Method for infection control and infection control system

Abstract

To estimate the infection status of a population at a lower cost or in an earlier stage, and to perform infection control of the population by using the information.SOLUTION: The infection control system includes: an observation unit for collecting a propagation substance (X) in a region Ri and measuring the amount of the propagation substance; and an analysis unit for calculating at least one value of an index or the approximate value of the index for infection control of an entire area, on the basis of the measurement value (VXi) of the amount of the propagation substance, the observation unit collecting the propagation substance by a method for collection based on accuracy required for infection control, and measuring the amount of the propagation substance.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present disclosure relates to methods and systems for infection control. [Background technology]

[0002] Measures to protect people and groups of people from infection by pathogens (hereafter referred to as "infection control") are a common concern for humans, regardless of time or place. Infection control measures can be implemented for individuals or groups, and various methods have been developed to date. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2020-008969 Summary of the Invention [Problem to be solved by the invention]

[0004] Infection control measures for individuals include prevention and treatment. These have the important objective of preventing pathogens from entering the body as much as possible and minimizing damage if they do enter the body. On the other hand, infection control measures for groups have the important objective of preventing pathogens from entering the body of a group through transmission as much as possible and establishing a medical system in case the infection spreads within a group.

[0005] While various methods have been developed for individual infection control, mass infection control poses many challenges, perhaps because there are few pandemics or other events that require such measures. For example, understanding the infection status within a population (e.g., how many people are infected, how quickly the infection is spreading, etc.; hereafter referred to as the infection status) is essential for taking countermeasures. However, this requires enormous costs (in terms of money, effort, time, and legal requirements). For example, even if everyone is simultaneously tested for infection at a certain point in time, the information becomes outdated the moment the testing is completed. In other words, if new infections or people recover from infection occur, this information will not be known unless further testing is conducted. Another problem is that people who have recently been infected may not have enough pathogens in their bodies to be detected by testing.

[0006] The objective of this disclosure is to estimate (including approximate estimation) the infection status of a population at lower cost or earlier, and to use this information to control infection in the population. Here, infection control typically refers to improving the infection status, such as reducing the number of infected people, but also includes taking measures according to the infection status, such as strengthening the medical system and adjusting economic activity. Hereinafter, the term infection control will be used to mean this estimation and control. [Means for solving the problem]

[0007] To solve the above-mentioned problems, the present disclosure utilizes what are called vectors contained in the environment (hereinafter referred to as air, but could also be water, etc.). Vectors are substances that suggest that a source of infection (such as an infected person) is nearby, and a typical example is the pathogen itself. For example, a person with a cold releases vectors into the environment by sneezing, coughing, talking, breathing, etc. Therefore, the presence of vectors in the environment indicates a high possibility that a source of infection is nearby. Furthermore, it also suggests the possibility that someone may ingest the pathogen through contact with the source of infection and become newly infected.

[0008] In this disclosure, by observing (collecting and measuring) propagating substances in the environment, it is possible to estimate the presence or absence of infected people in a target area, the number of new infections in that area, etc., which is useful for infection control. In addition to simply measuring propagating substances, more detailed estimations can be made by combining them with the number of people in the area. While this disclosure focuses on human populations, the techniques presented here can also be applied to, for example, animal or plant groups, and in the special case, when a population consists of a single individual. Furthermore, they can be applied when infection with a pathogen is harmless or even beneficial.

[0009] Specifically, the infection control system disclosed herein is characterized by comprising an observation unit that collects a propagating substance (X) in an arbitrary area Ri and measures its quantity, and an analysis unit that calculates the value or an approximate value of one or more indicators for infection control of the entire area based on the measured quantity of the propagating substance (VXi). In particular, the analysis unit inputs the resident amount (si), which is the total time spent by the group within the region Ri, and estimates the airborne amount (qi) of pathogens from the measured amount of the transmitted material (VXi). Using the airborne amount (qi) and the resident amount (si), the analysis unit calculates the pathogen emission amount (ei) or the pathogen absorption amount (ai), and based on the calculation results, outputs countermeasures for infection control for the entire area previously associated with the region Ri. The pathogen emission amount (ei) is obtained by dividing the airborne amount (qi) by the resident amount (si). The pathogen absorption amount (ai) is obtained by multiplying the airborne amount (qi) and the resident amount (si). Here, the suspended amount of the propagating substance (qi) includes cases where it is proportional to the amount of the propagating substance (VXi) (specific suspended amount q'). Similarly, the staying amount, emitted amount, and absorbed amount include the specific staying amount (si', si''), the specific emitted amount (ei'), and the specific absorbed amount (ai'), respectively. Note that proportionality also includes approximate proportionality.

[0010] Furthermore, the method for taking infection control measures according to the present disclosure is characterized in that it estimates and predicts the infection status by collecting propagating substances in the environment and measuring their amount as a measurement value VXi. Note that the results of estimating and predicting the infection status may be used for control. The same applies hereinafter. Here, the target area may be more than several tens of meters from the location where the data was collected, or a distance greater than that covered by known technology at the time of filing of this disclosure. By measuring the amount of substances in the environment that are both causes and effects that impair the functioning of living organisms (for example, viruses in water are not in this state), the infection status can be estimated and predicted. Specifically, the infection control method disclosed herein is characterized by including the steps of collecting a propagating substance (X) in a certain area Ri and measuring its amount, calculating one or more index values ​​or approximate values ​​thereof for infection control of the entire area based on the measured amount of the propagating substance (VXi), and outputting countermeasures for infection control of the entire area based on the calculated index values ​​or approximate values ​​thereof.

[0011] Furthermore, the method according to the present disclosure is characterized in that an index representing the infection status is estimated or predicted using at least one of VXi, si', and si''. The method according to the present disclosure is characterized in that ei'=VXi / si' is calculated using VXi,si'. Furthermore, the method according to the present disclosure is characterized in that ai'=qi'*si'' is calculated using VXi,si''. Note that the amount of influence of the number of new infections pi, etc. may be estimated using the calculated ai'. The method according to the present disclosure is characterized in that ai'=ei'*si'*si'' is calculated using ei', si', and si''. The method according to the present disclosure is also characterized in that e0'' is calculated by combining the measured values ​​of the multiple sampling portions. Furthermore, the method disclosed herein is characterized in that even for areas R where there is no collection area, a'=ei'*s'*s'' or a'=e0''*s'*s'' is calculated using ei' or e0'' from areas with similar infection status. The method according to the present disclosure is characterized in that it measures qi', ei', and ai' to measure the effectiveness of infection control measures, which may be used to optimize infection control measures through feedback control.

[0012] According to the system and method for infection control disclosed herein, even when the number of new positive cases is decreasing, if ai' is increasing, a lockdown can be implemented, and conversely, even when the number of new positive cases is increasing, if ai' is decreasing, a lockdown can be lifted. The ability to make predictions using ai' in this way is a feature of the present disclosure. The above-described method can be realized as a system for taking measures against infection, and can also be realized as a computer program for operating this system or device. [Effects of the Invention]

[0013] According to the present disclosure, it is possible to estimate the infection status of a group at a lower cost or earlier, and to use that information to take measures against infection in the group. [Brief explanation of the drawings]

[0014] [Figure 1] 1 is a functional block diagram of an infection control system 1 according to the present disclosure. [Figure 2] FIG. 10 is a configuration diagram illustrating the case where the analysis unit 3 is realized by a program. [Figure 3] Example of area 6 with one collection point 21. [Figure 4] An example of an area 6 having multiple collection sections 21. [Figure 5] An explanatory diagram of judgment criterion 41. [Figure 6] 10 is a flowchart of a first determination process (first embodiment). [Figure 7]10 is a flowchart of a first infection quantification function (first embodiment). [Figure 8] 10 is a flowchart of a second infection quantification function (second embodiment). [Figure 9] 10 is a flowchart of a third determination process (third embodiment). [Figure 10] 10 is a flowchart of a third infection quantification function (third embodiment). [Figure 11] An explanatory diagram of update standard 42. [Figure 12] 10 is a flowchart of an update process (fourth embodiment). [Figure 13] 10 is a flowchart of a fourth infection quantification function (fourth embodiment). [Figure 14] The relationship between the number of new infections and the number of new positive cases. [Figure 15] Flowchart of infection restriction processing (eighth embodiment) [Figure 16] FIG. 10 is a diagram illustrating an advantage of the second embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0015] Hereinafter, examples and embodiments of the present disclosure will be described with reference to the drawings.

[0016] [Example 1] Although the central idea of ​​this disclosure is simple, to the inventor's knowledge, no other country or region is implementing infection control measures based on a similar idea, which is likely evidence of the difficulty of arriving at this idea. The inventor conducted research using his own body for approximately two years through observation and recording, and discovered that the idea was effective. Please refer to the supplementary notes at the end of this example for details about the inventor's constitution.

[0017] In the following, the epidemic of the novel coronavirus SARS-CoV-2 (hereafter referred to simply as the "virus"; however, when it is confusingly similar to viruses in general, it will be referred to as the "coronavirus") that began in Japan in early 2020 will be divided into seven waves, each of which refers to the following periods: Wave 1 from early January to early May 2020, Wave 2 from mid-May to mid-October 2020, Wave 3 from late October 2020 to mid-February 2021, Wave 4 from late February to late May 2021, Wave 5 from early June to late November 2021, Wave 6 from early December 2021 to mid-June 2022, and Wave 7 from late June to August 7, 2022 (the examples in this disclosure show observation results up to Sunday, August 7, 2022). The division of each wave will vary depending on how you look at it, but this is not particularly important in the discussion below. Furthermore, the above divisions may seem a little early compared to the fluctuations in the number of new positive cases, but this is because the divisions are based on fluctuations in the number of new infections (which, as will be explained later, are one to two weeks earlier), rather than on the statistics for the number of new positive cases. Note that "new infections" refer to people who have ingested the pathogen on a given day and become newly infected, while "new positive cases" refer to people who have newly tested positive and whose data is published on that day (note that not all new infections will necessarily appear as new positive cases in later statistics, as some people recover before being tested).

[0018] When interpreting the observation results, it is also advisable to be aware of national holidays, so they are listed below. The national holidays in 2020, including those that fell on Sundays, were January 1st (Wednesday), January 13th (Monday), February 11th (Tuesday), February 23rd (Sunday), February 24th (Monday), March 20th (Friday), April 29th (Wednesday), May 3rd (Sunday), May 4th (Monday), May 5th (Tuesday), May 6th (Wednesday), July 23rd (Thursday), July 24th (Friday), August 10th (Monday), September 21st (Monday), September 22nd (Tuesday), November 3rd (Tuesday), and November 23rd (Monday).

[0019] The national holidays in 2021, including Sundays, are January 1st (Friday), January 11th (Monday), February 11th (Thursday), February 23rd (Tuesday), March 20th (Saturday), April 29th (Thursday), May 3rd (Monday), May 4th (Tuesday), May 5th (Wednesday), July 22nd (Thursday), July 23rd (Friday), August 8th (Sunday), August 9th (Monday), September 20th (Monday), September 23rd (Thursday), November 3rd (Wednesday), and November 23rd (Tuesday).

[0020] In addition, the public holidays in 2022, including Sundays, are January 1st (Saturday), January 10th (Monday), February 11th (Friday), February 23rd (Wednesday), March 21st (Monday), April 29th (Friday), May 3rd (Tuesday), May 4th (Wednesday), May 5th (Thursday), July 18th (Monday), August 11th (Thursday), September 19th (Monday), September 23rd (Friday), October 10th (Monday), November 3rd (Thursday), and November 23rd (Wednesday).

[0021] Furthermore, below, when we refer to the "7-day average" of new positive cases, we do not mean the usual "average of the day and the preceding 6 days," but rather the "average of the day and the preceding 3 days." This is because the former is a value in which the number of new positive cases has been smoothed and delayed by 3 days, but in this disclosure we simply want to see the smoothed, undelayed value.

[0022] <Central idea of ​​this disclosure> The idea behind this disclosure is based on two findings. Details will be provided below, but here we will summarize them. The first finding is that coronaviruses can be detected over a much wider range than generally believed. This means that simply examining the amount of virus in the air in a smaller area can determine the amount of virus collectively emitted by a larger group of people. So, what can be done if we know the amount of virus collectively emitted by a group? It would be natural to think that "the amount of virus is linked to the current total number of infected people in a group, and therefore we can estimate that." On the other hand, the claim that "the amount of virus is linked to the number of new infections (probably on that day) occurring within a group, and therefore we can predict future statistics" seems absurd at first glance (at least to the inventor). However, the second finding is that the latter is actually correct.

[0023] Furthermore, rather than being a finding, it is merely a point of observation that the virus infection situation tends to be similar in adjacent places (adjacent prefectures or neighboring towns, etc.) (This is thought to be due to the virus being transmitted between people traveling between those places, as well as similar human activities and weather conditions.) For example, the trends in the number of new positive cases in Tokyo and Kanagawa Prefecture are very similar.

[0024] The above three points lead to the following idea: "By simply examining the amount of virus in the air within a smaller area, we can estimate the number of new infections (perhaps on that day) within a larger area and predict the number of new positive cases in the future." Note that estimating and predicting the number of new infections and new positive cases includes not only the numbers themselves, but also, in a broader sense, the estimation and prediction of the increase or decrease in numbers (more precisely, a comparison of the numbers at two points in time). Furthermore, when it comes to infection control, it is not necessarily necessary to estimate and predict both the number of new infections and the number of new positive cases. For example, predicting the number of new positive cases is useful for securing hospital beds in advance, but on the other hand, estimating the number of new infections is more important for making early decisions about lockdowns, for example.

[0025] In this example, statistical data on the number of new infections was unavailable, so only a forecast of the number of new positive cases is presented. However, since the number of new positive cases is a by-product of the number of past new infections, it can essentially be interpreted as an estimate of the number of new infections (probably the number of new infections on that day, but this is uncertain; there may be a discrepancy of several days). Naturally, the fluctuations in the number of new infections and the number of new positive cases are very similar, but we will explain this in more detail here. As shown schematically in Figure 14, the number of new positive cases has a distribution such that "the proportion of people who test positive on day t after infection is y(t) percent of new infections." (The figure represents the cases where y(4) = 10%, y(5) = 40%, y(6) = 20%, y(7) = 5%, and y(t) = 0 otherwise. Note that, as mentioned above, the sum of y(t) for all t does not necessarily equal 100%; in this example, we used 75%. Actual statistics on coronaviruses are published by the National Institute of Infectious Diseases and other organizations.) In other words, the number of new positive cases is a delayed representation of the number of new infections from the past, which is smoothed out by weighting and adding the numbers from several days before and after (however, in reality, as will be explained later, statistical fluctuations due to factors such as the day of the week and whether it is a holiday or not are added, making the relationship more complicated). Below, we will provide more detail on the two findings above.

[0026] <Details of the first finding> First, let's look at the first finding. It's generally believed that the risk of contracting the coronavirus is greatest in rooms or trains where an infected person is present, in areas adjacent to a room where there's airflow through gaps in doors, or outdoors when approaching an infected person. Of course, the virus (whether it's infectious or not is another matter; for example, it could be virus remnants or fragments. The term "virus" will be used in the same sense below) can spread further away. However, for example, if you're at the beach, about 10 meters from the water's surface and only a handful of people are within a 30-meter radius, there's usually no need to worry about the presence of the coronavirus. However, even in such places, if the infection is spreading, the human body may be able to detect it. The detected virus is likely a mixture of viruses shed by people currently or previously present in the area, viruses shed by people in distant locations that have spread, and viruses leaking out from people in nearby buildings or cars (even in rooms with closed windows, minute amounts of the virus can enter from outside. It's likely that the virus is also leaking in the opposite direction, from indoors to outdoors).

