (until around November 21 (Saturday) when the record of 539 people was set) as the infection spread (although the reason for the increase is unknown, it may be related to the fact that the mainstream of the virus in the country was replaced from B.1.1.284 to B.1.1.214 at almost the same time). The reason for not predicting the increase is that the increase in the number of new positives after D2 was perceived in the past was temporary. In addition, no special changes in the amount of virus were perceived 14 days ago, that is, from October 24 (Saturday) to November 7 (Saturday). However, on November 16 (Monday), taking the opportunity that 3 of the past 4 days were L0, when looking back at the virus amount in the last 14 days (November 3 (Tuesday) to November 16 (Monday)), it was subjectively basically the same. Judging from the records, there are more L0s. In addition, if slight fluctuations are removed, it can be considered basically fixed (there are more L0s, among which levels L2, L3, L4, and F can be seen everywhere). At that time, because the core concept of the present disclosure had not been clearly achieved, it was simply believed that if there were more L0 days, the number of new positives would also decrease. Therefore, a prediction was made in the form of two possibilities: "The number of new positives will decrease in the next 14 days (November 17 (Tuesday) to November 30 (Monday)). There is also a possibility that the situation will remain the same." As a result, if the fluctuations on each day of the week are removed, it can be considered that it is basically the same. Although the prediction for the period from November 7 to November 30 has the disadvantage of not being able to foresee an increase in the number of new positives exceeding 500 as mentioned above, the inventor saw the result and not only considered predictions based on D2, but also began to consider predictions based on the amount of virus, that is, began to consider the assumption that "if the amount of virus is the same, the number of new positives after 14 days will also be the same."

[0104] In addition, the virus was first detected outdoors in late November 2020 (before that it was only detected indoors or on trains), and since then the amount of virus outdoors has also been included in the observations.

[0105] Next, on December 3, 2020 (Thursday), the virus load increased further, reaching the highest level in the past month. However, since the prediction of the virus load is at the hypothetical stage, it is predicted in a slightly conservative form that "the number of new positives in two weeks may increase by about one level." In fact, 14 days later, on December 17 (Thursday), the number of new positives increased sharply to 822, which was the largest number in the past month. This suggests that the sharp increase in the virus load is not only reflected in D2, but also in the sharp increase in the number of new positives 14 days later.

[0106] After that, on December 24 (Thursday), the amount of virus increased sharply. Since it was an amount that had not been perceived so far, it was recorded as L4 with the annotation "strong amount of virus" (the amount on this day was the largest in the third wave of observation). Moreover, it decreased significantly on the next day, December 25 (Friday). At this point in time, the infection spread to an unprecedented level, and the situation was different from the past. It was not certain that the peak of the number of new positives could be predicted with the peak of the virus amount (sharp increase and subsequent sharp decrease), and it was only recorded as "the number of new positives may increase on January 7, 2021 (Thursday)". In fact, as mentioned above, the number of new positives on January 7 was also the largest.

[0107] In addition, although it is for reference only, at the time point of January 31, 2021 (Sunday), it is recorded as "The current number of infections can be considered to be roughly the same as in mid-December 2020. The current number of infections in Tokyo is also roughly the same as in mid-December." Although it seems that the same words are repeated at first glance, the first half means that the "virus amount 14 days ago" in "now" and "mid-December" is subjectively roughly the same. On the other hand, the "number of infections" in the second half means "the number of new positives." In other words, the main point of the entire record is the assumption that the number of new positives will be the same 14 days after the day when the virus amount is confirmed to be the same. However, even if the records of levels are compared, it cannot be verified (even if you look at the virus amount on January 17, 2021 (Sunday), and the record of the virus amount from November 26 (Thursday) to December 6 (Sunday), 2020, it is difficult to compare). In addition, since this record is not a forecast but a post-confirmation, it is for reference only.

[0108] In addition, on January 20, 2020 (Wednesday), it was recorded that "the number of infections has remained high for about a week, and it has not improved or worsened. It seems to have improved in the past few days" (since it is more for the purpose of recording than for prediction, the expression is vague). In addition, the "number of infections" here means "the number of new infections." In other words, the record reviews the amount of virus in the past week and summarizes the following situation, that is, L4 was recorded from January 13 (Wednesday) to 15 (Friday), and L3 was recorded on the 17th (Sunday), 19th (Monday), and 20th (Wednesday) (no observation was made on the 16th and 18th). However, judging from the number of new positives from January 27 (Wednesday) to February 3 (Wednesday) 14 days later, it has decreased compared to the past week. Although it is a text with the main purpose of recording rather than prediction, it is difficult to say that it is correct as a prediction.

[0109] <Details of this Example: Details of Observation and Prediction (Early Stage of the 4th Wave)>

[0110] Next, the turning point from the third wave to the fourth wave is described. At the end of the third wave, a decrease in the amount of virus was seen everywhere from around February 1, 2021, and the amount of virus was particularly low from February 12 (Friday) to February 21 (Sunday) (only L0 to L2). However, it increased to L3 on February 22 (Monday), and then after D1 was sensed on February 23 (Tuesday), L3 to L4 continued until March 1 (Monday). Therefore, on February 23, it was only predicted that "the number of new positives may increase slightly on March 9 (Tuesday), 14 days later", and as described later, a slightly more detailed prediction was made on March 8 (Monday). And in fact, 14 days later (February 26 (Friday) to March 7 (Sunday)), when the virus was particularly low, the number of new positives was close to minimal.

[0111] From around March 2 (Tuesday) to April 10 (Saturday), 2021, we will prevent the pathogenicity of the alpha variant and switch to simple observations such as not going into the crowd. There will also be days when no observations are made. In addition, after this period, the number of records based on the grading method has decreased.

[0112] The virus increased further on March 3, 2021 (Wednesday), which can be considered to be close to the amount 14 days ago, when the number of new positives was about 400 to 500 (although there is no record, it can be considered to be around early December 2020). Although it is subjective, when it is judged to be closer to 500 than 400, it is predicted that "the number of new positives on March 17, 2021 (Wednesday) 14 days later will be about 500 people." Although the result is 409 people, it is a high accuracy as a prediction based on human senses, and it strongly supports the assumption that the number of new positives 14 days later can be predicted by the amount of virus. In addition, it is about a month since the number of new positives exceeded 400 (since February 18 (Thursday)), and the graph also shows a characteristic increase.

[0113] <Summary of this Example>

[0114] The details of the observation and prediction results after the 4th wave, and the 6th and 7th waves will be left to Examples 2 to 4, and this example is summarized here. As described so far, although it is an imprecise method based on observation and prediction based on human senses, by simply investigating the amount of virus in the air inhaled by a person while walking, the increase and decrease, peak value, and number of new positive people in the entire Tokyo metropolitan area can be predicted to some extent.

[0115] In addition, in this embodiment, infection control is not implemented. However, of course, if the infection status can be inferred and predicted, infection control such as ensuring beds can be implemented in advance. For example, at the time point when it is predicted on March 3, 2021 that "the number of new positive people on March 17, 14 days later, is about 500", it is sufficient to introduce measures such as improving the medical system as soon as possible. In addition, if measures to prevent the spread of infection are taken at the time point when the increase in the virus is perceived around February 23, 2021, the fourth wave of the epidemic can be prevented or delayed. In addition, not only during the infection expansion period, but also during the reduction period, controls such as increasing the amount of going out in advance to resume economic activities can be implemented.

[0116] In addition, if inference or prediction can be made, it will have a positive impact on people's psychology. If inference or prediction cannot be made, even if you know that "infection control should be carried out in advance 14 days ago" after the number of new positive cases increases, it is not clear "So now, as a group or as an individual, what should be done specifically", and people will become negative psychologically, and infection control will be easily loosened little by little. In contrast, if inference or prediction can be made in advance, it will be clearer what should be done now, and countermeasures will be taken proactively, which is a great advantage.

[0117] In addition, as previously written, the number of new positives is a secondary amount of the delayed performance of the number of new infections in the past, so this embodiment is essentially an inference of the number of new infections. So why can the amount of virus be used to infer it? The first assumption is that for the amount of virus in the air, the virus released by the nearest (i.e., the same day or a few days ago) newly infected person in the adjacent area accounts for most of the virus, and as a result, the amount of virus can be used to infer the number of new infections. When a person is infected with a virus, the following process will be pursued, that is, the amount of virus in the body changes over time, and the initial less amount of virus will increase, and reach a maximum after a few days, and then decrease again. Therefore, it can also be considered that for the virus in the air, the virus released on the day when the amount of virus in the body of the nearest newly infected person reaches the maximum accounts for most of the virus. If this assumption is correct, when the amount of virus in the air is divided by the amount of people in the adjacent area, an amount roughly proportional to the number of new infections in the area can be obtained. However, in the case of the coronavirus, it is said that the time when the amount of virus in the body reaches its maximum is roughly equal to the date of onset (in a study published on March 23, 2021, the maximum amount of virus excretion is reached two days after onset: https: / / www.amed.go.jp / news / release_20210323-02.html). Therefore, the 14-day delay observed in the second and third waves is roughly the same as the number of days from onset to the announcement of the number of new positive cases, but slightly longer than the empirical feeling.

[0118] The second assumption is that, although the details will be described in the second embodiment, the amount of virus in the air represents the number of new infections of the day who inhale the virus and are infected. If the perception of D1 and D2 described in this embodiment is taken as an example, the inventor believes that there are other people who bring concentrated viruses into the body on the days when D1 and D2 are perceived, and there are even people who are infected for this reason. Moreover, it can be said that for this number of people, the number of people who perceive the day of D2 is more than the number of people who perceive the day of D1. Similarly, on days when the inventor perceives a large amount of virus, other people in the neighboring area are also exposed to a large amount of virus, among which there are also occasional people who are brought into the body and infected (close to the infected person, etc.). Moreover, it can be said that the more the amount of virus perceived by the inventor, the higher the probability of infection, and the more the number of new infections increases (where the amount of people in the neighboring area is the same). If it is assumed that this assumption is correct and the second embodiment can be applied, then when the amount of virus in the air is multiplied by the amount of people in the neighboring area, an amount roughly proportional to the number of new infections in the area can be obtained. When based on this assumption, the estimation of the number of new infections in this embodiment can be said to be an approximation that sets the number of people to be fixed.

[0119] In addition, it is possible that there is a relationship between the amount of virus in a more complex mechanism and the number of new infections, but this is not certain at the current time point.

[0120] In addition, although the amount of viruses in the air may decrease on rainy or windy days, the accuracy of human perception will not allow for a difference beyond daily fluctuations. However, when there is fresh wind, the decrease in viruses may only be felt during that period.

[0121] Here, a comparison with several prior arts is made. First, there are already mechanisms that measure the amount of PM2.5, photochemical oxidants, pollen, etc. in the air and issue alarms. They determine whether the target substance is detected to a level that is harmful to the human body. In contrast, the method disclosed in the present invention is characterized in that it does not take the amount of detected viruses (more generally, pathogens) as a question of whether it is harmful to the human body. That is, instead of focusing on the absolute amount of the virus, it focuses on its fluctuations. Even if the detected virus is a very small amount, it can be indirectly inferred from this how many new infections there are in the surrounding area (in this embodiment, the entire Tokyo metropolis). This feature is considered to be one of the barriers to achieving the concept of the present invention (in infection countermeasures, we are interested in the amount of virus that is dangerous to infection, and there is no need to accurately obtain the amount of virus when there is no danger). As another barrier, the following can be cited, namely, PM2.5 in the air is a "cause" of harm to the human body, but not a "result". In contrast, viruses in the air are a "cause" of harm to the human body (if the virus enters the body, it will be infected), and are also a "result" (the virus is released from the infected person). Therefore, attention is sometimes turned to the "result" that PM2.5 and the like do not have. For example, since there is no worry about infection when the virus is in trace amounts, it is easier to focus only on the "result", that is, the presence of an infected person nearby, than on the "cause". The second assumption and the second embodiment described above are characterized in that the focus is on the "cause" here. On the other hand, when focusing on the "result", it can also be considered that it is not obvious whether it is related to the "number of new infections" as in the first assumption, and in any case, it is natural to think that it is related to the current "total number of infections".

[0122] Second, technology has been developed to measure the amount of coronavirus in sewage and infer or predict infection status. This technology also does not focus on the "cause" aspect of the virus, but on the "result" aspect.

[0123] Third, there may be an idea to sample the air in public transportation such as trains to determine the presence of the virus, but it is believed that the idea of ​​inferring the number of new infections based on the amount of virus has not yet been considered.

[0124] <Supplement: About the inventor’s constitution and the perception of coronavirus based on the human body>

[0125] The inventor can sense the presence of coronavirus (or its residue) in the air, including the concentration. Although the indirect proof is a prediction of this embodiment, it is an indisputable fact for me (the original inventor is not a person with strong skin or mucous membranes, but it is believed that the reason may be that the body is also prone to react to coronavirus). When the concentration of the virus is high, there will be a unique stimulation at the moment of inhalation (the feeling of foreign matter passing through the respiratory tract. I have never felt it before the coronavirus pandemic). In addition, outdoors, for the concentration of the virus in that place, if there is no wind, you can stop and take a few breaths to feel the discomfort deep in the throat (possibly caused by inflammation. It reaches a different part from the part that causes pain due to the common cold). If the discomfort is mild, you can also feel that it will recover in a few tens of seconds. Moreover, if you walk outdoors while paying attention to the changes in the discomfort in the throat, you will know that the concentration of the virus has changed in most cases within a period of moving about ten to several tens of meters. For the inventor, you can sense the concentration of the virus like visually confirming the concentration of smoke.

[0126] Of course, the above physical reactions can also be explained by something other than the virus. However, if it is caused by environmental pollutants, for example, it cannot be explained that it is more indoors than outdoors (but it is not often because it depends on the location or time period). On the contrary, if it is a causative substance such as sick building syndrome, it is difficult to think that it can be felt in large quantities even outdoors. Moreover, it is natural to think that substances derived from people are felt more in crowded places. Moreover, before the pandemic, it has never been felt for such a long time, and if the intensity of the reaction is linked to the epidemic, it is most appropriate to think that it is the coronavirus.

[0127] Moreover, if the mutant strains are different, there are cases where the body's reactions when introduced are different. If the amount is appropriate, those mutant strains can be distinguished. For example, when a large amount of mutant strains (believed to be the Delta mutant or the strain of the B.1.617 lineage to which it belongs) are introduced into the body, diarrhea or frequent urination will occur. In addition, when a large amount of mutant strains (believed to be the Alpha mutant) are introduced into the body, reactions will occur on the left chest, left armpit, left toe, etc. The mutant strains (believed to be the Omicron mutant) that are more common in the 6th wave also have reactions similar to those in the 4th wave at the early time point.

[0128] However, the inventor was not sure from the beginning that he could sense the coronavirus. The cause was that he caught a cold in February 2020 (it was thought that he might have been infected with the coronavirus). The high fever lasted for nearly a week, and after the fever subsided, the uncomfortable state of the throat continued for several months. For example, when he was talking, he suddenly felt a "particularly strong virus". The inventor initially thought that the cause was that the virus still remained in the throat and did not disappear. However, when the emergency measures implemented in the suburbs of Tokyo from April 7, 2020 were lifted on May 25 and the number of people going out increased, the condition of the throat that had improved once frequently worsened when going out. In particular, there were many cases where the condition worsened in stores that were stopped by, but if we consider that this was because the number of people increased and the amount of virus in the store increased significantly, there is an explanation. After that, we also observed the conditions for the body to react when going out, and gradually reacted to the virus in the air and there was no doubt that the condition of the throat deteriorated.

[0129] On August 1, 2020 (Saturday), in the suburbs of Tokyo, the number of new positives in the second wave is increasing, but for the inventor, about a week before that, it was a period when the virus was hardly felt when going out. Since the virus in the air is a mixture of viruses released by an unspecified majority of people, its amount is small, and statistically there should be fewer infected people (however, at that point in time, it is believed that the amount of virus is not related to newly infected people but to all infected people). If that is the case, it is believed that the peak of the infection expansion has passed, so it is recorded. In addition, the maximum number of new positives in the second wave is 472 people on August 1. This is the initial prediction of this embodiment.

[0130] [Example 2]

[0131] In this example, observations and predictions from the middle of the fourth wave onwards are described. Most of the predictions in this example are not as clear-cut or wrong as those in Example 1 (although the reason is unknown, it may be related to the increase in the proportion of alpha variants, which are very different in nature from the past, or the popularization of vaccination), but for the sake of fairness, all of them are recorded.

[0132] <Details of observation and forecast (mid-term and beyond of the 4th wave)>

[0133] On March 8, 2021 (Monday), if we look back at the viral load over the past 14 days, as mentioned above, the period from February 22 (Monday) to February 28 (Sunday) (strictly speaking, until March 1 (Monday)) increased compared to the previous week. After that, including the aforementioned March 3, the viral load increased further, but the viral load in the days before March 7 (Sunday) and on March 8 was lower than on March 3. Therefore, the prediction is "This week (March 8 (Monday) to March 14 (Sunday)) will increase slightly, and next week (March 15 (Monday) to March 21 (Sunday)) will increase further. After that (after March 22 (Monday)), it will stabilize (although the expression "stabilize" is vague, it means "decreasing")". As a result, the prediction for "this week" can be said to be correct as written above. The next "next week" is also close to the correct answer if we look at the graph of the number of new positive cases, but it is incorrect for "after that". However, although it is a postscript, based on the observation of the amount of virus, the period referred to by "afterwards" should not be "after March 22", but "the recent days including March 22". In other words, if the prediction is "next week (March 15th to March 21st) will increase around Wednesday, but will be stable in the days before March 22nd", it will be considered closer to the correct answer.

[0134] On March 17, 2021 (Wednesday), the virus load was the same level as on March 3, so it was predicted that "the week of March 31 (Wednesday) 14 days later (around March 29 (Monday) to April 4 (Sunday)) will have roughly the same number of new positives as the week 14 days after March 3 (around March 15 (Monday) to March 21 (Sunday))". Since the results are not the same but have an increasing trend, it is difficult to say that it is the correct answer (and, although it is a postscript, it is also wrong to predict the second half of the week based on the virus load in the first half of the week). However, the number of new positives on March 31 (Wednesday) was 414, which is roughly the same as the 409 on March 17 (Wednesday). In addition, on March 19 (Friday), which was a day off, the virus load was still the same, although it was slightly less than on March 17. The number of new positives on April 2 (Friday), 14 days later, was 437 (although it was initially announced as 440, it was later revised), which is still the same level as 414. In addition, although the amount of virus on March 18 (Thursday) was very low compared to the days before and after, the number of new positives on April 1 (Thursday), 14 days later, was 475, which was more than the days before and after. This can also be explained by the increase in the number of people who were diagnosed as positive 15 days after being infected on March 17 and those who were diagnosed as positive 13 days after being infected on March 19.

[0135] On March 21 (Sunday), 2021, looking back at the virus load in the last 14 days (March 7 (Sunday) to March 20 (Saturday)), the prediction is "There will be a slight decrease in the first half of this week (March 21 (Sunday) to 27 (Saturday)), and an increase in the second half. Next week (March 28 (Sunday) to April 3 (Saturday)) will be the same as or increase more than last week (March 14 (Sunday) to March 20 (Saturday))." The reason is that, first of all, in the first half of this week (March 7 to 13), the virus load around March 8 was not that high, but it increased subjectively afterwards. In addition, regarding the second half of this week (March 14 to 20), the virus load from March 18 to 20 was subjectively added to the aforementioned prediction recorded on March 17 (March 19 will also be as high as on March 17). The results for "this week" are difficult to verify because the changes within a week fluctuate depending on the day of the week, but since the number of new positive cases throughout the week is on the rise, it feels like a half-correct answer. Regarding the "next week" part, although it is correct in the sense of increasing, the degree of increase is greater than subjective, which is unexpected.

[0136] On April 6, 2021 (Tuesday), it was recorded as "the amount of virus is very high and it is increasing every week" (the expression is vague because it is more for the purpose of recording than for prediction). The results show that the maximum amount observed on this day was the maximum amount of the fourth wave, but there was no recognition of the peak of the virus amount at that time due to reasons such as interval observation. In addition, although the number of new positives in the next 14 days can be said to be "increasing week by week", the number of new positives on April 20 (Tuesday), 14 days later, was 710 (although it was 711 when it was initially announced, it was later revised), which was not the maximum value of the fourth wave.

[0137] In the following period from April 11 (Sunday) to May 16 (Sunday), 2021, the amount of virus fluctuated in a complex manner. In addition, the predictions for this period were incorrect. Although the reason is unclear, it may be related to the fact that the mainstream of the virus has changed to the alpha variant (according to statistics from the Tokyo Metropolitan Government, the proportion of N501Y variants, including alpha variants, has increased sharply from less than 10% to about 30% since the week of March 29 (Monday), 2021. Furthermore, it has increased sharply to about 60% since the week of April 19 (Monday), 2021: https: / / www.bousai.metro.tokyo.lg.jp / _res / projects / default_project / _page_ / 001 / 013 / 860 / 47kai / 2021052708.pdf). In addition, the incorrect answers during this period, in turn, indicate the specificity of the correct answers or similar results predicted in other periods. Each prediction and its results are described below.

[0138] The amount of virus decreased significantly from April 11 (Sunday) to April 18 (Sunday), 2021 (14 days later, it was April 25 (Sunday) to May 2 (Sunday)). Therefore, the forecast for April 16 (Friday) was "the number of infected people will decrease rapidly with the peak in the next week (until April 24 (Saturday)). If the incubation period of the alpha variant is shorter, it will start to decrease from a little before that." However, this is incorrect because the number of new positives continued to increase from April 25 to May 2.

[0139] In addition, April 29 (Wednesday) to May 5 (Wednesday), 2021, are long consecutive holidays (April 30 (Thursday) is a weekday). It is believed that the sharp decrease in the number of new positive tests around May 5 (Wednesday) is due to the decrease in the number of tests due to the consecutive holidays.

[0140] The virus level will increase again from around April 19, 2021 (Monday) (14 days later, around May 3 (Monday)). Therefore, the forecast for April 21 (Wednesday) is "increasing from May 3". This forecast is based on the premise that the number of new positive cases will decrease until May 2, and as mentioned above, it is difficult to verify because it is considered to include statistical confusion caused by consecutive holidays, so it is difficult to say that it is the correct answer (it is incorrect if you only look at the numbers).

[0141] On April 30, 2021 (Friday), looking back at the recent viral load, it was recorded that "the viral load last week and this week (April 19, 2021 (Monday) to April 30, 2021 (Friday)) is basically the same." Although this is not a clear prediction, if it is interpreted as "the number of new positives from May 3 (Monday) to May 14 (Friday) 14 days later will also be the same except for the fluctuations on the day of the week", it is difficult to say that it is correct (although it is difficult to verify like the previous prediction, it is incorrect if you only look at the numbers).

[0142] In addition, from May 11 (Tuesday) to 13 (Thursday), 2021, the amount of virus was second only to April 6, which was the maximum of the 4th wave (in addition, signs of increase appeared around May 9 (Sunday)). Then on May 16 (Sunday), since D1 was perceived as mentioned above, it was predicted that "the number of new positive cases from May 23 (Sunday) to May 30 (Sunday) (14 days after the signs of increase) will increase significantly." However, in fact, it has decreased significantly, which is incorrect. In addition, there is another point on May 16. Looking back at the amount of virus in the past week starting from May 2 (Sunday), it was subjectively predicted that "the number of new positive cases from May 16 (Sunday) to May 23 (Saturday) will be basically the same or increase slightly." Since the changes within a week fluctuate every day of the week, it is difficult to verify, but since it is considered to be decreasing, it is also incorrect.

[0143] [Example 3]

[0144] In this embodiment, observation and prediction in wave 6 are described. In wave 5, no prediction is performed as described above.

[0145] <Details of observations and forecasts (Wave 6)>

[0146] In the sixth wave, which began around early December 2021 and was dominated by the Omicron variant, observations were made with more attention paid to larger fluctuations such as weekly fluctuations rather than minute fluctuations on a daily basis. However, if there were days with characteristic viral loads, they were also recorded. In addition, to prevent the highly infectious Omicron variant, only intermittent observations were made until January 17, 2022 (since the characteristics of the variant were known after January 18, observations were made almost every day). In addition, in the sixth wave, records were made using the "comparison method" rather than the "grading method."

[0147] In addition, although the "delay period" until the perceived amount of virus is reflected in the number of new positives was set to "14 days" in the 2nd, 3rd, and 4th waves, more than half a year has passed since then, and the ease of receiving inspections or the statistical system may have changed compared to then. However, the only information on hand is that the incubation period of the Omicron variant is about 3 days, which is about two days shorter than the previous strain of about 5 days (https: / / www.niid.go.jp / niid / ja / diseases / ka / co rona-virus / 2019-ncov / 2484-idsc / 10434-covid19-43.html). In addition, considering that even if the system changes, it will not change significantly, the forecast is temporarily set to "12 days" in the 6th wave. In fact, although it cannot be determined clearly, if we look back at the forecast and its results, the most likely one is "8 days."

[0148] In addition, April 29 (Friday) to May 5 (Thursday) 2022 is a long holiday (although only May 2 (Monday) is a weekday, it can be considered that many people are on vacation), but the number of new positive cases increased from May 7 (Saturday) to May 13 (Friday) "8 days later" (meaning that it was more than the same day of the week in the previous week). In contrast to the decrease in the period before and after, if this is due to the increase in the number of people going out due to the long holiday, it can be considered that the new infections during the long holiday appeared in the statistics as new positives 8 days after infection. Even in the same 6th wave, the mainstream of the Omicron variant changed from BA.1 to BA.2 during the peak period (January to February 2022) and the long holiday, but this result became a benchmark for obtaining the delay period.