[0027] <Details of the second finding: Relationship between the day when the number of new positive cases reaches its peak and the viral load> Next, regarding the second finding, the inventors actually walked through District A in Kanagawa Prefecture almost every day during the third, sixth, and seventh waves, observing and recording the amount of coronavirus in the air. As a result, there were a total of five days when the virus load was extremely high (for which reason, monitoring was discontinued midway due to physical strain) and significantly higher than the most recent days. Each of these findings is explained below. First, the virus load during the third wave peaked on Thursday, December 24, 2020, and was significantly higher. As described below, during the third wave, there was thought to be a correlation between the amount of virus in the air and the number of new positive cases in Tokyo 14 days later. However, 14 days after December 24 was Thursday, January 7, 2021, and the number of new positive cases in Tokyo on that day was actually 2,520, the highest number during the third wave (2,447 when first announced, but later corrected). The comparison is with Tokyo rather than Kanagawa Prefecture because prefectural borders are merely artificial, there is a great deal of traffic between Area A and Tokyo, the infection situations in both areas are similar as mentioned above, and Tokyo has a large sample size and a well-established system as the capital, making data such as daily changes the most reliable. Next, in the sixth wave, the virus load peaked on Wednesday, January 26, 2022, and was exceptionally high. As described below, in the sixth and seventh waves, there is a high possibility that the amount of virus in the air is correlated with the number of new positive cases in Tokyo eight days later. However, the peak number of new positive cases in Tokyo was 21,562 on Wednesday, February 2, seven days later (21,576 when first announced, but later corrected). Finally, during the seventh wave, there were several days with exceptionally high viral loads, in descending order: (1) Tuesday, July 26, 2022, (2) Thursday, July 21, and (3) Monday, July 25 (however, the difference between (2) and (3) was slight). Then, eight days after (1), on Wednesday, August 3, the number of new positive cases in Tokyo was 38,940, marking a peak, but not the highest of the seventh wave, and the second highest. The actual peak number of new positive cases was 40,406 on Thursday, July 28, which was seven days after (2).

[0028] Although the word "severe" was used above, the third wave on December 24, 2020 was severe at the time, but it is possible that it was less severe than the sixth and seventh waves (however, it is difficult to compare as the observation periods are far apart).

[0029] In addition, place names are represented by symbols in this disclosure. First, A and A1 are districts in Kanagawa prefecture (district A1 is located within district A). Next, P1 to P10 are locations within district A.

[0030] On the other hand, during the fourth wave, due to concerns about the highly infectious alpha variants, observations were simplified and spread out over longer periods, and the increase and decrease in viral load showed complex trends (although the relationship is unclear, the fourth wave coincided with the period when the proportion of alpha variants increased and domestic vaccinations began. The trends in the proportion of variants in Tokyo and across Japan can be seen at https: / / www.bousai.metro.tokyo.lg.jp / _res / projects / default_project / _page_ / 001 / 013 / 947 / 49kai / 202106100 8.pdf, https: / / www.nikkei-science.com / ?p=64775 are useful references. The vaccination status for Japan, not just Tokyo, can be found at https: / / info.vrs.digital.go.jp / dashboard / . Furthermore, the peak could not be identified due to the fact that there were no days during the observation period when the viral load was particularly high (the highest viral load during the fourth wave, although not particularly high, was Tuesday, April 6, 2021, followed by Tuesday, May 11, 2021-Thursday, May 13, 2021). Next, during the first wave, I was unaware of my own constitution and therefore did not observe the virus. Furthermore, during the second wave, I predicted that the number of new positive cases would peak within two weeks on Saturday, August 1, 2020, but at that time I had not yet arrived at the idea disclosed herein, and I was not actively observing the virus, so this information is for reference only. Finally, during the fifth wave, due to concerns about the highly contagious delta variant, and conversely, from around October 2021 after the peak (until late December when the sixth wave began), the virus was hardly detected, observations were only carried out intermittently and no predictions were made.

[0031] <Details of the second finding: The relationship between finer fluctuations in the virus and the number of new positive cases> More detailed fluctuations will be described later in <Details of this Example> for waves 2, 3, and the beginning of wave 4. Waves from the middle of wave 4 to wave 7 will be described in Examples 2 to 4.

[0032] <Details of the second finding: Summary> The results above and those described below suggest that increases or decreases in viral load tend to be reflected, with a delay, in the number of new positive cases. Furthermore, a similar viral load suggests an approximate relationship, in which the delayed number of new positive cases is also similar. Using this property, it is possible to predict the future number of new positive cases from the viral load, or estimate the current number of new infections, thereby enabling early infection control. The word "approximate" is used here for the following reasons: First, it is more accurate to consider the number of people in addition to the viral load (for example, according to the interpretation in the second embodiment, the amount obtained by multiplying the viral load by the number of people has a stronger correlation with the number of new positive cases). Second, in addition to the number of people, factors such as the proportion of people with antibodies or infected people, the proportion of mutant strains in the viral load, and the weather on the day (temperature, humidity, wind flow, etc.) are also relevant. Third, as mentioned above, when the viral load is expressed as the number of new positive cases, the distribution shown in Figure 14 is also relevant. Fourth, there is bias in statistics and aggregation (such as fewer tests on holidays) and errors.

[0033] Here, I would like to add some additional information about the first finding. Even in the early stages, when the number of new positive cases is extremely low, the virus is thought to be circulating in the air in large quantities in the city that can be detected by the human body. For example, the inventor first detected the mutant strain that was prevalent in the fifth wave (likely a delta mutant or the B.1.617 lineage to which it belongs) on Monday, May 24, 2021, while outdoors during observations under the second plan described below. While moving around that day, he noticed that his body felt different from usual when he ingested the virus, but he didn't think too much about it and ended up ingesting too much, which led to diarrhea and frequent urination that evening. (The inventor had never experienced frequent urination before, but after that, he experienced frequent urination a few times until around September 2021, and has not experienced it since.) At that time, the number of B.1.617 lineage infections in Japan was still low. The Ministry of Health, Labour and Welfare announced on May 21, 2021, that the number of cases of this lineage imported from overseas and discovered at quarantine stations totaled 160 as of May 7 (https: / / www.mhlw.go.jp / stf / newpage_18805.html). The Ministry of Health, Labour and Welfare also announced on May 26, 2021, that the number of cases of this lineage in Japan as of May 24 totaled only 29 (https: / / scienceportal.jst.go.jp / newsflash / 20210527_n01 / and https: / / www.fukuishimbun.co.jp / articles / - / 1325735). In other words, even if statistical data on the number of new positive cases is unavailable or insufficient, it appears possible to estimate the infection status and implement infection control measures early on simply by measuring the amount of virus in the air.

[0034] The details of this example will now be described, starting with a specific method for observing (collecting and determining or measuring) the amount of virus.

[0035] <Details of this Example: Method for Observing Viral Amount (Collection)> First, airborne coronavirus levels generally tend to be higher in crowded places than in sparsely populated areas. Furthermore, these levels vary from day to day and even within a single day. The most notable feature is that levels are low in the morning but high after the evening. The reason for this is unknown, but for example, the virus in a group's body decreases as people recover and regain strength during sleep, but then multiplies after waking due to fatigue, and may then be transmitted through contact with other people during the day. The inventor conducted observations for approximately 60 minutes per day (in reality, the virus level for that day can be roughly determined in about 10 seconds to 30 minutes, but longer observations were used to improve accuracy. Furthermore, even without active observation, the virus level in the air is automatically detected by breathing while simply traveling or doing other errands, so adding this time makes the total two or more hours). Based on the above, the inventors changed the location and time period in three ways (hereinafter referred to as Plans 1, 2, and 3) to suit the infection epidemic. The reason for the lack of a single set of locations is that when the number of infected people is low, the virus can only be detected in crowded or enclosed spaces. However, when the number of infected people is high, it becomes difficult to determine fluctuations in viral load in such places, making it easier to make judgments by examining locations and times when the virus load is only detectable when the number of infected people is high. Below, we explain the locations, starting with those with low numbers of infected people (note that this does not mean that these locations have particularly high levels of the virus; given the same number of people, it is likely that any location is the same). The first plan covers various buildings and facilities in the A1 district, as well as outdoors, from approximately 6:00 PM to 7:00 PM. The second plan covers the locations in the first plan, plus the streets between points P1, P2, and P3, and nearby roads, from approximately 6:00 PM to 7:00 PM (note that not all of these locations will be visited every time; however, as explained below, the observation results will not change significantly). The third plan is from around 16:00-17:00, going from P4 to P5 (turn right and walk on the southwest side of the main street) to P6 (cross over to the northeast side of the main street here) to P7 to the three-way intersection beyond that (however, turn left at P7 and walk around the nearby blocks for about 10 minutes before coming out at the three-way intersection) to P8 to P9 to P3 to P10, and the surrounding roads (although it is not guaranteed that you will walk all of these every time).In addition, common to the three plans above, we also took into consideration the places people visited, the interior of trains when traveling to other areas in Kanagawa Prefecture or towards Tokyo, and, during periods of infection spread, the amount of virus inside homes that entered from outside when people were at home.

[0036] When observing using the same plan, the amount of virus fluctuates from day to day, but also from location to location. However, the distribution of virus levels tends to be roughly the same from day to day, and does not change randomly from day to day. Therefore, even if the observation time is reduced to 30 minutes, for example, the amount of virus in the remaining locations can be roughly estimated, so the observation results will not change significantly, and even if they do change, it will only be by one level, as described below (L0 to L4). The scale of the distribution of virus levels by location varies in size, but when it is small, outdoors, the scale would be something like "X intersection has more virus than the surrounding area."

[0037] While these are very rough guidelines, Plan 1 is useful when the number of new positive cases (forecasted for the future) reaches several hundred, Plan 2 when it reaches several hundred to several thousand, and Plan 3 when it reaches several thousand or more. In this example, Plan 1 was used for observation during the second wave (although active observation began around early August). Plan 1 was used during the third wave until the virus was first detected outdoors in late November 2020, after which the scope was gradually expanded to Plan 2 over the course of approximately two months. Plan 2 was used primarily during the fourth wave, with Plan 1 also used when virus levels were low. Plan 2 was used primarily during the fifth wave, with Plan 3 also used near the peak in mid-August 2021. Conversely, Plan 1 was used from around October 2021, when the virus was barely detectable outdoors. Plan 1 was used during the sixth wave, with Plan 1 or Plan 2 used initially, but Plan 3 was used after January 18, 2022. In the seventh wave, only the third plan was observed.

[0038] Although not implemented, if measurements were to be performed mechanically, it would be possible to use a collection device such as an air sampler with a gelatin filter and collect air samples while traveling the same route for approximately 30 minutes at a fixed time each day, following one of the plans described above. For example, one could depart from point P2 at 6:30 PM, walk a little more slowly, and arrive at point P3 at 7:00 PM. If the amount of virus collected is insufficient, one could increase the time, extend the route, operate multiple collection devices in parallel, or change the plan (or collect and measure using all three plans in parallel and select the measurement results from the appropriate plan depending on the infection status). The viral RNA adhering to the filter could then be extracted and the copy number determined by PCR or other methods. However, it is important to note that virus amounts collected using different plans cannot be compared. Note that virus amounts can change even by moving one street over to the next. Furthermore, the situation can change depending on whether one walks left or right on a major street. Therefore, when walking the same route, it is preferable to walk on the same side of the same street. It is also best to keep the number of people on the route as even as possible. This is because when passing through extremely crowded places, the amount of virus collected there will dominate, and fluctuations in the amount of virus collected in other places will be relatively small and unlikely to be reflected in the measurement results. Similarly, virus amounts tend to be higher indoors than outdoors, so care must be taken when passing through both indoor and outdoor areas in a single collection run. For example, measuring the average amount of virus in each location in advance and setting a route that passes through areas where there is not a large difference in the amount can prevent the amount in a specific location from becoming dominant. Another effective method would be to collect virus samples using a fixed device in a single location with high foot traffic (for example, installing it on the ceiling of a subway station ticket gate in the A1 district).

[0039] <Details of this Example: Method for Observing Viral Amount (Determination of Amount)> Viral load was assessed using two methods (hereafter referred to as the "level method" and "comparison method"). Until the middle of the fourth wave (April 19, 2021), assessments were primarily made using the level method, but occasionally the comparison method was also used, and occasionally the comparison method alone. After that, assessments were made using the comparison method. The reason for the change in assessment method was that the main objective of the observations was to capture the maximum and minimum fluctuations in viral load, and the comparison method, which can track more detailed fluctuations, was more suitable. Also, in the fifth, sixth, and seventh waves, observations were also made using the third plan, which was significantly different in location and time from the previous waves, so recording using the same scale would have been confusing. Each method is explained below.

[0040] First, the "level method" judges the amount of virus on that day on a five-level scale from level 0 to 4 (hereafter referred to as L0 to L4). L0 means that no virus is detected or only a trace amount. L1 means a small amount of virus is detected, and the higher the number, the more virus is detected. Note that these levels are determined subjectively based on the body's response, so the standards are likely to fluctuate over the long term.

[0041] Next, the "comparison method" compares the viral load to a previous time point and determines whether it is higher, lower, or similar. Here, a "time point" in the past refers to a specific day or a specific week, typically the previous day, seven days ago (the same day of the week one week ago), or the entire previous week. As an application of this method, when comparing two different time points (e.g., A and B), if the viral load is higher than that at time A but lower than that at time B, it can be determined to be intermediate between the two. Records in the comparison method are kept in free-form text rather than symbols, as in the level method. Note that simple comparisons of viral loads detected using different plans are meaningless and therefore not performed. However, even if the plans are different, amounts detected under partially overlapping conditions (the same time and place, or adjacent time periods and places where similar viral loads can be detected, even if not exactly the same) can be compared if the overlap is large enough.

[0042] The advantage of the comparative method is that it can track more minute fluctuations than the level method. For example, the viral load on one day may be L2, and the next day it may also be L2 but slightly higher than the previous day. With the level method, both would be recorded as L2, but with the comparative method it is possible to record the difference. On the other hand, the advantage of the level method is that it can compare the viral load on two appropriate days, although not as precisely as with the comparative method (except when the two days are far apart and the baseline is thought to have fluctuated).

[0043] As an exception, the level method recorded the results as D1, D2, and F. The inventor initially considered these to be "levels" of viral load, but it gradually became clear that they should not be treated as equivalent. However, due to this background, they are still recorded as levels. Details are explained below.

[0044] First, D1 and D2 indicate that the detection of "particularly strong virus" (a sudden, strong reaction in the throat, causing hoarseness; this does not occur in L0-L4) occurred once and twice, respectively, on that day. When this "particularly strong virus" was detected, it was recorded as D1 or D2, regardless of the amount of virus in other locations on that day (L0-L4 or F, described below). What is unique about D1 and D2 is that, while the amount of virus usually does not change significantly after moving a few meters, the detection of D1 and D2 is sudden and is not felt if the person moves even a few meters away from the location. Furthermore, the detection has only ever occurred indoors or on a train. This is likely because an infected person was nearby at the time, or in the not-too-distant past, and the inventor inhaled the infected person's breath. As will be explained later, from August 1, 2020, when recording began, to August 7, 2022, there were four D1 detections and seven D2 detections, but there was never a D3 or higher detection (i.e., a "particularly thick virus" was detected more than three times a day).

[0045] Next, F was a classification used only in the early stages of observation (from August 1, 2020, when recording began, to December 2020), and although recorded nine times, it has not been used since. In the early stages of observation, the virus was generally detected only in limited locations with high population densities, such as indoors and on trains, and the amount was recorded as a level L0–L4 based on a comprehensive assessment of the amount after visiting these scattered locations. However, there were occasional days when trace amounts were unexpectedly detected even in sparsely populated indoor locations. Initially, not knowing what this meant, we recorded it as an F. However, we later realized that F simply represented a form of daily fluctuation in the amount of virus in the same location, and have since incorporated it into the L0–L4 levels mentioned above and no longer use it as a separate record. In other words, the virus can be detected anywhere, not just in densely populated locations (even this was not obvious at first), but just as the amount fluctuates daily in densely populated locations, it also fluctuates daily in sparsely populated locations. In sparsely populated areas, the virus is usually so low that it cannot be detected, but the days when it increased enough to be detected were recognized as F, and this should not be treated as special simply because of that. Initially, if such detection occurred, it was recorded as F regardless of the amount of virus in other locations. However, on two occasions in December 2020, both the level detected in other locations and F were recorded, so there is some fluctuation in the standard. However, F is not very important in the following predictions.