[0149] However, in Example 1, two hypotheses are described as reasons why the number of new infections can be inferred from the amount of virus. If the first hypothesis, the explanation based on the "amount of virus released by the newly infected person" and the statistics during the large-scale consecutive holidays are combined, the "delay period" until the inventor's virus perception is manifested in the number of new positives must be a few days shorter than 8 days (the number of days until the amount of virus released after infection reaches the maximum. If it is set to be equal to the incubation period, it is about 3 days), which is contrary to the results described below. Compared with the first hypothesis, this supports the second hypothesis, that is, the explanation based on the "amount of virus brought in by the newly infected person".

[0150] In the sixth wave, based on the spread of the overseas Omicron variant and the rate of increase in the number of new positives in Japan in the earliest period (until around early January 2022), it is conceivable that the infection will spread rapidly. Therefore, compared with the slight fluctuations in the amount of virus, the observation was started with the main purpose of when and to what extent it would increase. Subsequently, on January 26, 2022 (Wednesday), after the start of the sixth wave of observation, the amount of virus increased significantly (it can be called strong), and the difference with the days before and after was also large. Therefore, on the next day, January 27 (Thursday), it was predicted that "the number of new positives will reach the maximum on February 7 (Monday), 12 days after January 26." However, since Tokyo Metropolitan Government began using "suspected patients (deemed positive)" from January 29, 2022 (Saturday) (the Ministry of Health, Labor and Welfare announced the recognition of suspected patients on January 24 (Monday). See https: / / www.tokyo-np.co.jp / article / 159276, etc.), the test is saturated, so it is not expected that the predicted results will be clearly shown. The actual number of new positives peaked on February 2 (Wednesday), seven days later.

[0151] Next, since January 31, 2022 (Monday) did not have the same volume as January 26, but showed a sharp increase, the prediction was that "the number of new positive cases on February 12 (Saturday) will be a smaller maximum value than on February 7" 12 days later. As a result, it was incorrect because it decreased on Saturday. In addition, since "12 days later" is a temporary value, when observing February 8 (Tuesday) mentioned above, although it increased compared to the previous day, it is difficult to verify because it cannot be distinguished from the fluctuations on each day of the week.

[0152] In the 6th wave, the rate of decrease in the amount of virus after the peak was slower than the rate of increase before the peak. Therefore, on February 11, 2022 (Friday), it was recorded as "the decrease in the amount of virus is a little bit", and the number of new positives was specifically predicted to be "less than 10,000 people" in the sense of 9,000 to 10,000 people (however, this is not the number of new positives on a specific day in the future, but the average number of people at that time. In addition, at this point in time, since the delay of the aforementioned "12 days" is likely to be corrected, there is no prediction of how many days later). The basis of this prediction is that the amount of virus around that day (around February 11) is subjectively less than half of the peak of the 6th wave (around January 26), so under the premise that the amount of virus and the number of new positives have a proportional relationship, it is a very rough result obtained from the number of new positives around the peak (around 20,000 people), but since it is still in the record, it should be recorded. The results showed that the seven-day average number of new positive cases on February 19 (Saturday), eight days after the predicted date, was about 14,000, about 40% to 60% more than predicted.

[0153] The amount of virus increased on February 15 (Tuesday), February 17 (Thursday), and February 18 (Friday) compared to the previous week. Initially, it was thought to be a random increase and no special attention was paid, but it was seen that it continued to appear except on February 16 (Wednesday), so on February 18, it was recorded as "the amount of virus began to increase from February 15". In addition, the amount of virus increased further on February 20 (Sunday), although not as much as on January 26, it was also comparable to it. Since it dropped significantly on the following February 21 (Monday), it was recorded as "the amount of virus on February 20 was the second maximum in the 6th wave". Although it is difficult to verify these predictions, first of all, on February 23 (Wednesday), 8 days after the amount of virus began to increase, the number of new positives increased characteristically (the difference with the number of new positives on February 22 (Tuesday) and 24 (Thursday) before and after was greater than the fluctuation of each day of the week). However, on Monday, February 28, eight days after the second maximum day, although the number of new positives on Monday was higher than the days before and after, there was no clear increase. In addition, the 7-day average of the number of new positives during the period from February 23 to 28 did not increase clearly. However, since it turned from a decrease to a flat state, it is believed that the increase in the amount of virus is reflected in the statistics to some extent.

[0154] On February 27, 2022 (Sunday), although the reason is unknown, the amount of virus was particularly low (the lowest since the peak of the 6th wave, and the decrease from the recent days has also been significant), and if compared with recent days, it can be said to be zero. However, since the new positive number is smoothed, it can be considered that although it is not zero, there will be days in the future when it will be significantly lower than the previous day. In fact, on March 7 (Monday), 8 days later, the number of new positives decreased significantly. This is a 42% decrease compared to the previous day, March 6 (Sunday), and although there is a trend of fewer numbers on Mondays, it can be said that the amplitude is larger than the fluctuation of each day of the week (since entering 2022, the Monday with the largest decrease compared to the previous day so far is February 21 (Monday), a decrease of 32%).

[0155] During the rest of the sixth wave, the amount of virus increased and decreased repeatedly, but there were no major fluctuations. Based on past experience, it is difficult to predict the number of new positive cases during such a period, so only observations were made.

[0156] [Example 4]

[0157] In this embodiment, observation and prediction in wave 7 are described. In wave 7, observation was performed according to the third plan, and in particular, the observation start time from July 7 (Thursday) to August 7 (Sunday) was 16:25 to 16:50.

[0158] <Details of observations and forecasts (Wave 7)>

[0159] In the 7th wave, the amount of virus began to increase continuously from the beginning of July. Subsequently, July 13 (Wednesday) was the day with the highest amount of virus since March in the 6th wave (however, less than January 26, 2022), which was very different from the days before and after. Although the "delay period" in the 7th wave is not clear, it is assumed to be close to the "8 days" with the highest probability in the 6th wave, so on the next day, July 14 (Thursday), it was predicted that "July 21 (Thursday) will be the tentative peak of the number of new positives in the 7th wave." However, when it came to July 21 (Thursday), the amount of virus increased further (a strong amount comparable to January 26), which was very different from the days before and after. Therefore, on the next day, July 22 (Friday), the tentative forecast was updated to "July 29 (Friday) will be the tentative peak of the number of new positives." We thought that was the end of it, but on July 25 (Monday), the virus level increased again significantly from the previous day, and after reaching the same or slightly lower level as on the 21st, the virus level increased further on July 26 (Tuesday), exceeding that on the 21st, and decreased significantly the next day, July 27 (Wednesday). Therefore, on the 27th, the tentative forecast was updated to "August 3 (Wednesday), eight days after the 26th, as the tentative peak of the number of new positive cases." From then on until August 7 (Sunday), the virus level was not detected to exceed that of July 26th.

[0160] The above predictions were verified. First, for the first tentative estimate, the number of new positives was 34,995 on July 22 (Friday), which was actually the maximum, one day later than predicted. Secondly, for the second tentative estimate, the number of new positives was 40,406 on July 28 (Thursday), which was actually the maximum, one day earlier than predicted. For the last tentative estimate, although the number of 38,940 on August 3 (Wednesday), the predicted day, was the maximum, it was about 4% less than July 28, and as a result, the maximum number of new positives in the 7th wave was on July 28. In addition, regarding the increase in the amount of virus on July 25, the day before the last tentative estimate, the number of new positives on August 2 (Tuesday), 8 days later, was 30,842, which did not show a special increase.

[0161] [First embodiment]

[0162] First, the terms common to all embodiments are defined. In addition, as a notation, x*y represents multiplication, x / y represents division, exp(x) represents an exponential function with a natural constant as the base, X∩Y represents intersection, and X∪Y represents union. In addition, in values ​​represented by lowercase letters i or j at the end (VAi, ej, ei', etc.), i and j represent subscripts unless otherwise specified.

[0163] <Region 6>

[0164] In this disclosure, implementation is in what is referred to as "Region 6" ( Figure 3 . An area of ​​a street, a city, a county / state, a country, etc. It is usually three-dimensional and can move or deform over time. An example of movement is a train or ship) of people in a group.

[0165] Generally speaking, non-residents of area 6 may enter and exit area 6. Since infection with pathogens is not related to whether or not one resides in the area, it is ideal that non-residents are included in the target group as long as they have stayed in area 6 for a period of time for infection countermeasures in area 6.

[0166] <Infection status, infection status>

[0167] The infection status of a group of people to pathogens is called the "infection status" of the group. In addition, the infection status of a group in a certain area is also called the infection status of the area. Usually, it is difficult to fully grasp the infection status, and only part of the information can be obtained.

[0168] In addition, the status related to an individual's infection is referred to as "infection status." This refers to whether the individual is infected, how much transmission material (typically the pathogen itself) is released to the body when infected, and other status.

[0169] <Pathogens, Pathogens>

[0170] There are many types of pathogens, such as influenza virus and tuberculosis bacillus. In addition, even the same influenza virus, such as H1N1 type and H5N1 type, generally has multiple subtypes. In the following, the type of pathogen and the subtype within the same type are collectively referred to as "pathotype". For example, the pathogen type "influenza virus" has subordinate pathogen types such as "H1N1 type" or "H5N1 type". In addition, although "influenza virus" and "tuberculosis bacillus" are different pathogen types, if the upper pathogen type "pathogen" is considered, they all become its subordinate pathogen types.

[0171] In the present disclosure, the route of transmission of pathogens between people is not particularly limited, and there may be airborne infection, droplet infection, contact infection, etc.

[0172] <Communication material, communication type>

[0173] The "transmission material" of a pathogen having a certain pathogen type X (referred to as "pathogen X") refers to a substance indicating that a source of infection of X is nearby. Generally, there are multiple types of pathogens, such as the pathogen itself, the remains or debris of the pathogen (a denatured pathogen or a part of the pathogen), all substances released by the pathogen, all substances released by a person infected with the pathogen, and even substances in which the above substances have been denatured during dispersal.

[0174] In addition, when a certain transmission substance exists in the air, even if the substance is the same, it is sometimes desirable to distinguish its form. For example, it is generally believed that the infectiousness of a pathogen is different when it is contained in droplets and when it is in the form of droplet nuclei. It is meaningful to distinguish these in infection countermeasures.

[0175] The difference in the substance or form of the transmission agent is called a "transmission type", and the transmission agent having a transmission type X is called "transmission agent X". Pathogen X itself is also a transmission agent and is represented by a transmission type with the same name X.

[0176] For example, when pathogen X is in a floating state, in order to distinguish whether it is infectious or inactivated, it can be expressed as transmission type XL and XD respectively. In addition, in order to distinguish the state contained in droplets and the state of droplet nuclei, it can be expressed as transmission type Xd and Xa respectively. Moreover, for example, the state of inactivated droplet nuclei is expressed as transmission type XDa. In this case, XDa is a lower transmission type of XD.

[0177] Two transmission types can overlap with each other. In the above example, XL∩Xd is generally not an empty set. In addition, the upper transmission type does not need to be completely classified by the lower transmission type. For example, it can also be assumed that for the previous transmission types Xd and Xa, the pathogen X contained in the raindrops is not classified in either of the two. Moreover, there is also a case where the same transmission substance Z corresponds to two different pathogens X and Y. It is a substance released due to an inflammatory response when a pathogen invades the human body.

[0178] In addition, it is only necessary to define the pathogen type or transmission type required for infection countermeasures, and it is not necessary to define all possible types. For example, in pathogen X, transmission types XL, XD, Xd, and Xa are defined, but in pathogen Y, only YL and YD can be defined, and in pathogen Z, no transmission type (except Z) can be defined.

[0179] In the following, A, B, C, ... are written to represent all defined types without distinguishing between pathogenic types and transmission types. That is, A = X, B = XL, C = XD, D = Xd, E = Xa, F = XDa, G = Y, H = YL, I = YD, J = Z, and so on. In addition, X, Y, Z are used as characters to represent any pathogenic type or transmission type.

[0180] <Infection Countermeasure System 1>

[0181] Figure 1 2 is a functional block diagram of the "infection countermeasure system 1". The infection countermeasure system 1 is composed of one or more "observation units 2" and "analysis units 3".

[0182] <Observation Section 2>

[0183] The observation unit 2 is composed of a "sampling unit 21" for sampling propagation substances and a "measuring unit 22" for measuring the amount of sampled propagation substances, and measures the amount of propagation substances in the air.

[0184] The sampling unit 21 and the measuring unit 22 may be placed in physically different places. In addition, there are also cases where the sampling and measuring functions are difficult to distinguish or cannot be distinguished, but in that case, the sampling unit 21 and the measuring unit 22 are considered to be an integrated unit ( Figure 1 The second observation unit 2 from the top in the figure). As such an example, the human body in Examples 1 to 4 can be cited. The respiratory system of the human body can be regarded as a sampling unit 21, and its surface part can also be regarded as functioning as a measuring unit 22 because it triggers an inflammatory-like reaction, but it is more natural to regard it as an observation unit 2 that integrates the sampling and measurement functions. In addition, there are reports that there are studies on the detection of coronaviruses by smell training creatures such as dogs or bees, which can also be regarded as an observation unit 2 that outputs observation results in the form of behavior. In addition, if it is assumed that the propagating substance absorbs light of a specific frequency, the amount of the propagating substance can also be measured by measuring the degree to which the specific frequency component is absorbed during the period when the light passes through a fixed distance within the area range 6. At this time, although no actual sampling has been done, it is considered that the propagating substance located on the path through which the light passes has been sampled.