[0046] To be more precise, I will explain the details. Initially, the only physical reaction stronger than L4 was the detection of "particularly dense virus," so D1 and D2 were treated as "levels" L5 and L6, respectively. However, on December 24, 2020, when the viral load was at its highest during the third wave, the amount exceeded the L4 level up to that point, and "particularly dense virus" was not detected. This meant that it was inappropriate to label D1 as L5, but I could not rewrite past records, so I was forced to record it as L4 with the annotation "extremely dense viral load." However, since then, no similar incidents have occurred when observing using the level method (this may be due to the fact that the observation location has been changed to an area with a lower amount of virus as the infection spreads. Also, the virus amount was also intense at the peak of the 6th and 7th waves mentioned above, namely January 26, July 21, July 25, and July 26, 2022, but was only recorded using the comparative method), so for record purposes, D1 and D2 are treated as levels L5 and L6.

[0047] <Details of this Example: Details of Observation and Prediction (Introduction)> Below, we discuss the results of individual observations and predictions. Just to be clear, the inventors did not conduct statistical analysis such as the effective reproduction number. Furthermore, for those today who look back on the progress of the infection situation since January 2020, when the first cases were discovered in Japan, as a confirmed past, the predictions in this example may appear to be merely tracing fluctuations. However, it should be emphasized that the inventors are not making predictions after the fact; they only know the number of new positive cases up to that day, and do not even know whether the numbers will increase or decrease thereafter (in the graph, there is no right-hand side of the day). Note that while some predictions in this example were recorded after midnight on the day, the content was determined on the day, so they will be described as being recorded on the day. All predictions written below will be listed, including those that are more of a record than a prediction, or predictions that were incorrect. However, while the inventors have observed and recorded minor fluctuations in addition to those described here, they have excluded those that cannot be interpreted as predictions, such as those that are indistinguishable from errors.

[0048] <Details of this Example: Details of Observation and Prediction (Delay in the Number of New Positive Cases in the Second and Third Waves)> This section discusses the correlation between viral load and the number of new positive cases 14 days later during the third wave mentioned above. First, to explain the 14-day delay, we will discuss predictions based on "particularly dense virus" D1 and D2, which are not strictly the same as predictions based on the "viral load" in the air (L0 to L4 in the level method), which is the central idea of ​​this disclosure. However, as described below, the detection of D1 and D2 cannot be expected to be accurate, so the predictions are for reference only. Furthermore, it should be noted that the 14-day figure is not universal and can change depending on factors such as the existence of mutant strains with different incubation periods and the ease of testing (for example, in Japan, until the situation changed around May 2020, many people did not get tested unless they had a fever for more than four days).

[0049] The inventor began recording the amount of virus in the air (although not daily) during the second wave on August 1, 2020, but did not initially believe that the amount of virus could predict the number of new positive cases. Initially, he noticed a trend toward an increase in the number of new positive cases about two weeks after detecting D2. Because whether or not an individual passes near an infected person during their travels is determined by chance, detecting D1 or D2 is largely a probabilistic factor (however, D2 is thought to be more likely than D1 to indicate the spread of "particularly dense virus" in an area). Furthermore, the number of times "particularly dense virus" is detected roughly doubles if the time spent indoors or on a train doubles. However, since he did not initially believe such accuracy was necessary, he only recorded the number of times, regardless of whether the time spent outdoors increased or decreased (on the other hand, with "virus amount" detection, even if the inventor's time outdoors doubled, the accuracy of the judgment would change, not the amount). For the reasons stated above, the accuracy of the prediction is not high. Furthermore, for some reason, the virus amount is often not that high the day after detecting D2.

[0050] Specifically, D2 was detected on August 9th (Sunday), September 2nd (Wednesday), September 9th (Wednesday), September 10th (Thursday), October 1st (Thursday), October 22nd (Thursday), and December 7th (Monday) during the second and third waves. On the other hand, D1 and D2 were not detected after the fourth wave. (This may be due to factors such as increased outdoor observation time and increased vaccination in Japan since the fourth wave. For reference, D1 was detected on December 29th (Tuesday), 2020, February 23rd (Tuesday), May 16th (Sunday), and September 5th (Sunday). Initially, we expected the number of new positive cases to increase approximately two weeks after D1, but D1 detection alone is likely to be largely coincidental and not useful for prediction.) The reason we include not only the date but also the day of the week here is that the number of new positive cases varies depending on the day of the week. Because there are fewer tests on Sundays and holidays, it is said that there tends to be fewer new positive cases the following day (Mondays, etc.). Therefore, some new positive cases may appear later, either the day after that, or may recover while waiting for a test and not appear in the statistics. Therefore, if the 14 days after the detection of D2 overlaps with these days, it is difficult to determine whether the number of new positive cases has increased.

[0051] Based on the above records alone, it is difficult to conclude that D2 detection is effective as a prediction. However, from the inventor's perspective, since he thought that the second wave would subside as of September 2nd, he was intrigued by the fact that it began to spread again about a week later, and began to wonder whether D2 detection might be effective in predicting an increase in the number of new positive cases. What was particularly striking about the detections on October 1st and October 22nd was that they showed a sharp increase of over 250 people exactly 14 days later (Thursday, October 15th and Thursday, November 5th), and this "14-day" figure became a focus of attention. (To repeat, if we look back at the confirmed past, the number of new positive cases during this period rose and fell cyclically, so it may seem easy to make predictions by tracing these fluctuations. However, this is something that can only be found out in hindsight, and at the time of detection, the entire future is uncertain. Incidentally, the results of the detections on September 9th and September 10th were unlikely to show up in the statistics because 14 days later were Wednesday, September 23rd and Thursday, September 24th, after the holidays. It is possible that this showed up as a sharp increase in the number of new positive cases on Saturday, September 26th, but this is unknown.) Furthermore, when it was detected on December 7, 2020, the infection was already spreading, with the number of new positive cases exceeding 300 for several days. This was different from when D2 was detected up to that point (August 9 to October 22, 2020), so it was difficult to judge what the number of new positive cases would be 14 days later on Monday, December 21, 2020. In the end, it was predicted that the number would increase, but this was incorrect.

[0052] Incidentally, in observations of the second, third, and fourth waves, the detection of D2 or a sudden increase in viral load and the subsequent sudden increase in the number of new positive cases 14 days later often occurred on Thursdays for some reason. Some literature interprets this as meaning that "there are days during the week when the counting process for new positive cases solidifies, resulting in a tendency for the number of new positive cases to increase on Thursdays" (e.g., https: / / www.asahi.com / articles / ASNBY7316NBPUTIL01B.html). However, this interpretation alone cannot explain the fact that even on the same Thursday, there are days when the number is particularly high and days when it is not. This example also predicts a surge on days other than Thursdays, such as Wednesday, March 3, 2021, as described below. However, if this interpretation of the literature is a factor, if there were no days when the counting process solidifies, the "14-day" delay could have been earlier or later, or could have been a wider range, such as "13 to 15 days."

[0053] <Details of this example: Details of observation and forecast (Wave 3)> From here on, we return to predictions based on "viral load." The inventor was unable to predict the increase in the number of new positive cases during the approximately two weeks from Saturday, November 7, 2020 (until around Saturday, November 21, when 539 cases were recorded) as the infection spread. (The cause of this increase is unknown, but it may be related to the fact that the dominant virus in Japan was replaced by B.1.1.214 from B.1.1.284 around the same time.) The reason for not predicting this increase is that the increase in the number of new positive cases after detecting D2 in the past was transient, and also because no significant change in viral load was detected 14 days prior to this period, i.e., from Saturday, October 24 to Saturday, November 7. However, on Monday, November 16th, three of the last four days had been L0. Looking back at the viral load over the last 14 days (Tuesday, November 3rd to Monday, November 16th), subjectively, it seemed to have remained roughly stable. Looking at the records, there were many L0 days, and apart from minor fluctuations, it appeared to be fairly constant (though there were many L0 days, levels L2, L3, L4, and F were also occasionally seen). At the time, I had not yet clearly arrived at the central idea of ​​this disclosure, and simply assumed that if there were more L0 days, the number of new positive cases would decrease. Therefore, I predicted two possibilities: "The number of new positive cases will decrease over the next 14 days (Tuesday, November 17th to Monday, November 30th). There is also a possibility that it will remain roughly stable." The result appeared to be roughly stable, excluding fluctuations by day of the week. As mentioned above, this prediction for the period from November 7th to November 30th has the drawback of not being able to predict that the number of new positive cases would increase to more than 500. However, after seeing this result, the inventor began to consider not only predictions based on D2, but also predictions based on viral load, that is, the hypothesis that "if the viral load is the same, the number of new positive cases 14 days later will also be the same."

[0054] The virus was first detected outdoors in late November 2020 (until then it had only been detected indoors or on trains), and since then, virus levels outdoors have also been included in observations.

[0055] Then, on Thursday, December 3, 2020, the viral load increased further, reaching the highest level in the past month. However, because the prediction based on viral load was still in the hypothetical stage, we made a somewhat conservative prediction that "the number of new positive cases two weeks later may have increased by another level." In fact, 14 days later, on Thursday, December 17, the number of new positive cases increased sharply to 822, the highest number in the past month. This suggests that not only D2, but also the sudden increase in viral load is reflected in the sudden increase in the number of new positive cases 14 days later.

[0056] Then, on Thursday, December 24th, the viral load rose sharply to a level never before detected, so it was recorded as L4 with the annotation "extreme viral load" (the amount on that day was the highest observed during the third wave). It then dropped significantly the following day, Friday, December 25th. At this point, because the infection was spreading at an unprecedented rate and the situation was different from previous days, we were not confident that the peak of the viral load (a sharp increase followed by a sudden decrease) would also predict the peak in the number of new positive cases, so we simply recorded that "the number of new positive cases may increase on Thursday, January 7th, 2021." In fact, as mentioned above, the number of new positive cases also peaked on January 7th.

[0057] For reference, as of Sunday, January 31, 2021, I recorded the following: "The current number of infected people seems intuitively about the same as it was in mid-December 2020. The current number of infected people in Tokyo is also roughly the same as it was in mid-December." While this may seem like a tautology at first glance, the first half of the statement means that the "viral load 14 days ago" for both "now" and "mid-December" is subjectively about the same. Meanwhile, the second half, "number of infected people," refers to the "number of new positive cases." In other words, the overall purpose of the record was to confirm the hypothesis that the number of new positive cases will be about the same 14 days after a day with a similar viral load. However, this cannot be verified by comparing records of levels, etc. (It is difficult to compare the viral load around Sunday, January 17, 2021 with the viral load records from around Thursday, November 26, 2020 to Sunday, December 6, 2020.) Furthermore, this record is a retrospective check, not a prediction, and is therefore for reference only.

[0058] Also, on Wednesday, January 20, 2020, he wrote, "The number of infected people has remained high for about a week, neither improving nor worsening. It seems to have improved a little in the past few days." (The wording is vague, as the purpose was to record rather than predict.) Note that "number of infected people" here refers to "number of new infections." In other words, this record summarizes the virus load over the past week, recording L4 from Wednesday, January 13 to Friday, January 15, and L3 on Sunday, January 17, Monday, January 19, and Wednesday, January 20 (no measurements were taken on the 16th and 18th). However, looking at the number of new positive cases 14 days later, from Wednesday, January 27 to Wednesday, February 3, it is lower than the previous week. While this statement is primarily intended as a record rather than a prediction, it is difficult to say that it is an accurate prediction.

[0059] <Details of this example: Details of observations and predictions (early stage of the fourth wave)> Next, I will discuss the turning point from the third wave to the fourth wave. As the third wave subsided, scattered decreases in viral load began to be observed around February 1, 2021, and the viral load was particularly low from February 12 (Friday) to February 21 (Sunday) (only L0-L2). However, it increased to L3 on February 22 (Monday), and then, after detecting D1 on February 23 (Tuesday), L3-L4 continued until March 1 (Monday). Therefore, on February 23, I simply predicted that "the number of new positive cases may increase slightly 14 days later on March 9 (Tuesday)." As described below, I made a more detailed prediction on March 8 (Monday). And indeed, 14 days after this particularly low virus period (from February 26 (Friday) to March 7 (Sunday)), the number of new positive cases was close to its minimum.

[0060] From around Tuesday, March 2, 2021, to Saturday, April 10, 2021, people were wary of the pathogenicity of the alpha variant and switched to simpler observations, such as avoiding crowded places. There were also days when no observations were conducted at all. After this period, the number of records using the level method decreased.

[0061] On Wednesday, March 3, 2021, the virus continued to increase, and the number of new positive cases appeared to be close to the number 14 days prior, when the number of new positive cases was between 400 and 500 (although records were not kept, this is believed to have occurred around early December 2020). Subjectively, we determined that the number was closer to 500 than 400, and predicted that the number of new positive cases 14 days later, on Wednesday, March 17, 2021, would be approximately 500. While the result was 409, this is a highly accurate prediction based on the human body's senses, and strongly supports the hypothesis that the number of new positive cases 14 days later can be predicted by the amount of virus. This is the first time in about a month (since Thursday, February 18) that the number of new positive cases has exceeded 400, and the graph also shows a notable increase.

[0062] <Summary of this Example> Details of the observation and prediction results after the fourth wave, and for the sixth and seventh waves, will be provided in Examples 2 to 4, and this Example will be summarized here. As mentioned above, although this is an inexact method of observing and predicting based on the human body's senses, it is possible to some extent to predict the increase / decrease, peak, and number of new positive cases across Tokyo by simply examining the amount of virus in the air inhaled by a single person while walking.

[0063] Note that infection control was not performed in this example. However, of course, if the infection situation could be estimated and predicted, infection control measures such as securing hospital beds could be implemented early. For example, if it was predicted on March 3, 2021 that "the number of new positive cases on March 17, 14 days later, would be approximately 500," measures such as establishing a treatment system could be taken early. Furthermore, if measures to prevent the spread of infection had been taken when the increase in the virus was detected around February 23, 2021, the fourth wave of the epidemic could have been prevented or delayed. Furthermore, not only during the infection expansion phase but also during the contraction phase, it would be possible to control the spread of infection by increasing the number of people going out and resuming economic activity early.

[0064] Furthermore, being able to estimate and predict has a positive effect on people's psychology. If estimation and prediction are not possible, even if it is realized after the number of new positive cases increases that "infection control should have been implemented 14 days ago," it is not clear "what specifically should we do now as a group or as individuals?" This can lead to psychological reluctance and infection control can gradually become lax. In contrast, being able to estimate and predict early has the great advantage of making it clearer what needs to be done now and allowing people to take proactive measures.

[0065] As noted above, the number of new positive cases is a delayed, secondary quantity representing the number of past new infections, so this example is essentially an estimate of the number of new infections. So why is estimation possible using viral load? The first hypothesis is that the amount of virus in the air is mostly comprised of virus released by recent new infections in the surrounding area (i.e., on the same day or several days prior), and as a result, the number of new infections can be estimated from the viral load. When a person is infected with a virus, the amount of virus in their body changes over time, initially low, then increasing, reaching a maximum several days later, and then decreasing again. Therefore, it can be thought of as the majority of the virus in the air being released on the day when the viral load in the body of the most recent new infection was at its maximum. If this hypothesis is correct, dividing the amount of virus in the air by the number of people in the surrounding area yields an amount roughly proportional to the number of new infections in that area. However, in the case of coronavirus, it is said that the amount of virus in the body reaches its maximum roughly on the day on which symptoms appear (one study published on March 23, 2021, found that viral excretion reaches its maximum two days after symptoms appear: https: / / www.amed.go.jp / news / release_20210323-02.html). This means that the 14-day delay seen in the second and third waves is roughly equal to the number of days between the onset of symptoms and the announcement of the number of new positive cases, but this is slightly longer than empirical evidence suggests.