[0185] <Sampling section 21>

[0186] The sampling unit 21 samples the propagation substances in the air. As an example, the following structure can be cited, that is, a device combining a gelatin filter and a fan (the fan plays the role of blowing air to the filter) is brought into the area range 6 as the sampling unit 21, and it is operated for several hours to allow the pathogens to be adsorbed on the filter. The sampling unit 21 is assigned a number starting from 1 (referred to as the "sampling unit number") i, and the sampling unit 21 with the sampling unit number i is marked as "sampling unit 21[i]".

[0187] The propagation material that is the object of sampling can be indoors or outdoors. In addition, not only can the sample be taken from the air existing at the location, but the exhaled breath of a person passing by can also be blown directly to the sampling unit 21. The sampling unit 21 itself can also be installed anywhere outdoors or indoors. In addition, it can also be set in a fixed position, or it can be sampled while moving (installed on the outside or inside of a car, tram, airplane, ship, etc.). In addition, when it is set in a place with strong winds, it is necessary to avoid the following situations, namely: the amount of propagation material that can be sampled due to being blown away by the wind changes; the amount of propagation material that can be sampled fluctuates according to the wind direction, which can easily cause errors.

[0188] When the amount of propagation material sampled by one sampling unit 21 is small, or when it is desired to save the trouble of measuring each of multiple sampling units 21 (for example, sampling units installed on each floor or each block in a building), the propagation material sampled by the multiple sampling units 21 can be measured as a total amount. In this case, the multiple sampling units 21 are processed as one sampling unit 21.

[0189] If the propagation substance can be cultured, the sampling unit 21 may have a culture function. This allows the amount required for measurement to be increased even if the propagation substance in the air is small, or prevents the decomposition of the propagation substance when there is a time interval between sampling and measurement.

[0190] The sampling unit 21 may perform sampling every day without interruption, or may perform sampling only during a specific time period or on a certain day of the week, etc. In addition, for example, even in the case of 24-hour sampling every day, there are methods such as: using only one filter to measure the total amount of propagation substances in a day through the measuring unit 22; or replacing the filter every hour and handing it over to the measuring unit 22 at any time to measure the amount of propagation substances every hour.

[0191] There may also be a sampling section 21 that samples only a certain propagation type instead of all the propagation types under consideration.

[0192] <Measurement unit 22, measurement value>

[0193] The measuring unit 22 measures the amount of the propagating substance sampled by the sampling unit 21 and outputs the result. For example, the measuring unit 22 extracts DNA / RNA from the pathogen adsorbed on the aforementioned gelatin filter and puts it into the qPCR device to measure the amount of the pathogen.

[0194] There is no limitation on the location where the measuring unit 22 can be set. The sampling unit 21 may be placed within the area range 6, and the amount of the propagated substance sampled there may be measured by the measuring unit 22 located at a remote location. For example, after the sampling unit 21 based on the aforementioned gelatin filter is operated within the area range 6 for several hours, the filter is removed and transported to a remote facility, and the amount of the pathogen is measured by a qPCR device located there. In this case, for example, multiple sampling units 21 may also share one measuring unit 22 in a time-divided manner. In the present disclosure, it is not important whether the measuring unit 22 is shared, so the following description will be based on a one-to-one correspondence between the sampling unit 21 and the measuring unit 22.

[0195] The amount of the measurement result may not be the number of the propagating substance itself. For example, it may be the Ct value of qPCR, or a Boolean value indicating the presence or absence of the propagating substance (set to "yes" when "true"). Alternatively, in the case of dogs that detect propagating substances by smell, the three stages of strong or weak reactions or no reactions may also be considered. In addition, it may be the area of ​​colonies presented on the culture dish after the pathogen has been cultured for a certain period of time.

[0196] For example, in the case of an observation unit 2 that can only know the "presence" of a propagation substance, if an observation unit 2 that can accurately know the amount of the propagation substance is provided in advance as a preliminary preparation and a relationship such as "the number of propagation substances when the measurement result is true is N or more per cubic meter" is obtained, the measurement result obtained in the form of a Boolean value can be converted into the amount of the propagation substance. In addition, if such observation units 2 are arranged in advance at multiple locations as a preliminary preparation, and the corresponding relationship between the measurement results in each observation unit 2 (for example, what is the ratio of the measurement results in the arranged observation units 2 that are true) and the amount of the propagation substance is obtained, the amount can be measured more accurately.

[0197] Hereinafter, the measurement result is set as the number of propagation substances, or an amount equivalent to the number, such as a constant multiple of the number, the number per unit time, or the number per unit volume.

[0198] The same number i is assigned to the measuring unit 22 and the sampling unit 21. The measurement is performed for each propagation type, and the output of the measuring unit 22 is called a "measurement value" and is represented by VAi, VBi, VCi, . . .

[0199] The measured value VXi is a series, and the amount of the propagating substance X measured (hereinafter also referred to as detected) at time t (t is an integer satisfying t≥0) is recorded as VXi[t]. Although the unit of time t is arbitrary, it is set to one day in the following text. That is, VXi[0] is the amount of the propagating substance X detected by the sampling unit 21[i] on the first day of the infection countermeasure, VXi[1] is the amount on the second day, and so on. However, even on the same day, the infection situation may change between morning and evening. Therefore, in the case of improving the accuracy of the infection countermeasure, a finer unit, such as one hour, will be used. In addition, even if it is taken as a day, it is not necessary to sample continuously for 24 hours. For example, it is also possible to sample only for 1 hour starting at 13:00 every day and use it as the amount for the day.

[0200] In the following, the amount that changes with time is also treated as a sequence of numbers or values ​​in a manner that discretizes time. In addition, unless otherwise specified, when a time is not specified with [], it is assumed to represent the entire sequence. That is, in the above example, VXi represents the entire sequence, and VXi[t] represents the tth value in the sequence.

[0201] <Observation area>

[0202] It is often not possible to measure the amount of the transmission substance in the entire area 6 using only one observation unit 2. In addition, the infection situation in the area 6 may not be uniform and may vary from place to place. In this case, it may not be possible to carry out detailed infection countermeasures based on only one measurement result. Therefore, it is generally considered that the observation unit 2 measures a smaller area called an "observation area" ( Figure 3 , Figure 4 ) in a space. However, even in this case, the measurement result of the observation unit 2 does not necessarily accurately represent the amount of propagation material in the observation area. At that time, it is assumed that the following methods are used to adjust until it can be considered sufficiently accurate (for details, see the <Method for Confirming Approximate Accuracy> described later), that is, narrowing the scope of the observation area, strengthening the measurement based on the observation unit 2 (extending the observation time, sampling in a larger range within the observation area, etc.). For example, there is a situation where, when the observation unit 2 is set up indoors in a certain building, if the outdoor of the building is also included in the observation area, the amount of propagation material indoors and outdoors is greatly different, and this approach is not suitable. At that time, it is better to limit the observation area to the indoors. Assuming that the result is that the observation area becomes too small, the following measures can be taken, that is, moving the observation unit 2 outdoors, or adding a device so that it is set up in both indoors and outdoors. In addition, although it is ideal for the observation area to fall within the area range 6, it can also extend to the outside or be completely located outside. In addition, the area range 6 itself can also be used as the observation area.

[0203] <Analysis Part 3>

[0204] The analysis unit 3 is composed of a "storage unit 31", a "determination unit 32" and an "update unit 33". The determination unit 32 performs the "determination process" to infer the infection status and control the infection. The update unit 33 provides feedback on the control result of the determination unit 32 and optimizes the control through the "update process". In addition, the "infection quantification function" possessed by the determination process and the update process is the core function of the present disclosure.

[0205] <Analysis device 5>

[0206] Figure 2This is a structural diagram of a device that realizes the analysis unit 3 (hereinafter referred to as "analysis device 5"). The analysis device 5 is composed of a "processing device 51" (typically, realized by a CPU, i.e., a Central Processing Unit), a "storage device 54" (typically, realized by a RAM, i.e., a Random Access Memory), an "input device 52", and an "output device 53". The processing (determination processing and update processing) of the analysis unit 3 is performed by storing its program in the storage device 54 and executing it by the processing device 51. The output of the device that realizes the function of the observation unit 2 (hereinafter referred to as "observation device") is input to the input device 52. The observation device and the input device 52 can be connected by a communication line, etc., or the output of the observation device can be manually input to the input device 52 by a person. In addition, the environmental data described later is also input into the input device 52. In addition, the "countermeasures" described later are output from the output device 53.

[0207] <Storage Unit 31>

[0208] The storage unit 31 stores “area range information 43 ,” “determination criterion 41 ,” “update criterion 42 ,” and other data, which will be described later.

[0209] <Environmental data>

[0210] Information other than measured values ​​that is useful for infection countermeasures is called "environmental data". Ideally, it is provided as time series data, including not only information on the current day but also information on the past. In addition, even if it is data for a certain day, it is ideal to provide, for example, hourly changes. Furthermore, it is ideal to provide not only data on the entire area range 6 but also the distribution of each location within the area range 6.

[0211] Environmental data include, for example, meteorological data (weather, temperature, humidity, wind direction, wind speed, sunshine hours, precipitation, etc. within area 6), statistical information of the group staying in area 6 (age, gender, vaccination or antibody status, movement path or stay time within area 6 on that day, etc.), etc.

[0212] <Regional information 43>

[0213] The area range information 43 is a table that records the position information of the area range 6 and the position information of each observation area (all when there are a plurality of observation areas) belonging to the area range 6 .

[0214] The location information here refers to the place indicating the area range 6 or the observation area, and more strictly speaking, it is information indicating the boundaries surrounding those areas. As a simple example, addresses can be listed. For example, as the location information of the area range 6, it is "Tokyo", and as the location information of the observation area, it is "△△ town in 00 city, Kanagawa prefecture", etc. In addition, since the area range 6 or the observation area is generally a three-dimensional area, it is necessary to specify not only the horizontal extension but also the vertical extension. For example, the "height of the highest building in the specified address" can be specified in advance as the vertical extension. In addition, the three-dimensional shape of the area can also be defined as a polygon using coordinates (latitude, longitude, height).

[0215] The table is composed of a plurality of entries. In the first entry, the position information of the area range 6 is recorded. In the next entry, the position information of the observation area R1 is recorded. Similarly, the position information of the observation area R2 and subsequent areas is recorded one entry at a time.

[0216] <Countermeasures>

[0217] The output of the determination unit 32 is called "countermeasures", which become the output of the infection countermeasure system 1 itself. For example, the outputs include "do nothing", "strengthen infection status monitoring based on random detection", "instructions to avoid going out", "lockdown for L days", "end the lockdown early", "strengthen the hospital's acceptance of infected people" and other countermeasures. Here, L is an appropriate value, which can be determined in advance or calculated by the determination unit 32. In addition, the output may be a countermeasure for each pathogen type, such as "spraying a drug effective against pathogen A".

[0218] In the following, as a countermeasure, only "blocking for L days" is adopted, and it is assumed that the determination unit 32 outputs L. Here, it is assumed that L is a positive integer or 0, and L=0 specifically means "do nothing".

[0219] <Determination Unit 32>

[0220] The determination unit 32 performs determination processing and outputs a countermeasure with reference to the determination criterion 41. The determination processing uses the measured values ​​VAi, VBi, VCi, ... (here, the number of samplers 21 is N, and i represents all integers from 1 to N).

[0221] In the present disclosure, the time for executing the determination process is set to 24:00 midnight, and this time is called determination time Td. In addition, for simplicity, the process is assumed to be completed instantly, and when the blockade is implemented, it is assumed to start from 0:00 am, which is time Td+1.

[0222] <Judgment Criteria 41>

[0223] The rule for calculating the countermeasure in the determination process is referred to as “determination criterion 41.” Although in the present disclosure, determination criterion 41 is defined as a table, more generally, it may be defined as a function that calculates the countermeasure based on the measured value and the environmental data.

[0224] The table consists of one or more entries. Each entry consists of a "condition" and a "countermeasure L" ( Figure 5 ). The determination process checks from the first entry in the table in order, and outputs the "countermeasure L" of the first entry whose "condition" is true.

[0225] In addition, in the last entry of the table, if the "condition" is set to always be true and the "corresponding measure L" is set to 0 (hereinafter referred to as the "judgment sentinel"), even if the "condition" is false in all other entries, a certain result will be output from the judgment process.

[0226] <Update method>

[0227] In the process (update process) of the update unit 33, the method of rewriting the determination criterion 41 is called an “update method.” Examples of the method include rewriting the “condition” and “countermeasure L” of an entry, adding or deleting an entry, etc., but there is no particular limitation.

[0228] In the following, as an update method, only the case of rewriting the countermeasure L is considered. Specifically, the update method is to set an integer (positive or negative value or 0) "ΔL", which means "rewrite to the value obtained by adding ΔL to L. If the addition operation makes L<0, rewrite to L=0 (that is, make L always become L≥0)."