[0066] The second hypothesis, described in detail in the second embodiment, is that the amount of virus in the air represents the number of new infections on that day, due to the ingestion of the virus and subsequent infection. Taking the detection of D1 and D2 described in this embodiment as an example, it is conceivable that on the day the inventor detected D1 and D2, other people also ingested concentrated virus, and that some people were infected as a result. It can be said that the number of such people was greater on the day D2 was detected than on the day D1 was detected. Similarly, on days when the inventor detects a high amount of virus, other people in the surrounding area are also exposed to a large amount of virus, and some may accidentally ingest a large amount (by approaching an infected person, for example) and become infected. It can be said that the higher the amount of virus detected by the inventor, the higher the probability of infection and the greater the number of new infections (assuming the number of people in the surrounding area remains the same). If this hypothesis is correct and the second embodiment can be applied, multiplying the amount of virus in the air by the number of people in the surrounding area will yield an amount roughly proportional to the number of new infections in that area. Based on this hypothesis, the estimation of the number of new infections in this example can be said to be an approximation assuming a constant number of people.

[0067] In addition, there may be a more complex mechanism that relates the amount of virus to the number of new infections, but this is not clear at this time.

[0068] It is possible that the amount of virus in the air may decrease on rainy or windy days, but I have never noticed any difference beyond the daily fluctuations in the accuracy of the human body's detection. However, I have noticed a decrease in the virus for a short time when a fresh breeze blew.

[0069] Here, we compare our method with several existing technologies. First, there are already systems that measure the amount of PM2.5, photochemical oxidants, pollen, and other substances in the air and issue alerts. While these systems determine whether the target substance is detected in sufficient quantities to be harmful to the human body, the method disclosed here is unique in that it does not address whether the amount of virus (or more generally, pathogens) detected is harmful to the human body. That is, it focuses on fluctuations in the virus rather than the absolute amount, and even if the detected virus amount is extremely small, it indirectly estimates the number of new infections in the surrounding area (in this example, the entire Tokyo metropolitan area). This characteristic is thought to be one of the barriers to achieving the idea disclosed here (in infection control, the focus is on the amount of virus that poses a risk of infection, and there is little need to accurately determine the amount of virus when there is no risk). Another barrier is that airborne PM2.5 and other substances are a "cause" of harm to the human body but not a "result," whereas airborne viruses are both a "cause" of harm to the human body (infection occurs when the virus enters the body) and a "result" (virus is released from an infected person). Therefore, attention may be drawn to the "result" aspect, which is not present in PM2.5 and other pollutants. For example, when the virus is present in trace amounts, there is no need to worry about infection, so it is easy to focus only on the "result," i.e., the fact that an infected person is nearby, rather than the "cause." The second hypothesis and second embodiment described above are characterized by focusing on the "cause." On the other hand, even when focusing on the "result," it is not obvious whether it correlates with the "number of new infections" as in the first hypothesis, and it seems more natural to assume that it correlates with the current "total number of infections."

[0070] Second, technology has already been developed to measure the amount of coronavirus in sewage and estimate or predict the infection status. This technology also focuses on the "result" of the virus, rather than the "cause" of the virus.

[0071] Thirdly, while there may already be ideas for collecting air samples on trains and other vehicles to determine whether or not the virus is present, it does not appear that any consideration has been given to estimating the number of new infections from the amount of virus.

[0072] <Supplementary information: The inventor's constitution and how the human body detects coronavirus> The inventor can detect the presence of coronavirus (or its remnants) in the air, including its concentration. While this indirect proof is a prediction in this example, it is an undeniable fact for the inventor (the inventor does not have particularly strong skin or mucous membranes, but believes that this may be due to his constitution, which makes him more susceptible to coronaviruses). When the virus concentration is high, a unique sensation occurs the moment one inhales it (the sensation of a foreign object passing through one's airways, something never felt before the coronavirus pandemic). Furthermore, outdoors, if there is no wind, the virus concentration can be detected by stopping and taking a few breaths as a degree of discomfort in the back of the throat (probably due to inflammation, which reaches a different area than the pain caused by a normal cold). If the discomfort is mild, it can be felt to subside within a few tens of seconds. Furthermore, by walking outdoors while paying attention to changes in this discomfort in the throat, one can often detect changes in the virus concentration over a distance of about 10 to 100 meters, allowing the inventor to sense the virus concentration as if one were visually detecting the concentration of smoke.

[0073] It is certainly possible to interpret the above bodily reactions as being caused by something other than a virus. However, if they were caused by environmental pollutants, for example, it would be difficult to explain why they were more common indoors than outdoors (although this depends on the location and time of day, so is not always the case). Conversely, if a substance were the cause of something like sick building syndrome, it is unlikely that it would be detected in large quantities outdoors. Furthermore, if it is detected in large quantities in crowded places, it is natural to assume that it is a substance of human origin. Furthermore, given that this has never been felt for such a long period of time before the pandemic, and that the strength of the reaction is linked to the epidemic, the most plausible explanation is that it is a coronavirus.

[0074] Furthermore, the body's reaction to different mutant strains can differ when ingested, and it is possible to distinguish between them if the amount is appropriate. For example, when a large amount of the mutant strain that was prevalent in the fifth wave (thought to be a delta mutant or the B.1.617 lineage strain to which it belongs) is ingested, it causes diarrhea and frequent urination. Similarly, when a large amount of the mutant strain that was prevalent in the fourth wave (thought to be an alpha mutant) is ingested, reactions occur in the left chest, left armpit, left toe, etc. The mutant strain that was prevalent in the sixth wave (thought to be an omicron mutant) also initially caused reactions similar to those in the fourth wave.

[0075] However, the inventor was not initially convinced that he could detect coronaviruses. It all began when he caught a cold in February 2020 (presumably due to coronavirus infection). He had a high fever for nearly a week, and even after the fever subsided, his throat continued to feel unwell for months. For example, he would suddenly sense a "particularly thick virus" while talking. The inventor initially thought the cause was the virus remaining in his throat and not disappearing. However, after the state of emergency measures implemented in the Tokyo area on April 7, 2020, were lifted on May 25, and people began going out more frequently, his throat condition, which had temporarily improved, began to worsen when he went out. This was particularly true when he visited a store, which can be explained by the increased number of people and the resulting significant increase in the amount of virus in the store. After observing the conditions that triggered his body's reactions when he went out, he gradually came to believe that his throat condition was worsening in response to the virus in the air.

[0076] On Saturday, August 1, 2020, the number of new positive cases in the Tokyo area was increasing during the second wave of the virus, but for the inventor, the week leading up to that point had been a period in which he barely felt any virus when going out. Because airborne viruses are a mixture of those released by an unspecified number of people, a low amount of virus means that statistically there should be a low number of infected people (however, at the time, he believed that the amount of virus correlated with the total number of infected people, not just new infections). If this was the case, he assumed that the peak of the spread of infection had already passed, and so he recorded it. The highest number of new positive cases during the second wave was 472 on August 1. This is the initial prediction of this example.

[0077] [Example 2] This example explains the observations and predictions from the middle of the fourth wave onwards. The predictions in this example were often not as clear or incorrect as those in Example 1 (the reasons for this are unknown, but may be related to the increase in the proportion of alpha variants, which have significantly different properties from previous cases, and the widespread use of vaccinations), but to be fair, all predictions are listed.

[0078] <Details of observations and forecasts (mid-stage of the 4th wave and beyond)> Looking back at the 14-day period from Monday, March 8, 2021, the virus load over the past 14 days was higher than the previous week from Monday, February 22 to Sunday, February 28 (strictly speaking, up until Monday, March 1), as mentioned above. After that, the virus load increased further, including on the aforementioned March 3. However, the virus load in the days immediately preceding Sunday, March 7, and on March 8 was lower than on March 3. Therefore, we predicted that the virus load would increase slightly this week (Monday, March 8 to Sunday, March 14), and then increase further next week (Monday, March 15 to Sunday, March 21). After that (Monday, March 22 onward), it would settle down again (the expression "settle down" is ambiguous, but it means "decrease"). As a result, the prediction for "this week" can be said to be correct, as explained above. Looking at the graph of new positive cases for "next week," the prediction was close to correct, but the prediction for "after that" was incorrect. However, although it was an afterthought, based on observations of viral load, the period "after" should have been "the last few days including March 22," rather than "after March 22." In other words, if they had predicted that "counts will increase around Wednesday next week (March 15-March 21), but will calm down in the last few days before March 22," it would have been closer to the correct answer.

[0079] On Wednesday, March 17, 2021, the virus load was at a similar level to that seen on March 3, and so I predicted that "the number of new positive cases in the week of Wednesday, March 31, 14 days later (approximately Monday, March 29 to Sunday, April 4), would be similar to that of this week, 14 days after March 3 (approximately Monday, March 15 to Sunday, March 21)." The results were not the same, and there was an increasing trend, so this prediction is difficult to say was correct (and, although retroactive, it was also a mistake to predict the second half of the week based only on the virus load in the first half). However, the number of new positive cases on Wednesday, March 31, was 414, almost the same as the 409 on Wednesday, March 17. Furthermore, one day later, on Friday, March 19, the virus load was lower than on the 17th, but still at a similar level. The number of new positive cases 14 days later, on Friday, April 2nd, was 437 (originally announced as 440 but later corrected), again similar to 414. The middle day, Thursday, March 18th, had significantly lower viral loads than the days before and after, but the number of new positive cases 14 days later, Thursday, April 1st, was conversely higher than the days before and after, at 475. This can also be interpreted as an increase due to the combined total of people who were infected on March 17th and tested positive 15 days later, and people who were infected on March 19th and tested positive 13 days later.

[0080] On Sunday, March 21, 2021, we looked back at the virus load over the past 14 days (Sunday, March 7 to Saturday, March 20) and predicted that "this week (Sunday, March 21 to Saturday, March 27) will decrease slightly in the first half but increase again in the second half. Next week (Sunday, March 28 to Saturday, April 3) will increase by the same or more than last week (Sunday, March 14 to Saturday, March 20)." The reason for this is that, first, in the first week (Sunday, March 7 to 13), the virus load was not as high as around March 8, but subjectively increased thereafter. Furthermore, for the second week (Sunday, March 14 to 20), we subjectively took into account the virus load from March 18 to 20 (including the fact that March 19 was similarly high to the 17th) in addition to the prediction recorded on March 17. The results for "this week" are difficult to verify because changes within a week vary by day of the week, but the number of new positive cases can be said to be on an increasing trend throughout the week, so it's half correct. As for the next "next week" part, it's correct in the sense that it has increased, but it was unexpected that the degree of increase was greater than subjective.

[0081] On Tuesday, April 6, 2021, it was recorded that "the amount of virus is extremely high and is increasing with each passing week" (the wording is vague, as the main purpose was to record rather than predict). Ultimately, this day was the highest amount observed during the fourth wave, but due to thinning of observations, it was not recognized as the peak of the virus amount at this point. Furthermore, while it can be said that the number of new positive cases over the following 14 days "increased with each passing week," the number of new positive cases 14 days later, on Tuesday, April 20, was 710 (711 when first announced, but later corrected), not the highest of the fourth wave.

[0082] During the following period, from Sunday, April 11, 2021, to Sunday, May 16, 2021, the virus load showed complex trends. The predictions during this period were also incorrect. The reason for this is unclear, but it may be related to factors such as the virus becoming more prevalent as a result of alpha variants. (According to Tokyo Metropolitan Government statistics, the proportion of N501Y variants, including alpha variants, rose sharply from under 10% to approximately 30% starting from the week of Monday, March 29, 2021. Furthermore, it rose sharply to approximately 60% starting from the week of Monday, April 19, 2021: https: / / www.bousai.metro.tokyo.lg.jp / _res / projects / default_project / _page_ / 001 / 013 / 860 / 47kai / 2021052708.pdf). The incorrect predictions during this period also highlight the specificity of correct predictions or similar results during other periods. Below we explain each prediction and its results.

[0083] The viral load decreased significantly between Sunday, April 11th and Sunday, April 18th, 2021 (14 days later, Sunday, April 25th and Sunday, May 2nd). Therefore, on Friday, April 16th, we predicted that "the number of infected people will peak next week (until Saturday, April 24th) and then decrease rapidly. If the incubation period of the alpha variant is short, the number of infected people will decrease a little before that." However, the number of new positive cases between April 25th and May 2nd continued to increase, so this was incorrect.

[0084] Please note that there was a long holiday from Wednesday, April 29th to Wednesday, May 5th, 2021 (although Thursday, April 30th was a weekday). The large decrease in the number of new positive cases around Wednesday, May 5th is likely due to a decrease in the number of tests due to the holiday.

[0085] Around Monday, April 19, 2021 (14 days later, around Monday, May 3), the amount of virus began to increase again. Therefore, on Wednesday, April 21, it was predicted that "the number would begin to increase from May 3." This prediction is difficult to verify because it is based on the assumption that the number of new positive cases would decrease until May 2, and as mentioned above, it is likely that the statistics will be affected by the consecutive holidays, but it is difficult to say that it is correct (if you look at the numbers alone, it is incorrect).

[0086] On Friday, April 30, 2021, the report looked back at the most recent viral load and recorded that "the viral load for last week and this week (Monday, April 19, 2021 to Friday, April 30, 2021) remained roughly flat." This is not an explicit prediction, but if we interpret it to mean that "the number of new positive cases 14 days later, from Monday, May 3 to Friday, May 14, will also remain flat, excluding fluctuations due to the day of the week," it is difficult to say that this is correct (as with the previous prediction, it is difficult to verify, but if we look at the numbers alone, it is incorrect).

[0087] Furthermore, the period from Tuesday, May 11th to Thursday, May 13th, 2021, saw the second-highest viral load after April 6th, the peak of the fourth wave (note that signs of an increase began to appear around Sunday, May 9th). Then, on Sunday, May 16th, as mentioned above, D1 was detected, leading to a prediction that "the number of new positive cases will increase significantly from Sunday, May 23rd to Sunday, May 30th (14 days after signs of an increase began to appear)." However, in reality, the number has decreased significantly, making this prediction incorrect. Also on May 16th, looking back at the viral load over the past week from Sunday, May 2nd, I subjectively predicted that "the number of new positive cases will remain roughly flat or increase slightly from Sunday, May 16th to Saturday, May 23rd." While changes within a week are difficult to verify due to day-of-the-week fluctuations, this prediction is also incorrect, as it appears to be decreasing.

[0088] [Example 3] This example explains the observation and prediction of the sixth wave. As mentioned above, no prediction was made for the fifth wave.

[0089] <Details of observations and forecasts (Wave 6)> During the sixth wave, which began around early December 2021 and was dominated by the Omicron variant, observations focused on larger fluctuations, such as weekly, rather than small daily fluctuations. However, if there were days with characteristic virus loads, these were also recorded. Furthermore, due to vigilance against the highly infectious Omicron variant, observations were only conducted intermittently until January 17, 2022 (after January 18, once the characteristics of the mutant strain were understood, observations were conducted almost daily). Furthermore, during the sixth wave, recordings were made using the "comparison method" rather than the "level method."

[0090] Regarding the "delay period" until the detected viral load is reflected in the number of new positive cases, we assumed it was 14 days for the second, third, and fourth waves. However, more than six months have passed since then, and it's possible that the availability of testing and statistical systems have changed since then. However, the only information we had was that the incubation period for the Omicron variant was approximately three days, about two days shorter than the conventional strain's incubation period of approximately five days (https: / / www.niid.go.jp / niid / ja / diseases / ka / corona-virus / 2019-ncov / 2484-idsc / 10434-covid19-43.html). Furthermore, we assumed that even if the system had changed, it wouldn't be significant, so we began our predictions for the sixth wave assuming a tentative "12 days." While it's impossible to pinpoint the exact time, looking back at our predictions and their results, the most likely time is eight days.

[0091] Furthermore, the period from Friday, April 29, 2022 to Thursday, May 5, 2022 was a long holiday (only Monday, May 2, was a weekday, but many people likely took the day off), and the number of new positive cases increased (meaning higher than the same day of the previous week) exactly eight days later, from Saturday, May 7 to Friday, May 13. This contrasts with the decrease seen in the periods before and after this period. If this was due to increased outings during the holiday, it is likely that new infections during the holiday would have appeared in the statistics as new positive cases eight days after infection. Even within the same sixth wave, the situation changed between the peak (January-February 2022) and the holiday, with the dominant Omicron variant shifting from BA.1 to BA.2. However, this result serves as a guideline for determining the delay period.

[0092] Incidentally, in Example 1, two hypotheses were presented as to why the number of new infections can be estimated from the amount of virus. When combining the first hypothesis, i.e., the interpretation based on "the amount of virus shed by newly infected individuals," with the statistics from this long holiday period, the "delay period" until the inventor's detection of the virus is reflected in the number of new positive cases must be several days shorter than eight days (the number of days until the amount of virus shed after infection reaches its maximum; approximately three days if it is equal to the incubation period), which deviates from the results described below. This supports the second hypothesis, i.e., the interpretation based on "the amount of virus taken in by newly infected individuals," rather than the first hypothesis.

[0093] It was assumed that the sixth wave would see a rapid spread of infection, based on the prevalence of the Omicron variant overseas and the rapid increase in the number of new positive cases in Japan in the early stages (until around early January 2022). Therefore, observations were started with the primary objective of determining how long and to what extent the virus would increase, rather than focusing on minute fluctuations in the amount of virus. On Wednesday, January 26, 2022, the amount of virus was exceptionally high (one could even say intense) since observations of the sixth wave began, and the difference between the days before and after was also large. Therefore, the following day, Thursday, January 27, it was predicted that the number of new positive cases would peak on Monday, February 7, 12 days after January 26. However, since testing was saturated, with Tokyo Metropolitan Government starting the "suspected patients (deemed positive)" system from Saturday, January 29, 2022 (the Ministry of Health, Labor and Welfare announcing the recognition of suspected patients on Monday, January 24; see, for example, https: / / www.tokyo-np.co.jp / article / 159276), it was not expected that the results of the prediction would be clearly visible. The actual number of new positive cases peaked seven days later, on Wednesday, February 2.

[0094] Next, on Monday, January 31, 2022, there was a sharp increase, although not as large as on January 26, and so it was predicted that 12 days later, "the number of new positive cases on Saturday, February 12, will peak at a smaller number than on February 7." This is incorrect, as the result shows a decrease for a Saturday. Also, since "12 days later" is a tentative figure, looking at the aforementioned "eight days later," Tuesday, February 8, it is difficult to distinguish from fluctuations by day of the week, making it difficult to verify.

[0095] During the sixth wave, the rate of decline in viral load after the peak was slower than the rate of increase before the peak. Therefore, on Friday, February 11, 2022, I recorded that "the viral load is gradually decreasing" and specifically predicted that the number of new positive cases would be "below 10,000," meaning 9,000-10,000. (However, this was a prediction of the average at that time, not the number of new positive cases on a specific future date. Also, at this point, the aforementioned "12-day" delay was likely to be revised, so I did not predict a specific number of days later.) The basis for this prediction was that the viral load on that day (around February 11) was subjectively just under half of that around the peak of the sixth wave (around January 26). Therefore, I assumed that the viral load and the number of new positive cases were proportional, and calculated it very roughly from the number of new positive cases around the peak (around 20,000). However, since it remains on record, I include it here. The results showed that the seven-day average number of new positive cases on Saturday, February 19th, eight days after the predicted date, was approximately 14,000, which was approximately 40 to 60% higher than predicted.

[0096] On Tuesday, February 15, Thursday, February 17, and Friday, February 18, 2022, the viral load increased compared to the previous week. Initially, I thought it was a random increase and didn't pay much attention to it. However, after seeing that it continued for several days except on Wednesday, February 16, on February 18, I recorded that "the viral load began to increase from February 15." Furthermore, on Sunday, February 20, the viral load increased further, not as high as on January 26, but comparable. The following Monday, February 21, it decreased significantly, so I recorded that "the viral load on February 20 was the second peak of the sixth wave." While these predictions are difficult to verify, there was a notable increase in the number of new positive cases on Wednesday, February 23, eight days after the viral load began to increase (the difference in the number of new positive cases between Tuesday, February 22 and Thursday, February 24, was greater than the variation between days of the week). However, on Monday, February 28th, eight days after the second peak, the number of new positive cases was higher for a Monday than the days before and after, but did not show a clear increase. Furthermore, during the period from February 23rd to 28th, the seven-day average number of new positive cases did not show a clear increase. However, since it went from a decrease to a plateau, it appears that the increase in viral load was reflected to some extent in the statistics.

[0097] On Sunday, February 27, 2022, for reasons unknown, the virus load was unusually low (it was the lowest since the peak of the sixth wave, and there had been a significant decrease since the previous few days), and compared to recent days, it could be said to be zero. However, because this is reflected in the number of new positive cases, it is thought that there will be days in the future when the load will not be zero but will be significantly lower than the previous day. In fact, eight days later, on Monday, March 7, the number of new positive cases decreased significantly. This was a 42% decrease compared to the previous day, Sunday, March 6, and although numbers tend to be lower on Mondays, it can be said that this difference is greater than the fluctuations between days of the week (until the start of 2022, the Monday with the largest decrease compared to the previous day was Monday, February 21, which was a 32% decrease).

[0098] During the remainder of the sixth wave, the amount of virus fluctuated but did not fluctuate significantly. Based on past experience, it is difficult to predict the number of new positive cases during such a period, so only observations were conducted.

[0099] [Example 4] This example explains the observation and prediction for Wave 7. Observations for Wave 7 were conducted using Plan 3, with the observation start time from 16:25 to 16:50 from Thursday, July 7 to Sunday, August 7.

[0100] <Details of observations and forecasts (7th wave)> In the seventh wave, the virus load began to increase continuously around the beginning of July. Then, on Wednesday, July 13th, the virus load was the highest since March during the sixth wave (although still lower than January 26th, 2022), and the difference between the days before and after was also large. The "delay period" in the seventh wave was unknown, but it was assumed to be close to the "eight days" that was the most likely period in the sixth wave. Therefore, on the following day, Thursday, July 14th, we predicted that "Thursday, July 21st would be the provisional peak in the number of new positive cases in the seventh wave." However, on Thursday, July 21st, the virus load increased further (to a tremendous amount comparable to that on January 26th), and the difference between the days before and after was also large. Therefore, the following day, Friday, July 22nd, we updated our provisional prediction, predicting that "Friday, July 29th would be the provisional peak in the number of new positive cases." It seemed like this would be the end of it, but on Monday, July 25th, the virus load increased again significantly from the previous day, reaching almost the same or slightly lower than on the 21st, and then on Tuesday, July 26th, the virus load increased further, exceeding that of the 21st, before dropping significantly the following day, Wednesday, July 27th. Therefore, on the 27th, the provisional estimate was updated, predicting that "Wednesday, August 3rd will be the provisional peak in the number of new positive cases," eight days after the 26th. From then until Sunday, August 7th, no virus load exceeding that of July 26th was detected.

[0101] Let's examine the above predictions. First, the first provisional estimate shows that the peak in new positive cases actually occurred on Friday, July 22nd, with 34,995 cases, one day later than predicted. Next, the second provisional estimate shows that the peak actually occurred on Thursday, July 28th, with 40,406 cases, one day earlier than predicted. The final provisional estimate shows that the peak occurred on Wednesday, August 3rd, with 38,940 cases, the predicted date, but this was about 4% lower than on July 28th. Ultimately, the peak in new positive cases during the seventh wave occurred on July 28th. Furthermore, regarding the increase in virus load on July 25th, the day before the final provisional estimate, the number of new positive cases eight days later, on Tuesday, August 2nd, was 30,842, showing no particular increase.

[0102] [First embodiment] First, we define terms common to all embodiments. In terms of notation, x*y represents multiplication, x / y represents division, exp(x) represents an exponential function with Napier's number as the base, X∩Y represents a product set, and X∪Y represents a union. Furthermore, in values ​​whose last letter is a lowercase i or j (such as VAi, ej, ei'), i or j represents a subscript unless otherwise specified.

[0103] <Area 6> In this disclosure, infection control measures will be taken for a group of people in an area called "Area 6" (Figure 3. A section of a city, a city, a prefecture / state, a country, etc. Generally has a three-dimensional extent and may move or change shape over time. Examples of moving areas include trains and ships).

[0104] Generally, non-residents of Area 6 also enter and exit Area 6. Infection with pathogens does not depend on whether one lives there or not, so it is desirable to include non-residents in the target population when taking infection control measures within Area 6, as long as they have spent time within Area 6.

[0105] <Infection status and infection state> The state of infection of a group of people with a pathogen is called the "infection status" of that group. The infection status of a group of people in a certain area is also called the infection status of that area. It is usually difficult to fully grasp the infection status, and only partial information is available.

[0106] The infection status of an individual is called the "infectious status," which refers to whether or not the individual is infected, and if so, how much of the infectious material (typically the pathogen itself) they are shedding outside their body.

[0107] <Pathogen, pathotype> For example, there are multiple types of pathogens, such as influenza viruses and tuberculosis bacteria. Even within the same influenza virus, there are generally multiple subtypes, such as H1N1 and H5N1. Below, we will refer to the types of pathogens and subtypes within the same type as "pathogen types." For example, the pathogen type "influenza virus" has lower-level pathogen types such as "H1N1" and "H5N1." Furthermore, while "influenza viruses" and "tuberculosis bacteria" are different pathogen types, when considering the higher-level pathogen type "pathogen," both are lower-level pathogen types.

[0108] In the present disclosure, the route by which a pathogen is transmitted from person to person is not particularly limited, and may be airborne, droplet, or contact infection.

[0109] <Propagation material, propagation type> A "transmitter" of a pathogen with pathotype X (called "pathogen X") is a substance that suggests that an infection source of X is nearby. Generally, there are multiple types for a single pathotype, such as the pathogen itself, dead or remnants of the pathogen (a pathogen that has been denatured or a part of the pathogen), all substances released by the pathogen, all substances released by an infected person, and even any of the above substances that have been denatured during dispersion.

[0110] Furthermore, when a certain infectious agent is present in the air, there are times when it is necessary to distinguish between its form even if the substance is the same. For example, it is generally said that the infectiousness of pathogens differs when they are contained in droplets and when they are in the form of droplet nuclei, and it is important to distinguish between these in infection control measures.

[0111] This difference in substance or form of the propagating agent is called the "propagation type," and a propagating agent with propagation type X is called "propagating agent X." Pathogen X itself is also a propagating agent, and is represented by the same propagation type X.

[0112] For example, when pathogen X is floating, it can be represented as transmission types XL and XD to distinguish between infectious and inactivated forms. Furthermore, to distinguish between the form contained in droplets and the form of droplet nuclei, they can be represented as transmission types Xd and Xa, respectively. Furthermore, for example, the form of inactivated droplet nuclei can be represented as transmission type XDa. In this case, XDa is a lower-level transmission type than XD.

[0113] Two transmission types may overlap. In the above example, XL∩Xd is generally not an empty set. Furthermore, a higher-level transmission type does not necessarily have to be completely classified by a lower-level transmission type. For example, given the transmission types Xd and Xa mentioned above, the pathogen X contained in a raindrop may not be classified into either of them. Furthermore, the same transmission substance Z may correspond to two different pathogen types X and Y. For example, this may be a substance released by the inflammatory response when a pathogen enters the human body.

[0114] It is not necessary to define all possible pathogen and transmission types, only those necessary for infection control. For example, for pathogen X, transmission types XL, XD, Xd, and Xa are defined, but for pathogen Y, only YL and YD are defined, and for pathogen Z, no transmission types (other than Z) need to be defined.

[0115] In what follows, we will use the letters A, B, C, ... to represent all defined types, regardless of pathogenicity or transmission type: A = X, B = XL, C = XD, D = Xd, E = Xa, F = XDa, G = Y, H = YL, I = YD, J = Z. We will also use the letters X, Y, and Z to represent any pathogenicity or transmission type.

[0116] <Infection Control System 1> Figure 1 is a functional block diagram of the "infection control system 1." The infection control system 1 is composed of one or more "observation units 2" and an "analysis unit 3."

[0117] <Observation Unit 2> The observation unit 2 is composed of a "collection unit 21" that collects propagation substances and a "measurement unit 22" that measures the amount of the collected propagation substances, and measures the amount of propagation substances in the air.

[0118] The sampling unit 21 and the measurement unit 22 may be located in different physical locations. In some cases, the sampling and measurement functions are difficult or impossible to distinguish. In such cases, the sampling unit 21 and the measurement unit 22 are considered to be integrated (the second observation unit from the top in Figure 1). An example of such an integrated unit is the human body in Examples 1 to 4. The respiratory system of the human body can be considered the sampling unit 21, and its surface area can function as the measurement unit 22 by causing an inflammatory reaction. However, it is more natural to view the human body as an observation unit 2 in which the sampling and measurement functions are integrated. Research has also been reported on training organisms such as dogs and honeybees to detect coronaviruses by odor, and these can also be considered as observation units 2 that output observation results in the form of behavior. Furthermore, if a propagation material absorbs light of a specific frequency, the amount of propagation material can be measured by measuring the degree to which that specific frequency component is absorbed as the light beam passes through a certain distance within Area 6. In this case, no actual sampling is performed, but the propagation material along the path of the light beam is considered to be sampled.

[0119] <Collection section 21> The sampling unit 21 collects airborne pathogens. For example, a device combining a gelatin filter and a fan (the fan blows air onto the filter) can be brought into area 6 as the sampling unit 21 and operated for several hours to allow the filter to adsorb pathogens. The sampling units 21 are assigned numbers starting from 1 (called "sampling unit numbers") i, and the sampling unit 21 with sampling unit number i is referred to as "sampling unit 21 [i]".

[0120] The propagating substances to be collected may be either indoors or outdoors. In addition to collecting samples from the air present in the location, the exhaled breath of a passing person may be directly directed at the collection unit 21. The collection unit 21 itself may be installed either indoors or outdoors. It may be installed in a fixed location, or it may be sampled while moving (for example, attached to the exterior or interior of a car, train, airplane, or ship). When installing the device in a windy location, care must be taken to avoid the amount of propagating substances that can be collected changing due to the wind blowing them away, or the amount of propagating substances that can be collected fluctuating depending on the wind direction, which can easily lead to errors.

[0121] When the amount of propagation material collected by one collection unit 21 is small, or when it is desired to avoid the trouble of measuring each of a large number of collection units 21 (for example, those installed on each floor or block of a building), the propagation material collected by multiple collection units 21 may be measured as a combined total amount. In this case, the multiple units are treated as a single collection unit 21.

[0122] If the propagation substance can be cultured, the collection unit 21 may be provided with a culture function. This makes it possible to increase the amount of propagation substance required for measurement even when the amount of propagation substance in the air is small, and to prevent the propagation substance from decomposing if there is a long time between collection and measurement.

[0123] The sampling unit 21 may perform sampling every day without a break, or may perform sampling only during specific times or days of the week. Even when sampling for 24 hours a day, for example, there are methods such as using only one filter to measure the total amount of propagating substances in a day with the measuring unit 22, or replacing the filter every hour and passing it to the measuring unit 22 as needed to measure the amount of propagating substances every hour.