[0229] The update unit 33 performs update processing with reference to the update criterion 42, calculates an update method, and then rewrites the determination criterion 41 in accordance with the update method. In addition, the update unit 33 may be omitted when the update processing is not performed.

[0230] <Update standard 42>

[0231] The rule for calculating the update method ΔL in the update process is the “update reference 42.” Although the update reference 42 is defined as a table in the present disclosure, it can be more generally defined as a function that calculates the update method ΔL based on the measured values ​​and environmental data.

[0232] The table consists of one or more entries. Each entry consists of "condition" and "update method ΔL" ( Fig.11 ) The update process checks the first entry in the table in order, and uses the "update method ΔL" of the first entry where the "condition" is true to update the determination criterion 41.

[0233] In addition, in the last entry of the table, if the "condition" is set to always be true and the "update method ΔL" is set to 0 (hereinafter, this entry is called "update sentinel"), even if the "condition" is false in all other entries, some result will be output from the update processing.

[0234] The update criterion 42 may be omitted when no update process is performed.

[0235] Since the definitions of common terms have been completed above, the details of the first embodiment will be introduced.

[0236] <Floating amount qi, released amount ei, retained amount si>

[0237] In this embodiment, it is considered that there is only one sampling unit 21 (only i=1), and the transmission substance is only one type of pathogen A (such as coronavirus). In addition, ideally, the amount of A produced in nature is so small that it can be ignored, and the A present in the observation area Ri can be regarded as a substance generated in the body of the person staying there. In addition, for the following discussion, even if the measurement value Vbi of measuring another transmission substance B is used instead of the measurement value VAi, if VAi and VBi are proportional, it still holds true.

[0238] Let the number of A per cubic meter in Ri at a certain time t (i.e., the tth day of infection countermeasures) (hereinafter referred to as "floating amount") be qi, and let the average number of A released by each person stranded in Ri per day (hereinafter referred to as "release amount") be ei. Furthermore, let the cumulative detention time of the group in Ri (the unit is set to person-day. For example, 10 person-days means 1,000 people are stranded for 0.01 days each or 100 people are stranded for 0.1 days each. Hereinafter referred to as "detention amount") be si. The cumulative detention time can also be referred to as the cumulative outing time when the residence or office in Ri is excluded from consideration. In order to measure the detention amount si, for example, a stopwatch can be provided to all people stranded in Ri on that day, and they can measure their own detention time and add up the detention time of all people.

[0239] In addition, since qi, ei, and si are quantities that change with time, they should be written as qi[t], ei[t], and si[t]. However, in the present embodiment, since t is fixed, "[t]" is omitted when it does not cause any particular confusion. Even values ​​defined as "quantities that change with time" are treated in the same manner below. In addition, in the following text, quantities that are proportional to qi, ei, and si themselves are often treated (including cases of approximate proportions), but these quantities are represented by adding apostrophes (') like qi', ei', and si', and are respectively referred to as "specific floating amount", "specific release amount", and "specific retention amount".

[0240] If the sampling unit 21[i] uniformly samples A in the air of the observation area Ri, it can be considered that VAi is proportional to qi, so VAi will be marked as qi' in the future. In addition, as a first-order approximation, the larger qi is, the more A is brought into the body by the people staying inside Ri, so it can be said that the infection situation is worse. In Examples 1 to 4, it was observed that in the case of coronavirus, qi' is linked to the number of new positives in the future.

[0241] In addition, although A present in Ri is in various states such as droplets, aerosols, and droplet nuclei, if qi is doubled, then as a first-order approximation, it can be said that the amount of A in the droplet state (the same applies to the aerosol and droplet nuclei) will also double. Therefore, if qi is doubled, the amount of A in the droplet state brought in by the people staying in Ri will also double. This means that even in the case where A is only infected by droplets (no aerosol or airborne infection occurs), when measuring qi, sampling can be performed in a manner that is not limited to A in the droplet state. That is, when the amount of A in each state of droplets, aerosols, and droplet nuclei that is indiscriminately sampled and measured is set to qi, as a first-order approximation, qi is proportional to the amount of A in the droplet state brought in by the people staying in Ri.

[0242] However, strictly speaking, as described below, it cannot be said that the larger the qi, the worse the infection situation. When implementing infection countermeasures with higher accuracy than the first approximation, various situations should be considered, namely: First, even if the qi value is the same, the number of people with antibodies may change the judgment of the infection situation. Second, depending on the pathogen, there may be both positive and negative effects by entering the body (activating the immune system, making it less likely to get other diseases, etc.). Third, although the sampled A is a substance released by the infected person through various behaviors such as sneezing, talking, breathing, etc., floating in the air and finally captured by the sampling unit 21, even if it is released as droplets, before being sampled, some become droplet nuclei and some remain in the droplet state, and the state is not the same. In addition, whether the air is dry on that day may also cause those ratios to change. Fourth, depending on the state of the infected person (for example, whether it is in the early stage of infection or after the early stage), the ratio of A released as droplets, aerosols, and droplet nuclei may change (for example, in the early stage of infection, it is 8:2:1, but later it becomes 2:1:1, etc.). In addition, there are other situations.

[0243] Next, considering from the perspective of the person who is released, as opposed to the person who brings it into the body, as a first approximation, it can be said that the larger the ei, the more pathogens there are in the body and the worse the infection condition.

[0244] However, strictly speaking, as described below, it is not necessarily true that the larger the ei, the worse the infection situation. When implementing infection countermeasures with higher accuracy than the first approximation, various situations should be considered, namely: First, even if the ei value is the same, the infection situation can be judged to be good or bad based on the internal decomposition such as the situation where there are more infected people with mild symptoms (or less pathogen release) and the situation where there are fewer infected people with severe symptoms (or more pathogen release). In particular, among the former mild infected people, it can be considered that there are also people with mild symptoms that are difficult to be called infections, and who will recover within a few hours without any subjective symptoms (in the inventor's experience, if the amount of coronavirus inhaled is small, the discomfort in the throat will recover within tens of seconds, but if it is large, it may take several hours to recover. During the recovery period, no special symptoms will appear, but it can be considered that the inventor himself is also releasing a small amount of virus. Others are likely to be the same, but they are not aware of it). In this case, the ratio of mild to severe infected people will be separately obtained or inferred to evaluate the infection situation. Second, depending on the type of pathogen, the amount of pathogen released by a person may not necessarily correspond to the state of health, such as when the release is at its maximum before the onset of disease. Third, the relationship between the amount of pathogen released by an infected person and the amount of pathogen in the body may be more complicated than a proportional relationship (diseases other than respiratory system diseases, etc.). Fourth, even if the ei value is the same, if the characteristics of the pathogen change due to mutation, the judgment of the infection status may change. In addition, there are other cases.

[0245] Consider the relationship between qi and ei here. The total amount of A released within Ri on the same day can be calculated as ei*si, but since as a first-order approximation, if the total amount is doubled, qi is also doubled, so qi and ei*si, or the same, but there is a proportional relationship between qi / si and ei. However, strictly speaking, A coming in from outside the observation area Ri may also be included in qi. However, if the infection status or the number of people near the boundary of Ri has not changed significantly, it can be considered that the amount of A coming in from the outside through a certain part of the boundary surface will be balanced and offset by the amount going out to the outside through the same part. Therefore, when defining the observation area Ri, if the boundary surface is set in that way, it is also possible for the amount of A coming in from the outside to be included in qi as a first-order approximation.

[0246] In addition, instead of obtaining si, a value si' proportional to it can also be obtained. For example, if the cumulative residence time in a part of Ri (e.g., a square area with a side of 100 meters in the center of Ri) is not the entire Ri, then as a first-order approximation, if the value of si is doubled, then si' will also be doubled, so there is a proportional relationship between si and si'. In addition, if it is defined as ei'=qi' / si' (hereinafter referred to as "relational expression EQS"), ei' and ei are proportional.

[0247] Thus, in order to obtain the quantities qi, ei, and si, it is considered that the primed qi', ei', and si' are often measured first. Moreover, there are also many cases where infection countermeasures are implemented based only on the primed quantities. For example, si' is measured but si is not obtained.

[0248] <Method for confirming approximate accuracy: qi and qi', ei and ei', si and si'>

[0249] The proportional relationship between qi and qi', ei and ei', and si and si' just described is a first-order approximation, but whether the accuracy of the approximation is sufficient can be confirmed by implementing the following procedure for multiple days (the number of days is determined according to the required accuracy). First, for si', in the previous example, an area of ​​100 meters on a side is considered, but the cumulative retention time in a larger area (the closer to Ri, the better, but determined according to the required accuracy) will be investigated to investigate whether there is a proportional relationship with si'. In addition, for qi', move without deviation within Ri for a longer time (time and distance are determined according to the required accuracy), sample A and measure the amount, and investigate whether there is a proportional relationship with qi'. Finally, for ei', a number of people are randomly selected from the people who stayed in Ri on the same day (the number of people is determined according to the required accuracy), and the average amount of A released by these people is actually measured to investigate whether there is a proportional relationship with ei'. If the proportional relationship is established, it can be indirectly inferred that the accuracy of qi' and si', which are the basis for calculating ei', is also sufficient. In addition, at this time, for example, if the amount of A released as droplets indicates the severity of the infection, it is sufficient to compare the average amount released only as droplets (excluding aerosols, etc.) with qi'. In addition, if there is a lack of accuracy in the above procedure, it can be dealt with in the following ways, that is, changing the measurement method of qi', ei', si', changing the setting position of the sampling unit 21[i], changing the range of Ri, etc.

[0250] In addition, whether the previously discussed relationship that the larger qi or ei (or qi' or ei') is, the worse the infection situation is, can also be confirmed by the above-mentioned method of checking health status by random sampling. That is, investigate the relationship between the amount of qi or ei and the health status of randomly selected people (including not only the health status on the day, but also the health status in the future as a result of the introduction of pathogens into the body on the day, etc.). If the larger qi or ei is, the worse the infection situation is, then the size of qi or ei can be directly regarded as the good or bad of the infection situation. In the case that this is not the case, that is, even if qi or ei and the infection situation are more complex relationships such as nonlinearity, for example, if there is a certain threshold, it can be said that "if qi or ei exceeds this threshold, the infection situation is indeed very bad", and when qi or ei exceeds this threshold, the infection control measure can be taken.

[0251] In the discussion so far, the infection status is determined by the amount of either qi or ei. However, how should we deal with the situation where, for example, qi is large but ei is small, or vice versa? Which indicator is more important may vary depending on the purpose, content, and other conditions of the infection countermeasure. For example, if the infection countermeasure is to call on people with low immunity to limit going out, the size of qi may be observed rather than ei. This is because even if ei is small, if qi becomes larger, the number of places with infection risks (as a first approximation) will increase, and the probability of infection by going out will also increase. On the other hand, for example, if the degree of spread of infection is important, even if qi is small, if ei is large (although the infection is spreading, it happens that the number of people going out on that day is very small, etc.), a lockdown may be implemented. In addition, as in Examples 1 to 4, when qi is linked to the number of new infections, even if the qi value is the same, the response will change depending on the size of ei (the extent of the spread of infection). This is because, for example, even if the number of new infections on that day is the same 100 people, the severity will be different when the total number of infections is 10,000 or when there are only 10 people. That is, there may be a case where infection countermeasures are implemented by observing both qi and ei. In addition, when implementing infection countermeasures with higher accuracy than the first approximation, various situations should be considered in addition to qi and ei.

[0252] <Example of infection countermeasures>

[0253] If the relationship between the relative floating amount qi', the relative release amount ei' and the infection status is understood through the above method, then qi' and ei' can be used to implement infection countermeasures. However, as described before, this relationship can be as simple as a proportional relationship, or more complex. In addition, there are many methods for infection countermeasures, but in this disclosure, the theme is not to reveal which method is good. Therefore, a general framework will be shown below. Although it is simple as an example, the following method is explained, that is, when qi' and ei' exceed a certain threshold (hereinafter referred to as the "floating threshold" Qi' and the "release threshold" Ei', respectively), it is considered that the infection status has worsened, and a blockade is implemented as an infection control method. However, it is not necessary to use both qi' and ei' at the same time, and infection countermeasures can be made using only qi' or only ei'. Of course, in this case, unnecessary values ​​can also be omitted.

[0254] In addition, for the thresholds Qi' and Ei', it is ideal that even after they are determined once, they are appropriately updated to maintain accuracy. This is because, as time goes by, the characteristics of the pathogen, the resistance of the population to the pathogen, the climate, etc. change, and thus the relationship between ei', qi' and the infection status may also change. In addition, the values ​​of Qi' and Ei' may be changed according to environmental conditions such as the temperature or humidity of the day. For example, under temperature or humidity conditions where the infection is easy to spread, a lower value may be used as Qi'.

[0255] In addition, although it is the target range of infection control, it does not necessarily have to be Ri. It can also be only a part of Ri, or an area including Ri such as the entire area range 6, or an area that does not overlap with Ri but is considered to have a similar infection status to Ri. For example, in the case of a blockade, "people who stayed in Ri on that day are prohibited from going out", "all people who may have stayed in Ri on that day are prohibited from going out", "entry into Ri", "entry into areas with similar infection status to Ri", "entry into the entire area range 6" and the like can be considered.