[0124] There may be a collection unit 21 that collects only a specific propagation type, rather than all of the propagation types under consideration.

[0125] <Measurement unit 22, measurement value> The measurement unit 22 measures the amount of the propagating substance collected by the collection unit 21 and outputs the result. For example, this function extracts DNA / RNA from the pathogens adsorbed on the gelatin filter and subjects them to a qPCR device to measure the amount of the pathogens.

[0126] The location of the measurement unit 22 is not important. The collection unit 21 may be placed within the area 6, and the amount of the infectious material collected there may be measured by a measurement unit 22 located in a remote location. For example, the collection unit 21 using the gelatin filter described above may be operated for several hours within the area 6, and then the filter may be removed and transported to a facility located far away, where the amount of pathogens may be measured using a qPCR device. In this case, for example, multiple collection units 21 may share a single measurement unit 22 in a time-sharing manner. In the present disclosure, whether or not the measurement unit 22 is shared is not important, so hereinafter, the description will be given assuming that the collection unit 21 and the measurement unit 22 correspond one-to-one.

[0127] The amount of measurement result does not have to be the actual number of transmitter particles. For example, it could be the Ct value in qPCR, or a true / false value indicating the presence or absence of a transmitter (if true, it is marked as "present"). In the case of dogs that detect transmitter particles by smell, it could be a three-level value: strong or weak reaction, or no reaction. Alternatively, it could be the area of ​​colonies that appear in a petri dish after culturing the pathogen for a certain period of time.

[0128] For example, in the case of an observation unit 2 that can only tell whether a propagation substance is present or not, if, as a preliminary step, it is installed together with an observation unit 2 that can accurately tell the amount of propagation substance, and a relationship such as "when the measurement result is true, the number of propagation substances is N or more per cubic meter" is obtained, the measurement result obtained as a true / false value can be converted into the amount of propagation substance. Also, if multiple such observation units 2 are placed in various locations and a correspondence between the measurement results at each observation unit 2 (for example, what percentage of the placed observation units 2 had true measurement results) and the amount of propagation substance is obtained as a preliminary step, the amount can be measured more accurately.

[0129] In the following, the measurement results will be the number of propagating substances, or a quantity equivalent to the number, such as the number multiplied by a constant, the number per unit time, or the number per unit volume.

[0130] The measuring unit 22 is assigned the same number i as the collecting unit 21. Measurement is performed for each propagation type, and the output of the measuring unit 22 is called a "measured value" and is represented by VAi, VBi, VCi, . . .

[0131] The measured value VXi is a sequence, and the amount of propagating agent X measured (hereinafter also referred to as detected) at time t (t is an integer satisfying t≧0) is represented as VXi[t]. The unit of time t is arbitrary, but below we will use one day. That is, VXi[0] is the amount of propagating agent X detected by sampling unit 21[i] on the first day of infection control, VXi[1] is the amount on the second day, and so on. However, since the infection situation can change between morning and evening even within the same day, if you want to improve the accuracy of infection control, you should use a finer unit, such as one hour. Note that even if one day is used as the unit, it is not necessary to sample for 24 hours; for example, you can sample for just one hour starting at 1:00 PM every day and use that amount for that day.

[0132] Similarly, below, quantities that change over time are treated as sequences or sequences of values ​​by discretizing time. Unless otherwise specified, if no time is specified in [ ], it is assumed to represent the entire sequence. In other words, in the above example, VXi represents the entire sequence, and VXi[t] represents the t-th value in the sequence.

[0133] <Observation area> A single observation unit 2 often cannot measure the amount of carriers in the entire area 6. Furthermore, the infection situation within area 6 may not be uniform and may vary from location to location. In such cases, a single measurement result alone may not be sufficient for detailed infection control measures. Therefore, observation unit 2 is generally considered to measure carriers in a smaller space called the "observation area" (Figures 3 and 4). However, even in this case, the measurement results of observation unit 2 do not necessarily accurately represent the amount of carriers within the observation area. In such cases, adjustments should be made by narrowing the observation area until it can be considered sufficiently accurate (see "How to Check Approximation Accuracy" below for details) or by strengthening the measurements by observation unit 2 (by extending the observation time, collecting samples from a wider area within the observation area, etc.). For example, if observation unit 2 is installed indoors in a building, including the building's exterior in the observation area may be inappropriate due to the large difference in the amount of carriers indoors and outdoors. In such cases, it is better to limit the observation area to the indoor area. If the observation area becomes too small as a result, this can be addressed by moving the observation unit 2 outdoors, or by adding another unit and installing it both indoors and outdoors. Ideally, the observation area should be contained within Area 6, but it may extend outside or be completely outside. Area 6 itself may also be the observation area.

[0134] <Analysis part 3> The analysis unit 3 is composed of a "storage unit 31," a "determination unit 32," and an "update unit 33." The determination unit 32 estimates the infection status and controls infection through "determination processing." The update unit 33 optimizes control by feeding back the control results of the determination unit 32 through "update processing." The "infection quantification function" that the determination processing and update processing have internally is a central function in this disclosure.

[0135] <Analysis device 5> FIG. 2 is a configuration diagram of a device (hereinafter referred to as the "analysis device 5") that realizes the analysis unit 3. 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 RAM, i.e., random access memory), an "input device 52," and an "output device 53." The processing of the analysis unit 3 (determination processing and update processing) is performed by storing the program in the storage device 54 and executing it on the processing device 51. The output of a device (hereinafter referred to as the "observation device") that realizes the function of the observation unit 2 is input to the input device 52. The observation device and the input device 52 may be connected via a communication line or the like, or the output of the observation device may be manually input by a person to the input device 52. Other environmental data, etc., described below, are input to the input device 52. Furthermore, the output device 53 outputs "countermeasures," etc., described below.

[0136] <Storage section 31> The storage unit 31 stores "area information 43," "determination criteria 41," "update criteria 42," and other data, which will be described later.

[0137] <Environmental Data> Information other than measured values ​​that is useful for infection control is called "environmental data." It is desirable to provide time-series data that includes not only current day information but also past information. It is also desirable to provide hourly changes for data from a single day. Furthermore, it is desirable to provide distributions for each point within Area 6, rather than just the entire Area 6.

[0138] Examples of environmental data include meteorological data (weather within Area 6, temperature, humidity, wind direction, wind speed, hours of sunshine, precipitation, etc.), statistical information on the population staying in Area 6 (age, gender, vaccination and antibody status, travel route and length of stay within Area 6 on that day, etc.). <Area Information 43> The area information 43 is a table that records the location information of the area 6 and the location information of each observation area belonging to the area 6 (if there are multiple observation areas, all of them). Here, location information refers to the location of Area 6 or the observation area, or more precisely, data that represents the boundaries surrounding those areas. A simple example is an address. For example, the location information for Area 6 could be "Tokyo," and the location information for the observation area could be "YY Town, YY City, Kanagawa Prefecture." Since Area 6 and the observation area are generally three-dimensional, it is necessary to specify not only the horizontal extent but also the vertical extent. For example, the vertical extent can be defined as "the height of the tallest building within the specified address." Alternatively, the three-dimensional shape of the area can be defined as a polygon using coordinates (latitude, longitude, altitude). The table consists of multiple entries. The first entry records the location information for Area 6. The next entry records the location information for observation area R1. Similarly, the location information for observation areas R2 and beyond is recorded one by one in each entry.

[0139] <Countermeasures> The output of the determination unit 32 is called a "countermeasure," and this becomes the output of the infection control system 1 itself. For example, the output countermeasures include "do nothing," "strengthen infection status monitoring through random testing," "instruct people to refrain from going out," "lockdown for L days," "early end of lockdown," and "strengthen hospitals' readiness to accept infected people." Here, L is an appropriate number, and may be determined in advance or calculated by the determination unit 32. It may also be possible to output countermeasures for each pathogen type, such as "spray a drug effective against pathogen A."

[0140] In the following, the only countermeasure is "L-day lockdown," and the determination unit 32 outputs L. Here, L is a positive integer or 0, and L=0 specifically means "do nothing."

[0141] <Judgment section 32> The determination unit 32 executes a determination process by referring to the determination criteria 41 and outputs a countermeasure. The determination process uses the measured values ​​VAi, VBi, VCi, ... (where N is the number of collection units 21, and i represents all integers from 1 to N).

[0142] In this disclosure, the time for executing the determination process is exactly midnight, and this time is referred to as the determination time Td. For simplicity, the process is assumed to be completed instantaneously, and lockdown will begin at exactly midnight on time Td+1.

[0143] <Judgment Criteria 41> The rules by which the determination process calculates countermeasures are the "determination criteria 41." In this disclosure, the determination criteria 41 are defined as a table, but more generally, they may be defined as a function that calculates countermeasures from measurement values ​​and environmental data.

[0144] The table consists of one or more entries. Each entry consists of a "condition" and a "countermeasure L" (Figure 5). The judgment process checks the entries in order from the top of the table, and outputs the "countermeasure L" of the first entry whose "condition" is true.

[0145] In addition, if the "condition" of the last entry in the table is always set to true and "countermeasure L" is set to 0 (hereafter, this entry will be called the "judgment sentinel"), then even if the "condition" of all other entries is false, some result will always be output from the judgment process.

[0146] <Update method> In the process (update process) of the update unit 33, a method for rewriting the judgment criteria 41 is called an “update method.” For example, the method may be rewriting the “condition” and “countermeasure L” of an entry, or adding or deleting an entry, but there are no particular restrictions on the method.

[0147] In the following, we will only consider the update method of rewriting countermeasure L. Specifically, the update method is an integer (positive / negative value or 0) "ΔL", which means "rewrite to the value obtained by adding ΔL to L. However, if the addition results in L<0, rewrite to L=0 (i.e., L is always set to L≧0)."

[0148] <Updated part 33> The update unit 33 executes the update process by referring to the update criteria 42, calculates the update method, and then rewrites the judgment criteria 41 accordingly. Note that the update unit 33 can be omitted if the update process is not performed.

[0149] <Update standard 42> The rule by which the update process calculates the update method ΔL is the "update criterion 42." In this disclosure, the update criterion 42 is defined as a table, but more generally, it may be defined as a function that calculates the update method ΔL from the measurement value and environmental data.

[0150] The table consists of one or more entries. Each entry consists of a "condition" and an "update method ΔL" (Figure 11). The update process checks the entries in order from the top of the table, and updates the judgment criterion 41 using the "update method ΔL" of the entry whose "condition" is first true.

[0151] In addition, if the "condition" of the last entry in the table is always true and the "update method ΔL" is set to 0 (hereafter, this entry will be called the "update sentinel"), then even if the "condition" of all other entries becomes false, some result will always be output from the update process.

[0152] The update criteria 42 can be omitted if no update process is performed.

[0153] Now that we have defined common terms, we turn to the details of the first embodiment.

[0154] <Suspended volume qi, released volume ei, remaining volume si> In this embodiment, there is only one sampling unit 21 (i = 1), and only one type of pathogen is considered, a certain pathogen A (e.g., coronavirus). Furthermore, the amount of A produced in nature is negligibly small, and it is desirable to consider A present in the observation area Ri as having been produced in the body of a person staying there. Note that the following discussion also holds true even if a measurement value VBi measuring another pathogen B is used instead of the measurement value VAi, as long as VAi and VBi are proportional to each other.

[0155] Let qi be the number of A particles per cubic meter within Ri at a certain time t (i.e., day t of infection control measures), and ei be the average number of A particles released per person per day who stays in Ri (hereafter referred to as the "emission amount"). Furthermore, let si be the total time spent by the group within Ri (unit: person-days. For example, 10 person-days represents 1,000 people staying for 0.01 days each, or 100 people staying for 0.1 days each. hereafter referred to as "stay amount"). If residences and offices are excluded from Ri, total stay time can also be rephrased as total time spent outside. To measure stay amount si, for example, all people staying in Ri on that day can be given a stopwatch and asked to measure their own stay time, and then the total stay time of everyone can be added up.

[0156] Note that qi, ei, and si are quantities that change over time, so they should ideally be written as qi[t], ei[t], and si[t], but in this embodiment, t is fixed, so "[t]" will be omitted unless it is particularly confusing. Values ​​defined as "time-changing quantities" will be used in the following as well. Furthermore, below we will often deal with quantities that are proportional to qi, ei, and si (including approximately proportional quantities) rather than qi, ei, and si themselves, and will write these with a dash (') as qi', ei', and si', and call them "specific suspended quantity," "specific discharge quantity," and "specific stored quantity," respectively.

[0157] If the sampling unit 21[i] is sampling A present in the air in the observation area Ri evenly, then VAi can be said to be proportional to qi, and VAi will be denoted as qi' hereafter. Furthermore, as a first approximation, the larger qi, the greater the amount of A taken into the body by people staying within Ri, and therefore the worse the infection situation can be said to be. In Examples 1 to 4, we saw that in the case of coronavirus, qi' is linked to the number of new positive cases in the future.

[0158] Furthermore, A present within Ri exists in various states, such as droplets, aerosols, and droplet nuclei. To a first approximation, if qi doubles, the amount of A in droplet form (including aerosols and droplet nuclei) will also double. Therefore, if qi doubles, the amount of A in droplet form taken up by people staying within Ri will also double. This means that even if A is only transmitted by droplets (not aerosols or airborne infections), it is not necessary to limit the collection of A in droplet form when measuring qi. In other words, if qi is the total amount of A collected and measured without distinction between droplets, aerosols, and droplet nuclei, then to a first approximation, qi is proportional to the amount of A in droplet form taken up by people staying within Ri.

[0159] However, as explained below, strictly speaking, a higher qi does not necessarily mean a worse infection status. Therefore, various factors must be taken into consideration when implementing infection control measures with a higher degree of accuracy than a first-order approximation. First, even with the same qi value, the assessment of infection status may differ depending on the number of people with antibodies. Second, depending on the pathogen, ingesting it may have both positive and negative effects (e.g., activating the immune system and making people less susceptible to other diseases). Third, the collected A is released by an infected person through various actions such as sneezing, talking, and breathing, drifts through the air, and is eventually captured by the collection unit 21. However, even if it is released as droplets, some may become droplet nuclei and others remain as droplets before being collected, so the state of the droplets is not uniform. Furthermore, the ratio of these may change depending on whether the air is dry on the day. Fourth, the ratio of A released as droplets, aerosols, and droplet nuclei may change depending on the infected person's condition (e.g., whether it is in the early stages of infection or later stages) (e.g., 8:2:1 in the early stages of infection, but changing to 2:1:1 later). And other circumstances.

[0160] Next, if we consider the perspective of the person releasing the pathogen, as opposed to the person taking it in, then as a first approximation, the larger the ei, the more pathogens there are in the body and the worse the infection situation.

[0161] However, as explained below, strictly speaking, a larger ei does not necessarily mean a worse infection situation. Therefore, various factors must be taken into consideration when implementing infection control measures with a higher degree of accuracy than a first-order approximation. First, even with the same ei value, the judgment of whether the infection situation is good or bad may differ depending on the breakdown, such as when there are many mildly infected people (or those who emit small amounts of pathogens) and a small number of severely infected people (or those who emit large amounts of pathogens). In particular, among the former mildly infected people, some may have symptoms so mild that they are hardly even considered an infection, and may recover within a few hours without any noticeable symptoms. (In my own experience, if only a small amount of coronavirus was inhaled, the discomfort in the throat subsides within a few tens of seconds, but if a large amount was inhaled, recovery can take several hours. While I do not experience any particular symptoms until recovery, I believe I myself am also shedding trace amounts of virus. Others are likely in the same situation, just unaware of it.) In such cases, the proportion of mildly and severely infected people must be calculated or estimated separately to assess the infection situation. Second, depending on the type of pathogen, the amount of pathogens released by a person may be at its highest before symptoms appear, and the degree of illness does not necessarily correspond to how unwell they feel. Third, the relationship between the amount of pathogens released by an infected person and the amount of pathogens in the body may be more complex than linear (e.g., diseases other than those of the respiratory system). Fourth, even with the same ei value, if the properties of the pathogen change due to mutation, the assessment of the infection status may change. And other factors.