[0256] <First determination process>

[0257] use Figure 6 The determination process in this embodiment (referred to as "first determination process". However, it is abbreviated as determination process) is described with reference to the flowchart of FIG. Figure 7 The first infection quantitative function is called as a subroutine. In addition, in the following text, the measured value VAi, the relative floating amount qi', the relative release amount ei', the relative retention amount si', the floating threshold Qi', and the release threshold Ei' are used as symbols. In addition, in order to ensure accuracy, [] are not omitted but written down.

[0258] The determination process starts after the determination time: Td and the sampling unit number: i are assigned as parameters (S101). Next, the first infection quantification function is called (S102). Here, for the time t that satisfies 0≤t≤td, qi'[t]=VAi[t], ei'[t]=qi'[t] / si'[t] are pre-calculated (S1021). In addition, although repeated, the calculation of the value that is not referenced in the determination criterion 41 can also be omitted here (in subsequent embodiments, the calculation of the value that is not referenced in the infection quantification function can also be omitted). Next, using j as an index, starting from the first entry (j=1) of the determination criterion 41 to the last entry (n is the number of entries in the determination criterion 41, j=n), the following steps are repeated (S103). First, check whether the condition of entry j (condition j) is true. At this time, Td, i, qi', ei', si', Qi', Ei' or environmental data can be appropriately referenced according to the condition (S104). If condition j is true, the corresponding measure L of the entry is output (S106), and then, when the update process described in the following embodiment is performed, (Td, i, j, L) is saved in advance for the process at that time (S107). On the other hand, when condition j is false, return to the beginning of the loop (S105).

[0259] For example, it is assumed that the judgment criterion 41 is composed of three items as follows: (1) Condition = "qi'[Td] ≥ Qi'*1.5", corresponding measure L = 20. (2) Condition = "qi'[Td] ≥ Qi'", corresponding measure L = 10. (3) Judgment sentinel.

[0260] At this time, in the judgment process, if qi'[Td]<Qi', the infection status is good, so "L=0" is output. In addition, if Qi'≤qi'[Td]<Qi'*1.5, the infection status is considered to be bad, and "L=10" is output. Furthermore, if qi'[Td]≥Qi'*1.5, the infection status is considered to be very bad, and "L=20" is output.

[0261] In the example of the above-mentioned judgment criterion 41, only qi' and Qi' are referred to in the "conditions" of each item, but as mentioned above, any value of qi', ei', Qi', and Ei' can be freely used. In addition, even in the place of "countermeasures", the value of L can be obtained by using the values ​​of qi', ei', Qi', and Ei', such as "L = (ei'[Td] / Ei')*10" (in addition, this formula is only an example for explanation). If so, even if the value of qi' is the same, the blockade period can be changed according to the size of ei'. That is, even if qi' is the same value on the day, in the case where ei' is small and Si' is large and in the case where ei' is large and Si' is small, the latter will be considered to have spread the infection more widely than the former, so that the blockade can be implemented for a long time.

[0262] When the output of the determination process is L>0, the lockdown is entered, but usually the determination process does not need to be executed again until the lockdown ends after L days. However, the determination process can also be used to determine the early end of the lockdown. That is, in order to properly monitor qi' and ei' during the lockdown, the time t (where Td<t<Td+L) can be used as the determination time, so that the determination process is executed again (however, during the lockdown, the reliability of the values ​​of qi' and ei' may be low due to the small number of people going out. In this case, on the day of time t, only a part of the group can be allowed to go out, and qi' and ei' can be measured on the basis of temporarily increasing the amount of people going out. In addition, as an example of t, the middle time point of the lockdown, that is, t=L / 2 (where L / 2 is rounded to the nearest decimal point) can be listed. At this time, for example, if the output corresponding measure L is L=0, it is considered that the infection situation has improved and ends early, and if L>0, the lockdown continues.

[0263] <Summary of this embodiment>

[0264] In this embodiment, infection countermeasures are implemented using the relative floating amount qi' and the relative released amount ei'. The simplest approach is to implement infection countermeasures by assuming that there is a proportional relationship between the infection status in the observation area Ri and qi' or ei', but there may be a more complex relationship.

[0265] In Examples 1 to 4, it was observed that qi' measured in the observation area Ri of several kilometers in the A region was linked to the number of new positive cases in the future in the large area 6 of Tokyo. That is, currently in Japan, the increase or decrease of the infection status, the peak value, etc. are determined by observing the statistics of the number of new positive cases, but if qi' is measured, the increase or decrease of the infection status, the peak value, etc. in the wider area 6 can be predicted with practical costs, and infection control can be implemented at an earlier opportunity. In addition, similarly, if ei' is obtained, the infection status in the wider area 6 can be inferred within the same day with practical costs and infection control can be implemented.

[0266] Even if someone thinks of detecting viruses in the air to know whether there are infected people nearby, they generally don't think of implementing it. This is because, using such a method, at most, you can only know the infection status of the surrounding area (for example, a few dozen meters square), and it takes a huge cost to know the infection status of Tokyo as a whole. This is an empirical feeling. However, this is not the case. In fact, it can be achieved at a very low cost, which is the main claim of this disclosure.

[0267] Furthermore, as described in Example 1, in the early stages of the spread of infection, even if sufficient statistical data on the number of new positive cases cannot be obtained, it can be expected that the infection status can be inferred and infection control can be implemented early and at low cost simply by measuring the amount of virus in the air.

[0268] [Second embodiment]

[0269] Even in this embodiment, it is considered that there is only one sampling unit 21 (only i=1) and there is only one type of a certain pathogen A in the transmission material. In addition, ideally, A does not exist in an environment where there is no infected person. In addition, for the following discussion, even if the measurement value Vbi of another transmission material B is used instead of the measurement value VAi, it is also valid as long as VAi and VBi have a proportional relationship. In addition, even in this embodiment, the infection countermeasure at a certain time t is considered.

[0270] <Absorption amount ai>

[0271] In this embodiment, the infection countermeasure using the amount called "absorption" is described. The result of multiplying the floating amount qi and the retention amount si in the observation area Ri at a certain time t is set as the absorption amount ai. That is, ai = qi*si, which is a quantity that changes with time. In addition, the amount proportional to ai (including approximate proportions) is marked with a prime sign like ai' and is called "specific absorption amount".

[0272] As observed in the first embodiment, qi is proportional to the amount of A brought into the body by the person stranded in Ri within a certain period of time. Therefore, ai is proportional to the total amount of A brought into the body by all the people stranded in Ri during their stay. As a first approximation, it can be said that the larger ai is, the worse or worse the infection condition of the group (all the people stranded in Ri). This is because such a large amount of pathogens enter the body of the group and have the opportunity to reproduce. Some people may be newly infected as a result, and even for those who are already infected, the less the amount of newly brought in pathogens, the better.

[0273] However, when implementing infection countermeasures with higher accuracy than the first approximation, various situations should be considered, similar to the discussion in the first embodiment (the relationship between qi, ei and the infection status). For example, even with the same ai value, the judgment of the infection status may change depending on the number of people with antibodies.

[0274] The relationship between the infection status of the group and ai can also be specifically expressed as follows. At this time, it is assumed that when the number of pathogens A brought into the body is x, the probability of infection is expressed by the function f(x) (for example, there is a study with N as a parameter and modeled as f(x) = 1-exp(-x / N), that is: https: / / www.medrxiv.org / content / 10.1101 / 2020.10.21.20216895v1.full). When drawing a graph of the function f(x), there is a tangent line of f(x) at x = 0, and when its slope is k, as a first-order approximation, the proportional relationship f(x) = k*x (hereinafter referred to as the relationship FKX) holds.

[0275] Moreover, although si is the quantity of the group, it is assumed that it is decomposed into <n1, d1>, <n2, d2>, <n3, d3>. Here, <n1, d1> means that there are n1 people who have stayed in Ri for d1 days. Since the amount of A brought in by each of the n1 people is proportional to d1*qi, if f(x) is proportional to x, then the number of people proportional to n1*d1*qi will be infected. It can be said that the same situation applies to n2 and n3. If they are added together, the number of people infected in Ri on that day will be proportional to n1*d1*qi+n2*d2*qi+n3*d3*qi. When this formula is deformed by noting that si=n1*d1+n2*d2+n3*d3, it will be equal to si*qi, that is, ai. However, this is an approximation. Since there are also people who have been infected in the group represented by n1+n2+n3, it will be more accurate to exclude this number. Since the above discussion also holds true for the internal decomposition of other si, if the number of new infections within the day is ultimately expressed as pi, then as a first approximation, pi is proportional to ai.

[0276] When infected with a pathogen, there is generally an incubation period. In other words, the infection is often discovered after a period of time has passed and the infection has become significant. In contrast, if the pi can be inferred, infection control can be carried out on the population at an earlier stage.

[0277] Although the number of new infections is obtained in the above, it is also possible to more generally obtain "the number of people whose probability f(x) of becoming this state is proportional to x". For example, if the probability of bringing in A and sneezing three or more times during the stay in Ri is f(x), and f(x) is proportional to x, then the number of people who sneezed three or more times due to A on Ri that day is also proportional to ai. In addition, in general, when a certain quantity used in infection countermeasures is proportional to ai (including approximate proportions), if it is set to be called an "influence quantity", various influence quantities can be inferred using ai.

[0278] In the present embodiment, a quantity ai' proportional to ai is used to carry out infection countermeasures. First, as in the first embodiment, a measured value VAi is used as a relative floating amount qi' proportional to qi. In addition, similarly to the relative retention amount si' in the first embodiment, a quantity si" (hereinafter, also referred to as relative retention amount) proportional to si is used, which is defined as ai' = qi' * si" (hereinafter, referred to as "relationship AQS"). The reason for using two relative retention amounts si' and si" here is that the two may not be the same value and the accuracy may be different. That is, the relative retention amount when ei' is obtained from qi' using the relationship EQS (ei' = qi' / si') and the relative retention amount when ai' is obtained from qi' using the relationship AQS may be different. In addition, of course, the same value of si" = si' may also be used.

[0279] In addition, in the first embodiment, ei' is obtained from qi' and si' by the relationship EQS, but in the present embodiment, ei' can also be obtained by actual measurement (the random sampling method described in the first embodiment, etc.), and ai' can be obtained from ei', si', and si" by the formula ai'=ei'*si'*si" (hereinafter referred to as the relationship AESS). In addition, at this time, if the two values ​​si' and si" are not obtained separately, and the result of multiplying them (si'*si") can be directly measured, it can also be multiplied with ei'.

[0280] In the following, the case where the number of new infections pi is estimated as a representative of the influence quantity and infection countermeasures are implemented is described. In addition, this method can also be applied to other influence quantities.

[0281] If the cumulative residence time of uninfected persons at time t is obtained by measurement or inference (the cumulative residence time of persons infected on that day is subtracted from the relative residence time si') as the relative residence time si", the inference of the number of newly infected persons pi discussed previously will become more accurate. This is the same discussion as the previous case of obtaining the relationship between ai and the infection status with an accuracy higher than the first approximation.

[0282] In addition, in Examples 1 to 4, it was observed that qi'=ei'*si' is linked to pi. If the above-mentioned proportional relationship between pi and ai holds, then in fact, ai'=qi'*si"=ei'*si'*si" is proportional to pi. When comparing the definition formulas of qi' and ai', it can be said that qi' is an approximation of ai' when si"=1. Alternatively, it can be said that qi' is an approximation of ai' obtained by replacing si' and si" with the square root of si' (√si'). Therefore, if the retention amount (si or si") is fixed, it can be said that qi' is proportional to ai', and the approximation accuracy of qi' to pi is high. Otherwise, for example, when the retention amount becomes 1.5 times, ai' will become 2.25 times, while qi' will only be 1.5 times, resulting in an error in the approximation of qi' to pi.

[0283] <Method for confirming approximate accuracy: ai and ai', pi and ai>

[0284] Although the ratios between ai and ai', and between pi and ai defined so far are first-order approximations, the relationship between ai' and pi is used as an example to illustrate how to confirm whether the approximation accuracy is sufficient. If the accuracy of the ratio between ai' and pi is sufficient, it can be inferred, although indirectly, that the accuracy of the ratio between ai' and ai, and between pi and ai is also sufficient.

[0285] First, from the personnel who enter the observation area Ri from the outside on the day (called the detention day) or the personnel who stay in Ri at 0:00 am on the detention day, multiple personnel (set as X people) are selected by random sampling for isolation. For the timing of isolation, in the case of the former, it is set to the moment of entry (the moment of initial entry in the case of the personnel who are scheduled to enter and exit Ri multiple times), and in the case of the latter, it is set to 0:00 am. In addition, from the personnel who come out from the inside of Ri on the detention day, or the personnel who stay in Ri at 24:00 pm on the detention day (but, except for the personnel who go out of Ri at least once and enter Ri again on the detention day), multiple personnel (set as Y people) are selected by random sampling for isolation. For the timing of isolation, in the case of the former, it is set to the moment of coming out from Ri (the moment of initial coming out in the case of the personnel who are scheduled to enter and exit Ri multiple times), and in the case of the latter, it is set to 24:00 pm. Then, these X+Y people are isolated for a sufficient time, and inspections are repeatedly performed (based on the measurement of the amount of pathogens in the body of PCR inspection, etc.) to investigate whether they have been infected. In addition, the total number of people who stay at Ri on the stay day is set in advance as N. In addition, the number of people X and Y is determined according to the required accuracy.

[0286] As a result, X' people were confirmed to be infected among X people, and Y' people were confirmed to be infected among Y people. The infection of these X'+Y' people was either caused by the reason of detention at Ri on the detention day (called the origin of the day) or by a reason before that (called the prior origin). In the following, let x = X' / X, y = Y' / Y. First, since Y is randomly selected, it can be inferred that N*y people are infected among N people (the total number of people with the origin of the day and the prior origin). Secondly, since X is also randomly selected, it can be inferred that N*x people among N people are infected due to the prior origin. If their difference is set to pi = N*(yx), it can be inferred that pi is the number of people infected due to the origin of the day.

[0287] By carrying out the above-mentioned actual measurement for a number of days (the number of days should be determined according to the required accuracy), it is possible to verify whether the accuracy of the ratio of ai' and pi is sufficient. If the accuracy is insufficient, it can be dealt with by changing the measurement method of qi', ei', si', si", changing the setting position of the sampling unit 21[i], changing the range of Ri, etc. In addition, through this actual measurement, even about the method of inferring pi from ai' using an approximation higher than the first order (i.e., second order, third order, etc.), it is possible to obtain information, thereby improving infection countermeasures (specifically, improving the judgment criterion 41).

[0288] <Example of infection countermeasures>

[0289] If the relationship between the specific absorption amount ai' and the infection status is known through the method described above, ai' can be used to implement infection countermeasures. However, for the same reason as the first embodiment, a general framework will be shown below. Although it is simple as an example, it illustrates the following method, that is, when ai' exceeds a certain threshold (hereinafter referred to as the "absorption threshold" Ai'), it is considered that the infection status has worsened and a blockade is implemented as an infection control method. In addition, of course, qi' and ei' can also be used together to implement infection countermeasures.

[0290] In addition, similarly to the first embodiment, it is ideal that the threshold Ai' is appropriately updated to maintain accuracy even after being determined once. Alternatively, the value of Ai' may be changed according to the environmental conditions of the day. In addition, although it is the target range of infection control, it does not necessarily have to be Ri.

[0291] <Second determination process>

[0292] use Figure 6 , Figure 8 The determination process in this embodiment (referred to as "second determination process". However, it is simply written as determination process here) is described. Figure 6 The first determination process is changed to, in step S102, Figure 8 The second infection quantification function is called as a subroutine, and the subsequent processing is the same. In addition, in the following text, the measured value VAi, the specific floating amount qi', the specific release amount ei', the specific retention amount si', si", the specific absorption amount ai', the release threshold Ei', the floating threshold Qi', and the absorption threshold Ai' are used as symbols. In addition, in order to ensure accuracy, [] are not omitted but written down.

[0293] In the second infection quantification function, for the time t satisfying 0≤t≤td, ai'[t]=qi'[t]*si"[t] (S1022) is recalculated. The other processing is the same as the first infection quantification function. In addition, with this change, from the "conditions" of the judgment criterion 41, in addition to Td, i, qi', ei', si', Qi', Ei' or environmental data, etc., si", ai', Ai' can also be appropriately referenced. In addition, for Qi' and Ei', they are set to be defined separately according to the method described in the first embodiment.

[0294] In addition, although the first embodiment exemplifies the determination criterion 41 composed of three items, if qi' is replaced by ai' and Qi' is replaced by Ai', it becomes an example of the determination criterion 41 in the present embodiment.

[0295] <Summary of this embodiment>

[0296] In this embodiment, infection countermeasures are implemented using the specific absorption amount ai'. The simplest approach is to assume that the infection status of the observation area Ri is proportional to ai' and to implement infection countermeasures, but a more complex relationship may exist.

[0297] In addition, in the relational expression AQS, the specific release amount qi' is used when obtaining ai'. Therefore, similarly to the discussion in the summary of the first embodiment, by using qi' measured in a narrow observation area Ri, it is possible to make infection countermeasures in a wider range (although the details will be described in the summary of the third embodiment, it is the entire area range 6, etc.) at a practical cost.

[0298] Then, although it will become repetitive, when ai' is proportional to the number of new infections pi, the number of new positives in the future can be predicted by ai'. In addition, not only predictions can be made, but also inferences can be made about the number of new infections in the morning of a certain day. If the number is large, infection control can be implemented on the same day by limiting the amount of outings in the afternoon, thereby reducing the number of new infections. Since the amount of pathogens in the air sometimes changes greatly from day to day, it can be expected that the effect will be better than restricting going out on the next day. In addition, infection control can be implemented on the same day, which has the advantage of being clearer about what should be done now and people can more actively assist in infection control compared to implementing infection control later after the number of new positives is determined.

[0299] Furthermore, using Fig.16 To illustrate the advantages of this embodiment. (A) represents all current infected persons, and (B) represents new infected persons. Of course, (B) is generated by the transfer of viruses from (A). Therefore, as a method of using the number of (A) to infer the number of (B), there are viewpoints such as the effective reproduction number. On the other hand, in this embodiment, as (C), the pathogens released by (A) are considered in a comprehensive manner. Therefore, as a causal relationship, it becomes the following process, that is, (B) is not directly generated from (A) (marked by the dotted arrow in the figure), but (A) releases (C), and (B) is generated as a result (marked by the solid arrow in the figure). Therefore, since the method of this embodiment of inferring the number of (B) based on the amount of (C) is closer to the method of obtaining the number of B based on the amount of (A) as a causal relationship, it is possible to implement a higher precision inference. Or at least, if the approximation is improved from the first to the second, third..., there is a potential possibility to achieve such accuracy.

[0300] [Third Embodiment]

[0301] In this embodiment, a method of extending the first embodiment and providing multiple (N, and since there is only one as a special case, N≥1) sampling units 21 is considered to improve the accuracy of infection countermeasures. In addition, for two sampling units 21[i], [j], the observation areas Ri, Rj may also overlap with each other. In addition, the transmission material considers only one type of pathogen A. Ideally, in an environment where there is no infected person, it is considered that A does not exist.

[0302] The infection conditions in Ri and Rj may be similar or different. When there is less movement of people or objects that spread infection between Ri and Rj, the infection conditions in the two areas are considered independent, so it is better to make the infection countermeasures independent. However, since this is equivalent to dividing the regional range 6 of the infection countermeasure, it is assumed that the infection conditions in the N observation areas are similar. At this time, the release amounts ei and e take similar values. In addition, the result is that if the amount of people in Ri and Rj is similar, the floating amounts qi and qj also take similar values.

[0303] In addition, during the spread of the new coronavirus in Japan since the beginning of 2020, the fluctuations in the number of new positive cases in Tokyo and Kanagawa Prefecture, for example, showed very similar trends. Therefore, the infection status of these central areas with high traffic volume (such as Tokyo Station and Yokohama Station) is considered to be similar to each other.

[0304] <Average specific release amount e0”>

[0305] In the first embodiment, when there is only one sampling unit 21, the relative release amount ei' proportional to the release amount ei is obtained. In this embodiment, since there are multiple sampling units 21, N (1≤i≤N) values ​​can be obtained as ei', and these values ​​are combined to implement infection countermeasures for the entire area range 6. As a result, it is possible to use information in a wider range than when there is only one sampling unit 21 to make highly accurate infection countermeasures.

[0306] However, generally speaking, since the proportionality coefficient between ei and ei' varies depending on the sampling unit 21, it is meaningless to simply average N ei'. Therefore, first, ei' is converted into a mutually comparable amount ei" (hereinafter also referred to as "specific release amount").

[0307] Such a quantity is represented by the release amount ei itself. Therefore, the following method can be considered, that is, for example, ei"=ei, and their average value e0" (hereinafter referred to as "average release amount") is calculated as e0"=(e1"+e2"+...+eN") / N, and if e0" exceeds a certain threshold value E0" (hereinafter referred to as "average release threshold"), infection control is implemented for the entire area range 6. Alternatively, a weight wi is assigned to each ei", and through their weighted average, e0"=(w1*e1"+w2*e2''+...+wN*eN") / (w1+w2+...+wN) is calculated. Here, the method of assigning weights is, for example, to assign a smaller weight to ei" of lower importance, such as ei" obtained based on a measurement value VAi with lower accuracy (when the performance of the sampling unit 21 or the measuring unit 22 is poor) or ei" obtained based on a sampling unit 21 installed in a place with few pedestrians. In addition, there may be a sampling unit 21 with a weight of 0. For example, if w2=0 is set, e0" is obtained without using the measurement results from the sampling unit 21[i](i=2). Conversely, if wi other than i=2 is set to 0, e0" is obtained only by the measurement results of the sampling unit 21[i](i=2). In addition, it should be pointed out in advance that the conventional average is a special case of the weighted average (all weights are 1).

[0308] Although ei is used as ei" in the above example, it generally takes time and effort to obtain ei. As another example of ei", ei" is determined as a relative amount of the ei' value at a certain moment (for example, t=0) set to the reference value 1. That is, ei"[t]=ei'[t] / ei'[0] (in addition, here, [] are not omitted but written down). At this time, if the corresponding threshold is set to Ei", the release threshold Ei' determined in the first embodiment can also be converted to Ei"=Ei' / ei'[0]. Furthermore, E0" is set to the value obtained by weighted averaging Ei".

[0309] <Example of infection countermeasures>

[0310] An example of the infection countermeasure is the example in the first embodiment, which only requires replacing the specific release amount ei' with the average specific release amount e0", and replacing the release threshold Ei' with the average release threshold E0". The details will be described below.

[0311] <Third determination process>

[0312] use Fig. 9 , Fig.10 The determination process in this embodiment (referred to as "the third determination process". However, it is only written as the determination process) is described by referring to the flowchart of the first embodiment. Figure 6 , Figure 7 Similar, so the differences between them are mainly explained. In addition, in the following text, as symbols, the measured value VAi, specific release amount ei', ei", specific retention amount si', weight wi, average specific release amount e0", average release threshold E0", release threshold Ei' are used. In addition, in order to ensure accuracy, [] are not omitted but written down.

[0313] and Figure 6 The difference is that the parts corresponding to S101, S102, and S107 are changed to S201, S202, and S207, respectively. First, since the determination process of this embodiment is not a specific sampling unit 21 [i], but a combination of the measurement results of N sampling units 21, in S201, unlike S101, the sampling unit number: i is not included in the parameter. For the same reason, in S207, unlike S107, (Td, 0, j, L) is saved as data for update processing. That is, in order to store that the determination is the result of combining N sampling units 21, 0 (invalid sampling unit number) is saved as the second value of the data.

[0314] Next, in S202, the third infection quantification function is called. In this step S2021, qi'[t], ei'[t] (0≤t≤Td, 1≤i≤N) are calculated for all N sampling units 21, and further, after ei"[t] is obtained as described above, their weighted average is obtained as e0"[t] (0≤t≤Td). In addition, although in this example, ei"[t] is obtained as ei"[t]=ei'[t] / ei'[0], other methods can also be used.

[0315] In the other steps S203, S204, S205, and S206, Figure 6 Similarly, the items of the judgment criterion 41 are investigated in order from the beginning (j=1), and if an item j whose condition is true is found, the corresponding measure L written therein is output. Then, in S204, Td, e0", E0" or environmental data may be appropriately referenced from the "condition" of the judgment criterion 41 (although other references, such as references to ei' and Ei' for 1≤i≤N, are not prohibited, they are not used in this example).

[0316] In addition, although the first embodiment exemplifies the determination criterion 41 composed of three entries, if qi' is replaced by e0", and Qi' is replaced by E0", it becomes an example of the determination criterion 41 in the present embodiment.

[0317] <Summary of this embodiment>

[0318] As described above, by installing a plurality of sampling units 21 within the area range 6 and integrating the measurement results thereof, it is possible to implement infection countermeasures based on more reliable information than when only one sampling unit is installed.

[0319] Furthermore, although in the above discussion, the average relative release amount e0" is calculated by combining the relative release amount ei', when the amount of people in each observation area Ri (1≤i≤N) is similar, the relative floating amount qi' can be similarly converted into a comparable amount, and its average value (hereinafter referred to as the "average relative floating amount") can be calculated as q0", and referenced from the judgment criterion 41. Thus, even when the relative floating amount is used, a wider range of information can be integrated to improve the accuracy of infection countermeasures. In addition, since the average relative floating amount q0" does not measure the retention amount si' but is calculated only based on the measured value Vai, a more convenient infection countermeasure can be achieved compared to the case of using the average relative release amount e0".