[0162] Now consider the relationship between qi and ei. The total amount of A released within Ri on that day can be calculated as ei*si, and as a first approximation, if this total amount doubles, qi will also double, so qi and ei*si, or equivalently, qi / si and ei, are proportional. Strictly speaking, however, qi may also include A that has entered from outside the observation area Ri. However, if the infection situation or number of people near the boundary of Ri has not changed significantly, it is thought that the amount of A entering from outside through a certain part of the boundary will be balanced and canceled out by the amount exiting through the same part. Therefore, if the boundary surface is taken in this way when defining the observation area Ri, then, as a first approximation, it is acceptable for the amount of A entering from outside to be included in qi.

[0163] Instead of calculating si, it is also possible to calculate a value si' that is proportional to si. For example, if si' is the total time spent in a part of Ri (for example, a square area with a side length of 100 meters at the center of Ri) rather than the entire Ri, then, to a first approximation, if si doubles, si' also doubles, so si and si' are proportional. Furthermore, if ei' = qi' / si' (hereinafter referred to as the "relationship EQS") is defined, ei' will be proportional to ei.

[0164] In this way, to obtain quantities such as qi, ei, and si, it is likely that first measuring qi', ei', and si' with a dash is necessary. Furthermore, it is likely that infection control measures will be carried out using only the quantities with a dash. For example, si' may be measured but si may not be obtained.

[0165] <How to check approximation accuracy: qi and qi', ei and ei', si and si'> The proportional relationships between qi and qi', ei and ei', and si and si' described above are first-order approximations, but the accuracy of the approximations can be confirmed by performing the following procedure over multiple days (the number of days is determined according to the required accuracy). First, for si', we considered an area with sides of 100 meters, but for larger areas (the closer to Ri the better, but the number is determined according to the required accuracy), and examine the total time spent there to see if it is proportional to si'. For qi', we travel evenly within Ri over a longer period of time (the time and distance are determined according to the required accuracy), collect A, and measure the amount to see if it is proportional to qi'. Finally, for ei', we randomly select multiple people (the number is determined according to the required accuracy) from those who stayed in Ri on that day, and actually measure the average amount of A they emit to see if it is proportional to ei'. If a proportional relationship is established, we can indirectly infer that the accuracy of qi' and si', which were used to calculate ei', is also sufficient. If the amount of A emitted as droplets indicates the infection status, for example, then qi' can be compared with the average amount emitted only as droplets (excluding aerosols, etc.). If the above procedure does not provide sufficient accuracy, measures can be taken to address this by changing the measurement method for qi', ei', and si', changing the installation location of the sampling unit 21[i], changing the range of Ri, etc.

[0166] Furthermore, the method of randomly sampling and testing health as described above can also confirm whether the relationship discussed earlier—that the higher the qi or ei (or qi' or ei') the worse the infection situation—holds true. In other words, the relationship between the amount of qi or ei and the health of randomly selected people is examined (not only their health on the day, but also, if necessary, their health on subsequent days as a result of ingesting pathogens on that day). If the higher the qi or ei, the worse the infection situation, then the magnitude of qi or ei can be regarded as a direct indicator of the infection situation. Even if this is not the case—if the relationship between qi or ei and the infection situation is nonlinear or more complex—if, for example, there is a threshold and it can be said that "if qi or ei exceeds that threshold, the infection situation is definitely bad"—then infection control measures can be implemented when qi or ei exceeds that threshold.

[0167] In the discussion so far, we have judged the infection status based on either the quantity of qi or ei. However, what should we do if, 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 circumstances of the infection control measure. For example, if the infection control measure is to encourage people with weakened immune systems to refrain from going out, we may need to look at the size of qi rather than ei. This is because even if ei is small, as qi increases, the number of places at risk of infection increases (to a first approximation), and the probability of infection increases by going out. On the other hand, if the spread of infection is important, for example, a lockdown may be necessary even if qi is small but ei is large (for example, the infection is spreading, but the amount of outings on that day happened to be extremely low). Furthermore, when qi is linked to the number of new infections, as in Examples 1 to 4, the response will differ depending on the size of ei (the degree of infection spread) even if the qi value is the same. This is because, for example, even if the number of new infections on a given day is the same (100), the severity will be different if there are already 10,000 total infected people compared to if there are only 10. In other words, it may be necessary to take infection control measures into account both qi and ei. In addition, when taking infection control measures with a higher accuracy than a first-order approximation, various circumstances should be taken into account in addition to qi and ei.

[0168] <Examples of infection control measures> If the relationship between the specific floating volume qi', the specific release volume ei', and the infection status can be understood using the methods described above, infection control measures can be implemented using qi' and ei'. However, as mentioned above, this relationship can be simple, such as a proportional relationship, or more complex. Furthermore, there are a wide variety of infection control methods, but the purpose of this disclosure is not to suggest which method is best. Therefore, below we present a general-purpose framework and, as an example, explain a simple method in which the infection status is considered to have worsened when qi' and ei' exceed certain thresholds (hereinafter referred to as the "floating threshold" Qi' and the "release threshold" Ei', respectively), and a lockdown is implemented as an infection control measure. However, it is not necessary to use both qi' and ei'; infection control measures can be implemented using only qi' or ei'. Naturally, in this case, unnecessary values ​​do not need to be calculated.

[0169] It is desirable to update the thresholds Qi' and Ei' as needed to maintain accuracy, even after they have been determined. This is because the relationship between ei' and qi' and the infection status may change over time as the properties of the pathogen, the population's resistance to the pathogen, and the climate change. The values ​​of Qi' and Ei' may also be changed depending on the environmental conditions of the day, such as the temperature and humidity. For example, if the temperature and humidity are conducive to the spread of infection, a lower value may be used for Qi'.

[0170] Furthermore, the scope of infection control does not necessarily have to be Ri. It could be only a part of Ri, an area that includes Ri such as the entirety of Area 6, or an area that does not overlap with Ri but is thought to have a similar infection situation to Ri. For example, in the case of a lockdown, various measures could be considered, such as "banning people who stayed in Ri on that day," "banning everyone who may have stayed in Ri on that day," "banning entry into Ri," "banning entry to areas with a similar infection situation to Ri," or "banning entry to the entirety of Area 6."

[0171] <First Determination Process> The judgment process in this embodiment (referred to as the "first judgment process", but simply referred to as the judgment process here) will be explained using the flowchart in Figure 6. This judgment process calls the first infection quantification function in Figure 7 as a subroutine. Note that the symbols used below are the measurement value VAi, specific airborne volume qi', specific release volume ei', specific residual volume si', airborne threshold Qi', and release threshold Ei'. For accuracy, brackets ([ ]) will not be omitted.

[0172] The determination process starts with the determination time: Td and the collection unit number: i given as arguments (S101). Next, the first infection quantification function is called (S102). There, for the time t satisfying 0≦t≦Td, calculate qi'[t]=VAi[t] and ei'[t]=qi'[t] / si'[t] (S1021). Note that although it is repetitive, values not referred to in the determination criterion 41 may be omitted from the calculation here (in subsequent embodiments as well, values not referred to may be omitted from the calculation in the infection quantification function). Next, using j as an index, repeat the following from the first entry of the determination criterion 41 (j = 1) to the last entry (j = n, where n is the number of entries in the determination criterion 41) (S103). First, check whether the condition of entry j (condition j) is true. At this time, appropriately refer to Td, i, qi', ei', si', Qi', Ei', and environmental data from the conditions (S104). If condition j is true, output the corresponding measure L of that entry (S106), and then, if an update process described in a later embodiment is to be performed, save (Td, i, j, L) for that time (S107). On the other hand, when condition j is false, return to the beginning of the loop (S105).

[0173] For example, assume that the determination criterion 41 is composed of the following three entries: (1) Condition = "qi'[Td]≧Qi'*1.5", corresponding measure L = 20. (2) Condition = "qi'[Td]≧Qi'", corresponding measure L = 10. (3) Sentinel determination.

[0174] At this time, if qi'[Td]<Qi' in the determination process, since the infection situation is good, output "L = 0". Also, if Qi'≦qi'[Td]<Qi'*1.5, output "L = 10" assuming the infection situation is bad. Furthermore, if qi'[Td]≧Qi'*1.5, output "L = 20" assuming the infection situation is very bad.

[0175] In the example of the above determination criterion 41, only qi' and Qi' are referred to in the "conditions" of each entry. However, as described above, any of the values of qi', ei', Qi', and Ei' can be freely used. Also, in the "countermeasures" section, for example, the value of L may be obtained using the values of qi', ei', Qi', and Ei' as in "L = (ei'[Td] / Ei') * 10" (note that this formula is just an example for explanation). By doing so, even for the same value of qi', the lockdown period can be changed depending on the magnitude of ei'. That is, even if the qi' value on the same day is the same, when ei' is small and si' is large, and when ei' is large and si' is small, it is considered that the infection has spread more in the latter case, and the lockdown can be implemented for a longer period.

[0176] When the output of the determination process is L > 0, enter lockdown, but usually, it is not necessary to execute the determination process again until lockdown ends after L days. However, the determination process can also be used to determine an early end to the lockdown. That is, even during lockdown, in order to appropriately monitor qi' and ei', the determination process may be executed again with time t (where Td < t < Td + L) as the determination time (however, during lockdown, since the amount of people going out is small, the reliability of the values of qi' and ei' may be low. In that case, on the day of time t, it may be possible to allow only a part of the group to go out, temporarily increase the amount of going out, and then measure qi' and ei'). As an example of t, the midpoint of the lockdown, that is, t = L / 2 (round up the decimal part of L / 2) can be cited. At this time, for example, if the output countermeasure L is L = 0, it is considered that the infection situation has improved and an early end is made, and if L > 0, the lockdown is continued.

[0177] <Summary of this embodiment> In this embodiment, infection countermeasures were taken using the specific floating amount qi' and the specific release amount ei'. It is simplest to take infection countermeasures assuming that the infection situation in the observation region Ri is in a proportional relationship with qi' or ei', but it may also be in a more complex relationship.

[0178] Furthermore, in Examples 1 to 4, we saw that qi' measured in an observation area Ri of several kilometers in District A is linked to the future number of new positive cases in the large area 6 of Tokyo. In other words, currently in Japan, the increase / decrease and peak of the infection situation are determined by looking at statistics on the number of new positive cases, but by measuring qi', it is possible to predict the increase / decrease and peak of the infection situation in the large area 6 at a practical cost and to implement infection control at an early stage. Similarly, by calculating ei', it is possible to estimate the infection situation in the large area 6 within the same day at a practical cost and implement infection control.

[0179] Although people may think of a method to detect viruses in the air and determine whether there are infected people nearby, they usually don't think to put it into practice. This is because, based on experience, such a method can only determine the infection status of a nearby area (for example, an area of ​​several dozen meters square), and it would be extremely costly to determine the infection status of an entire city such as Tokyo. However, this is not the case. The main claim of this disclosure is that it can actually be done at very little cost.

[0180] Furthermore, as described in Example 1, even if sufficient statistical data on the number of new positive cases is not available in the early stages of the spread of infection, it is expected that the infection situation can be estimated early and at low cost and infection control can be achieved simply by measuring the amount of virus in the air.

[0181] [Second embodiment] In this embodiment, there is also one sampling unit 21 (only i=1), and only one type of pathogen A is considered as the propagating agent. It is also preferable to consider that A does not exist in an environment where there are no infected people. Note that the following discussion also holds true even if a measurement value VBi measuring another propagating agent B is used instead of the measurement value VAi, as long as VAi and VBi are proportional. In this embodiment, infection control measures at a certain time t are also considered.

[0182] <Absorption amount ai> In this embodiment, we will describe an infection control measure that uses a time-varying quantity called "absorption amount." At a certain time t, the total number of A absorbed by all people staying in the observation area Ri during their stay is defined as absorption amount ai. Furthermore, a quantity proportional to ai (including approximately proportional) is denoted with a dash, such as ai', and is called the "specific absorption amount."

[0183] To a first approximation, the larger ai is, the worse or worse the infection situation of the population (all people who stayed at Ri) is. This is because that amount of pathogens has had the opportunity to enter the population's bodies and multiply. Some people will become newly infected as a result, and even for those who are already infected, it is better to have as few new pathogens as possible.

[0184] However, when taking infection control measures with a higher accuracy than first-order approximation, various circumstances should be taken into consideration, as in 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 who have antibodies.

[0185] The relationship between a population's infection status and ai can be concretely illustrated as follows. Let's say that when a person ingests x units of pathogen A, the probability of infection is expressed as a function f(x). (For example, one study has modeled this as f(x) = 1 - exp(-x / N), where N is a parameter: https: / / www.medrxiv.org / content / 10.1101 / 2020.10.21.20216895v1.full) When a graph of the function f(x) is drawn, if there is a tangent to f(x) at x = 0 and its slope is k, then to a first approximation, a proportional relationship of f(x) = k * x (hereafter referred to as the "relationship FKX") holds.

[0186] And si is the amount of the population, but its breakdown is, for example,<n1,d1> ,<n2,d2> ,<n3,d3> Here,<n1,d1> represents n1 people who stayed in Ri for d1 days. The amount of A absorbed per person by these n1 people is proportional to d1*qi, so if f(x) and x are proportional, then the number of people infected will be proportional to n1*d1*qi. The same is true for n2 and n3; the total number of people infected within Ri on that day is proportional to n1*d1*qi + n2*d2*qi + n3*d3*qi. Rearranging this equation, noting that si = n1*d1 + n2*d2 + n3*d3, results in si*qi. However, this is only a first-order approximation, and since some people are already infected within the population represented by n1+n2+n3, it is more accurate to exclude those people. The above discussion also holds for other breakdowns of si. Therefore, if the number of new infections within Ri on that day is represented as pi, then, to a first-order approximation, pi is proportional to si*qi.

[0187] When a person is infected with a pathogen, there is generally an incubation period. In other words, it is often only after some time has passed that the infection is discovered. In contrast, if pi can be estimated, it is possible to control infection in the population at an earlier stage.

[0188] Above we calculated the number of new infections, but more generally, we can calculate "the number of people whose probability of becoming that way, f(x), is proportional to x." For example, if the probability of sneezing three or more times after ingesting A while staying in Ri is f(x), and f(x) is proportional to x, then the number of people in Ri who will sneeze three or more times due to A on that day will also be proportional to si*qi. In general, when a quantity used in infection control is proportional to si*qi (including approximately proportional), this will be called an "influence amount."

[0189] In this embodiment, infection control is performed using a quantity ai' proportional to ai. First, as in the first embodiment, the measured value VAi is used as the specific airborne quantity qi' proportional to qi. Also, as with the specific residual quantity si' in the first embodiment, a quantity si'' with two dashes is used, which is proportional to si (hereinafter also referred to as the specific residual quantity). As a first approximation, if qi doubles, ai also doubles, and if si doubles, ai also doubles, so it can be said that ai is proportional to both qi and si. This proportional relationship can be expressed by the relational expression with dashes, ai' = qi' * si'' (hereinafter referred to as the "relational expression AQS"), which shows that the absorption amount and the impact amount are approximately proportional to each other. The reason for using two specific residual quantities si' and si'' here is that the two do not have to be the same value and may have different precision. That is, the specific quantity used to calculate ei' from qi' using the relational expression EQS (ei' = qi' / si') may be different from the specific quantity used to calculate ai' from qi' using the relational expression AQS. Of course, they may also be the same value with si'' = si'.