[0320] Similarly, regarding the specific absorption amount ai', when the amount of people in each observation area Ri is similar, ai' can also be converted into a comparable amount, and its average value (hereinafter referred to as the "average specific absorption amount") can be obtained as a0", and referenced from the judgment criterion 41. Therefore, even if the specific absorption amount is used, it is possible to integrate a wider range of information to improve the accuracy of infection countermeasures.

[0321] In addition, the following applications can also be considered. Taking the average specific release amount e0" as an example, through this method, for each observation area Ri (1≤i≤N) and any area R with similar infection conditions (the sampling unit 21 may not be set), the specific retention amount of people inside R can be obtained as s', s", and the specific absorption amount a' inside R can be inferred by the relationship a'=e0"*s'*s". That is, even in an area where the sampling unit 21 is not set, if s', s", are measured, e0" can be used to infer a'. This was briefly mentioned in the summary of the second embodiment, but it means that if ei' or e0" is obtained from qi' in a narrow observation area Ri (1≤i≤N) when R is set to the entire area range 6, s', s" in the area range 6 can be measured to infer a', and infection countermeasures for the entire area range 6 can be implemented.

[0322] [Fourth embodiment]

[0323] In this embodiment, a method of extending the first embodiment (the second embodiment and the third embodiment can also be extended in the same manner) and optimizing the processing of the determination unit 32 is described. In the first determination process of the first embodiment, since there are various methods for infection countermeasures, a general framework is shown and an example thereof is described. In this embodiment, the subject is not to show which of the various optimization methods is better. Therefore, the general framework is still shown, and a simple optimization method is described as an example.

[0324] As in the first embodiment, it is assumed that there is only one sampling unit 21 (only i=1), and that the propagating substance is only one type of a certain pathogen A. Ideally, in an environment where no infected person exists, it is assumed that A does not exist either.

[0325] In the embodiments so far, the determination unit 32 outputs values ​​such as L=20 and L=0 as corresponding measures. Here, the effects of these corresponding measures will be verified in the future and optimized as follows, that is, when the effect is less than expected, the value of L is increased, and when it is greater than expected, the value of L is reduced. In addition, although in this embodiment, the relative release amount ei' is used to verify the effect, it can also be changed to use the relative floating amount qi' or the relative absorption amount ai' for verification (however, it should be noted that qi' or ai' are different from ei', and they are amounts that increase as the number of people in the observation area Ri increases). In addition, in order to simplify the explanation, it is assumed that the blockade is not ended in advance.

[0326] When the determination unit 32 performs the determination process at the past determination time Td, the four values ​​stored in the last step S107 are (Td, i, j, L), and L>0. Then, the blockade for L days starts from the time Td+1, and ends at the time Td+L. The update process will be performed at an appropriate time later (for example, the next day after the blockade ends, that is, the time Td+L+1), and the four values ​​stored at this time will be passed as parameters.

[0327] <Update Process>

[0328] use Fig.12 The update process is described in the flowchart of Fig.13 The fourth infection quantitative function is called as a subroutine. In addition, in the following text, the measured value VAi, the relative floating amount qi', the relative release amount ei', the relative retention amount si', the floating threshold Qi', and the release threshold Ei' are used as symbols. In addition, in order to ensure accuracy, [] are not omitted but written down.

[0329] The update process is started by being given the judgment time: Td, sampling unit number: i, entry number: j, and corresponding measures: L as parameters (S301). Next, the fourth infection quantification function is called (S302). Therefore, for the time t that satisfies 0≤t≤Td+L, it is calculated as qi'[t]=VAi[t], ei'[t]=qi'[t] / si'[t] (S3021). Next, using j' as an index, starting from the first entry (j'=1) of the update benchmark 42 until the last entry (using n as the number of entries in the update benchmark 42, j'=n), the following steps are repeated (S303). First, at the beginning, check whether the condition of entry j' (condition j') is true. At this time, Td, i, j, L, qi', ei', Qi', Ei' or environmental data, etc., can be appropriately referenced according to the conditions (S304). If condition j' is true, the update method of the entry is set to ΔL (S306), and then the corresponding measure L of entry j of the judgment standard 41 is updated to Max (L + ΔL, 0) (Max (a, b) means a when a = b, and the larger of a and b in other cases). In addition, this formula means that although ΔL is added to L, 0 is substituted when L is a negative value (S307). On the other hand, when condition j' is false, it returns to the beginning of the loop (S305).

[0330] For example, consider the continuation of the example of the determination process in the first embodiment. In this case, for example, (Td, i, j = 1, L = 20) are stored as four values. In addition, r = ei'[Td+L] / ei'[Td], and the update reference 42 is composed of three items as follows: (1) Condition = "r < 0.3", update method ΔL = -1. (2) Condition = "r ≥ 0.6", update method ΔL = +1. (3) Update sentinel.

[0331] In this case, the update process is executed. If r<0.3, the lockout period is considered too long, and the countermeasure L of item j of the criterion 41 is reduced to 19. If r≥0.6, the lockout period is considered too short, and L is increased to 21. Otherwise, L is kept at 20.

[0332] <Summary of this embodiment>

[0333] As described above, by rewriting the judgment criterion 41 by using the update process, even if the infection control method initially adopted is not optimal, it can be gradually improved to the optimal state. Therefore, for example, even if the information about the nature of the pathogen is insufficient, infection control can be started first and gradually improved while being applied. In addition, as the situation changes over time (for example, the nature of the pathogen changes, so that the infectiousness increases or decreases, or the number of people who obtain antibodies in the group increases, etc.), infection control can also be optimally maintained accordingly.

[0334] [Fifth embodiment]

[0335] In general, a quantity Ii (hereinafter referred to as an "indicator") that represents the infection status or is helpful for infection countermeasures can be expressed as Ii[t] = Ii(t; VAi, si', si"; x, y, ...; ui, vi, ...) by using time t, measurement value VAi, specific retention volume si', si", environmental data x, y, ..., parameters ui, vi, ... as independent variables (in addition, the semicolon indicates the grouping of parameters and is the same as the comma, which is just a separator). Here, the environmental data x, y, ..., and the parameters ui, vi, ... are constants or quantities that change with time. In addition, the function Ii can be expressed as a specific formula or implemented by computer simulation that outputs results when independent variables are input.

[0336] In the embodiments described so far, the floating amount qi, the relative floating amount qi', the release amount ei, the relative release amount ei', the absorption amount ai, the relative absorption amount ai', the number of newly infected people pi, etc. are used as indicators. For the indicator Ii represented by a more general function, if the parameters ui, vi, ... can be determined by regression analysis or machine learning based on the measured value of Ii, it is possible to infer Ii[t].

[0337] For example, in the second embodiment, pi is calculated assuming that the amount of pathogens introduced into the body is proportional to the infection probability, but even if the probability presents a more complex relationship, pi can be estimated as long as the function Ii is determined.

[0338] Furthermore, when function Ii is determined, if Ii is solved for one of its independent variables, such as environmental data x, then x can sometimes be expressed as a function of another independent variable of function Ii and Ii[t]. That is, x is obtained in reverse from Ii[t]. Thus, for example, the proportional coefficient between the amount of pathogens brought into the body and the probability of infection (the coefficient k in the relationship FKX) can be inferred based on qi', si", and pi. Furthermore, by monitoring the time changes of the proportional coefficient, signs of changes in the situation (the emergence of new mutants, etc.) can also be captured. In addition, si' can also be inferred based on qi' and ei', that is, the amount of pathogens in the air can be used to infer the amount of people in the observation area Ri.

[0339] [Sixth embodiment]

[0340] The infection status of a group depends on the time of day (day of the week, morning, afternoon, etc.), and may temporarily worsen or improve. For example, on Monday, since the day before was a day off, people may feel tired quickly after work, and the amount of pathogens released may be greater than on other days of the week.

[0341] In order to control infection without being overly sensitive to temporary fluctuations, there is a method of controlling not only by the value at time t but also by, for example, the average value of the most recent week (if it is the relative release amount ei', it is the average value of ei'[t-6], ei'[t-5], ..., ei'[t]). Alternatively, the corresponding measures are outputted taking into account the attributes of the moment. For example, the values ​​of the previously described floating threshold Qi', release threshold Ei', absorption threshold Ai', etc. are changed according to the day of the week (for example, Monday's Ei' is pre-set to be larger than other days of the week), etc. More generally, in the judgment criterion 41 and the update criterion 42, entries are added that take into account the attributes of the moment (not limited to the judgment moment Td, but also the attributes of other moments, such as the attributes of the moment Td-1).

[0342] [Seventh embodiment]

[0343] According to observations of the inventors' physical responses, during the spread of infection caused by the new coronavirus in Japan starting in early 2020, the amount of pathogens in the air was low in the early morning or noon hours and increased over time.

[0344] As an infection control, in the case of restricting going out while minimizing the damage to the economy, a method of restricting only certain time periods instead of restricting 24 hours a day can be considered. If the above characteristics are utilized, if the length of the restricted time is the same, the effect is better in the evening than in the morning and at noon (since the specific absorption amount ai' is defined as ai'=ei'*si'*si", if the amount of people is the same, it is obvious that the amount when ei' is smaller becomes smaller. In addition, here, ei' is the specific release amount, and si' and si" are the specific retention amounts).

[0345] [Eighth embodiment]

[0346] In this embodiment, it is also considered that there is only one sampling unit 21 (only i=1), and the propagation substance is only one type of a certain pathogen A. Fig.15 In the process, the infection control process starts with the determination time: Td, the sampling unit number: i being assigned as parameters (S401). After that, the following loop process is performed (S402). First, the first infection quantitative function is called to calculate the floating amount qi' and the release amount ei' (S403). However, the first infection quantitative function may not necessarily be called here, and the second infection quantitative function may also be called. Next, it is determined whether the infection situation has become good enough. Here, it is investigated whether qi'[Td] is smaller than the threshold value Qi' which is pre-set in the same way as the floating threshold value in the first embodiment (S404). However, it is not necessary to investigate the floating amount qi', but the release amount ei' or the absorption amount ai' may also be investigated. In addition, instead of simply comparing with the threshold, a complex condition such as the "condition" in the judgment criterion 41 may be investigated. The result of the judgment is that if the infection situation becomes good enough, the infection control process ends. Otherwise, a blockade of L days will be implemented (S405). Among them, L is a positive value, and the same value (for example, L=3) can be used all the time, or it can be changed each time each cycle is repeated. After that, only one day of blockade is lifted and normal life is resumed (S406). In addition, this does not have to be one day, and an appropriate number of days such as two days can also be selected. Finally, in the call of the first infection quantification function in the next cycle, in order to be able to calculate the infection status on the day when the blockade was lifted in step S406, L+1 is added to Td (S407).

[0347] This infection control process has the following effect, namely, during the blockade, the blockade is intentionally lifted for only one day, economic activities are temporarily restarted, and the infection status is easier to grasp (the amount of outing is intentionally restored to normal, thereby increasing the amount of virus in the air and making the measured value VAi reflect the infection status of more people).

[0348] Explanation of symbols

[0349] 1Infection Countermeasures System

[0350] 2 Observation Department

[0351] 21 Sampling Department

[0352] 22 Measurement Department

[0353] 3Analysis Department

[0354] 31 Storage

[0355] 32 Judgment Department

[0356] 33 Update Department

[0357] 41 Criteria

[0358] 42 Update Baseline

[0359] 43 Area Range Information

[0360] 5Analytical device

[0361] 51 Processing device

[0362] 52 Input Devices

[0363] 53 Output device

[0364] 54 Storage Device

[0365] 6. Regional scope

Claims

1. An infection countermeasure system, characterized in that: have: An observation unit that samples the propagation substance (X) in the region Ri and measures its amount; An analysis unit calculates the value of one or more indicators or their approximate values ​​for infection countermeasures in the entire area based on the measured value (VXi) of the amount of the propagating substance.

2. The infection countermeasure system according to claim 1, wherein: The analyzing unit outputs a countermeasure for infection prevention in the entire region based on the calculated value of the indicator or an approximate value thereof.

3. The infection countermeasure system according to claim 2, wherein: The analysis unit inputs the cumulative residence time of the group in the area Ri, that is, the residence amount (si), and infers the floating amount (qi) of the pathogen based on the measured value of the amount of the propagating substance (VXi), and uses the floating amount (qi) and the residence amount (si) to calculate the release amount (ei) of the pathogen or the absorption amount (ai) of the pathogen, and outputs the corresponding measures based on the result of the calculation.

4. The infection countermeasure system according to claim 3, wherein: The pathogen release amount (ei) is obtained based on the result of dividing the floating amount (qi) by the retention amount (si).

5. The infection countermeasure system according to claim 3, wherein: The pathogen absorption amount (ai) is obtained based on the result of multiplying the floating amount (qi) and the retention amount (si).

6. A method for countermeasures against infection, characterized in that: include: The step of sampling the propagation material (X) in a certain area Ri and measuring its amount; A step of calculating the value of one or more indicators or their approximate values ​​for infection countermeasures for the entire area based on the measured value (VXi) of the amount of the transmission substance; The step of outputting countermeasures for infection control in the entire region based on the calculated value of the indicator or its approximate value.

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