[0190] Furthermore, in the first embodiment, ei' is calculated from qi' and si' using the relational expression EQS, but in this embodiment, ei' may be calculated by actual measurement (such as the random sampling method described in the first embodiment), and ai' may be calculated from ei', si', and si'' using the expression ai'=ei'*si'*si'' (hereinafter referred to as "the relational expression AESS"). In this case, if the result of multiplying si' and si'' (si'*si'') can be measured directly without calculating the two values ​​si' and si'' separately, then ei' may be multiplied by this result.

[0191] Below, we explain how to estimate the number of new infections pi as an influence quantity and take infection control measures. Note that this method can also be applied to other influence quantities.

[0192] If we measure or estimate the specific stay amount si'' by using the total stay time limited to people who are not infected at time t (specific stay amount si' minus the total stay time of people who are already infected on that day), the estimation of the number of new infections pi in the previous discussion will become more accurate. This is the same discussion as when we tried to find the relationship between ai and the infection situation with an accuracy of first approximation or better.

[0193] In Examples 1 to 4, we saw that qi' = ei' * si' is linked to pi. If the proportional relationship between pi and si * qi holds, then in reality ai' = qi' * si'' = ei' * si' * si'' is proportional to pi. Comparing the definitions of qi' and ai', qi' can be said to be an approximation of ai' when si'' = 1. Alternatively, qi' can be said to be an approximation of ai' obtained by replacing si' and si'' with the square root of si' (√si'). Therefore, if the amount of stay (si or si'') is constant, qi' is proportional to ai', and the accuracy of the approximation of qi' to pi can be said to be high. If this is not the case, for example, if the amount of stay increases by 1.5 times, ai' will increase by 2.25 times, while qi' will only increase by 1.5 times, resulting in an error in the approximation of qi' to pi.

[0194] <How to check approximation accuracy: ai and ai', pi and ai> The proportional relationships between ai and ai', and between pi and ai defined so far are first-order approximations, but we will explain how to check whether the accuracy of the approximation is sufficient, using the relationship between ai' and pi as an example. If the accuracy of the proportional relationship between ai' and pi is sufficient, then it can be indirectly estimated that the accuracy of the proportional relationships between ai' and ai, and between pi and ai, or if ai' is calculated using the relational formula AQS, the accuracy of the underlying qi',si'' is also sufficient.

[0195] First, on that day (called the stay day), multiple people (call it person X) are randomly selected and quarantined from among those who enter the observation area Ri from outside or those who are staying in Ri at exactly midnight on the stay day. The timing of quarantine is the moment of entry in the former case (the moment of first entry for those who plan to enter and exit Ri multiple times), and midnight for the latter. Additionally, multiple people (call it person Y) are randomly selected and quarantined from among those who leave Ri on the stay day or those who are staying in Ri at exactly midnight on the stay day (excluding those who leave Ri at least once and re-enter). The timing of quarantine is the moment of exit from Ri in the former case (the moment of first exit for those who plan to enter and exit Ri multiple times), and midnight for the latter. After that, these X+Y people are quarantined for a sufficient period of time and repeatedly tested (e.g., by PCR to measure the amount of pathogens in their bodies) to determine whether they are infected. The total number of people staying in Ri on the stay day is also set to N. The number of people X and Y is determined depending on the required accuracy.

[0196] As a result, let's say that X' people out of X people and Y' people out of Y people are confirmed to be infected. These X'+Y' infections are either caused by staying at Ri on the day of stay (called same-day origin), or there is a cause before that (called prior origin). Below, let's assume x = X' / X and y = Y' / Y. First, since Y is selected randomly, it can be estimated that N*y people out of N people will be infected (the total number of people from the same day and prior origin). Next, since X is also selected randomly, it can be estimated that N*x people out of N people will be infected from prior origin. If the difference between these is taken as pi = N*(yx), then pi can be estimated to be the number of people infected from same-day origin.

[0197] By performing the above measurements over multiple days (the number of days is determined depending on the required accuracy), it is possible to verify whether the proportionality between ai' and pi is sufficiently accurate. If the accuracy is not sufficient, measures can be taken such as changing the measurement method for qi', ei', si', and si'', changing the installation location of the sampling unit 21[i], or changing the range of Ri. Furthermore, these measurements can provide information on methods for estimating pi from ai' with first-order or higher approximations (i.e., second-order, third-order, etc.), which can be used to improve infection control measures (specifically, improve the judgment criteria 41).

[0198] <Examples of infection control measures> If the relationship between the specific absorption amount ai' and the infection status can be determined using the method described above, infection control measures can be implemented using ai'. However, for the same reasons as in the first embodiment, a general-purpose framework will be shown below, and as an example, a simple method will be described in which the infection status is deemed to have worsened when ai' exceeds a certain threshold (hereinafter referred to as the "absorption threshold" Ai'), and a lockdown is implemented as infection control. Naturally, infection control measures can also be implemented using qi' and ei' together.

[0199] As in the first embodiment, it is desirable to update the threshold Ai' as needed to maintain accuracy even after it has been determined. Alternatively, the value of Ai' may be changed depending on the environmental conditions of the day. Furthermore, the target range of infection control does not necessarily have to be Ri.

[0200] <Second Determination Process> The judgment process in this embodiment (referred to as the "second judgment process", but simply referred to as the judgment process here) will be explained using Figures 6 and 8. This judgment process is the same as the first judgment process in Figure 6, except that in step S102 the second infection quantification function in Figure 8 is called as a subroutine, with the remainder of the process being the same. Note that the symbols used below are measurement value VAi, specific floating amount qi', specific release amount ei', specific residual amount si', si'', specific absorption amount ai', release threshold Ei', floating threshold Qi', and absorption threshold Ai'. For accuracy, brackets [ ] are not omitted.

[0201] In the second infection quantification function, a new ai'[t] = qi'[t] * si''[t] is calculated for times t that satisfy 0≦t≦Td (S1022). All other processing is the same as in the first infection quantification function. Note that with this change, the "conditions" of the judgment criterion 41 may refer to si'', ai', and Ai' as appropriate in addition to Td, i, qi', ei', si', Qi', Ei', and environmental data. Note that Qi' and Ei' are assumed to be defined separately using the method described in the first embodiment.

[0202] Furthermore, in the first embodiment, the judgment criteria 41 consisting of three entries is exemplified, but if qi' therein is replaced with ai' and Qi' with Ai', it becomes an example of the judgment criteria 41 in this embodiment.

[0203] <Summary of this embodiment> In this embodiment, infection control measures were taken using the specific absorption rate ai'. The simplest way to take infection control measures is to assume that the infection status in the observation area Ri is proportional to ai', but a more complex relationship may also be used.

[0204] Furthermore, the relational expression AQS uses the specific emission amount qi' when calculating ai'. Therefore, as in the discussion in the summary of the first embodiment, qi' measured in a narrow observation area Ri can be used to implement infection control measures over a wide area (such as the entire area 6, as will be described in detail in the summary of the third embodiment) at a practical cost.

[0205] And to reiterate, when ai' is proportional to the number of new infections pi, it is possible to predict the number of new positive cases in the future using ai'. In addition to predictions, it is also possible to control infections on the same day, for example by estimating the number of new infections in the morning of a certain day, and if that number is high, by restricting people's outings in the afternoon, thereby reducing the number of new infections. Because the amount of pathogens in the air can change significantly from one day to the next, this is expected to be more effective than imposing restrictions on outings the following day. Furthermore, being able to control infections on the same day has the advantage that it is clear what specific actions should be taken now, rather than implementing infection control measures after the number of new positive cases becomes known at a later date, and people can cooperate more proactively in infection control efforts.

[0206] The advantages of this embodiment will be further explained using Figure 16. (A) represents all current infected people, and (B) represents new infected people. Naturally, (B) arises as a result of pathogen transmission from (A). Therefore, concepts such as the effective reproduction number can be used to estimate the number of (B) using the number of (A). In contrast, in this embodiment, (C) is considered to be the pathogen released by (A) in its entirety. The causal relationship is not that (B) arises directly from (A) (dotted arrow in the figure), but rather that (A) releases (C), which then causes (B) to arise (solid arrow in the figure). Therefore, the method of this embodiment, which estimates the number of (B) from the amount of (C), is closer in causality to the method of calculating the number of (B) from the number of (A), and therefore allows for more accurate estimation. Or, at least, there is the potential for achieving such accuracy by improving the approximation from first order to second order, third order, and so on.

[0207] [Third embodiment] In this embodiment, the first embodiment is expanded to consider a method of increasing the accuracy of infection control by installing multiple (N units, including a single unit as a special case, so N≧1) sampling units 21. Note that the observation areas Ri and Rj for the two sampling units 21[i] and 21[j] may overlap each other. Also, only one type of pathogen type A is considered as the transmission agent. It is desirable to consider that A does not exist in an environment where there are no infected people.

[0208] The infection situations within Ri and Rj may be similar or different. When there is little movement of people or objects that transmit infection between Ri and Rj, it may be better to consider the infection situations in the two areas as independent and implement independent infection control measures. However, this is equivalent to dividing the infection control area 6, so from here on, we will assume that the infection situations in the N observation areas are similar to each other. In that case, the emission amounts ei and ej will take similar values. As a result, if the number of people in Ri and Rj is similar, the floating amounts qi and qj will also take similar values.

[0209] Furthermore, in the COVID-19 pandemic that began in Japan in early 2020, the fluctuations in the number of new positive cases in Tokyo and Kanagawa prefectures, for example, have been very similar. Therefore, the infection situations in these central, busy areas (for example, Tokyo Station and Yokohama Station) are thought to be similar to each other.

[0210] <Average specific emission amount e0''> In the first embodiment, the specific emission amount ei' proportional to the emission amount ei was calculated when there was one collection unit 21. In this embodiment, there are multiple collection units 21, so N (1≦i≦N) values ​​are calculated as ei', and these are combined to perform infection control for the entire area 6. This allows for more accurate infection control using information from a wider range than when there is one collection unit 21.

[0211] However, in general, the proportionality coefficient between ei and ei' differs depending on the collection unit 21, so simply finding the average of N ei' is meaningless. Therefore, first, ei' is converted into a mutually comparable amount ei'' (hereinafter also referred to as the "specific release amount").

[0212] A representative example of such a quantity is the emission amount ei itself. Therefore, for example, let ei'' = ei, calculate the average value e0'' (hereinafter referred to as the "average specific emission amount") as e0'' = (e1'' + e2'' + ... + eN'') / N, and if e0'' exceeds a certain threshold E0'' (hereinafter referred to as the "average emission threshold"), implement infection control for the entire area 6. Alternatively, a weight wi is assigned to each ei'' and the weighted average is calculated as e0'' = (w1 * e1'' + w2 * e2'' + ... + wN * eN'') / (w1 + w2 + ... + wN). Here, weighting can be done by assigning a small weight to ei'' that is less important, such as ei'' calculated from a less accurate measurement value VAi (e.g., when the performance of the sampling unit 21 or measurement unit 22 is poor) or ei'' calculated from a sampling unit 21 installed in a low-traffic area. It is also possible for some sampling units 21 to have a weight of 0. For example, if w2 = 0, e0'' will be calculated without using the measurement results from the collection unit 21[i] (i = 2). Conversely, if wi other than i = 2 is set to 0, e0'' will be calculated using only the measurement results from the collection unit 21[i] (i = 2). It should be noted that the normal average is a special case of the weighted average (all weights are 1).

[0213] In the above example, ei was used as ei", but it is generally time-consuming to calculate ei. Another example of ei" is to define ei" as a relative amount with the value of ei' at a certain time (for example, t = 0) as a reference value of 1. That is, ei"[t] = ei'[t] / ei'[0] (note that here the square brackets are not omitted). In this case, the release threshold Ei' defined in the first embodiment can also be converted as Ei" = Ei' / ei'[0], assuming the corresponding threshold is Ei". Furthermore, E0" is a value obtained by weighting the average of Ei".

[0214] <Examples of infection control measures> An example of infection control measures can be achieved by replacing the specific release amount ei' with the average specific release amount e0'' and the release threshold value Ei' with the average release threshold value E0'' in the example of the first embodiment. Details will be described below.

[0215] <Third Determination Process> The judgment process in this embodiment (referred to as the "third judgment process", but simply referred to as the judgment process here) will be explained using the flowcharts in Figures 9 and 10. These are similar to Figures 6 and 7 in the first embodiment, so the differences from these will be mainly explained. Note that the symbols used below are measurement value VAi, specific release amounts ei', ei'', specific residual amount si', weight wi, average specific release amount e0'', average release threshold E0'', and release threshold Ei'. Also, for accuracy, brackets [ ] will not be omitted.

[0216] The difference from FIG. 6 is that the parts corresponding to S101, S102, and S107 have been changed to S201, S202, and S207, respectively. First, the judgment process of this embodiment combines the measurement results of N collection units 21 rather than a specific collection unit 21[i], so in S201, unlike S101, the collection unit number: i is not included in the argument. For the same reason, in S207, unlike S107, (Td, 0, j, L) is saved as data for the update process. That is, in order to remember that this judgment is a combination of N collection units 21, 0 (invalid collection unit number) is saved as the second value of the data.

[0217] Next, in S202, the third infection quantification function is called. In step S2021, qi'[t], ei'[t] (0≦t≦Td, 1≦i≦N) are calculated for all N collection parts 21, and then ei''[t] is calculated as described above, and the weighted average of these is calculated as e0''[t]. (0≦t≦Td). In this example, ei''[t] is calculated as ei''[t]=ei'[t] / ei'[0], but other methods are also acceptable.

[0218] 6, in the other steps S203, S204, S205, and S206, the entries of the judgment criterion 41 are checked in order from the first (j=1), and when an entry j for which the condition is true is found, the countermeasure L written there is output. Then, in S204, Td, e0'', E0'', environmental data, etc. may be referenced as appropriate from the "condition" of the judgment criterion 41 (other references, such as ei', Ei' for 1≦i≦N, are not prohibited, but are not used in this example).

[0219] Furthermore, in the first embodiment, the judgment criterion 41 consisting of three entries is exemplified, but if qi' there is replaced with e0'' and Qi' with E0'', it becomes an example of the judgment criterion 41 in this embodiment.

[0220] <Summary of this embodiment> As described above, by installing multiple sampling units 21 in area 6 and combining their measurement results, infection control measures can be implemented based on more reliable information than if only one unit were installed.

[0221] In the above discussion, the specific emission amounts ei' were combined to determine the average specific emission amount e0'', but if the number of people in each observation area Ri (1≦i≦N) is similar, the specific airborne amounts qi' can be similarly converted into amounts that can be compared with each other, and the average (hereinafter referred to as the "average specific airborne amount") can be determined as q0'', which can be referenced from the judgment criterion 41. This makes it possible to combine a wider range of information, even when using specific airborne amounts, and improve the accuracy of infection control measures. Furthermore, since the average specific airborne amount q0'' can be determined only from the measured values ​​VAi without measuring the resident amount si', infection control measures can be implemented with less effort than when using the average specific emission amount e0''.

[0222] Similarly, for the specific absorption amount ai', if the number of people in each observation area Ri is similar, ai' can be converted into a mutually comparable amount, and the average (hereinafter referred to as the "average specific absorption amount") can be calculated as a0'' and referenced from the judgment criterion 41. In this way, even when using the specific absorption amount, a wider range of information can be integrated to improve the accuracy of infection control measures.

[0223] The following application is also possible. Taking the average specific emission rate e0″ as an example, the specific resident doses of people within any area R (which does not necessarily have a sampling unit 21 installed) with a similar infection situation to each observation area Ri (1≦i≦N) can be calculated as s′ and s″, and the specific absorption rate a′ within R can be estimated using the relation a′ = e0″*s′*s″. That is, even in areas where a sampling unit 21 is not installed, a′ can be estimated using e0″ by measuring s′ and s″. As briefly mentioned in the summary of the second embodiment, this means that if R is the entire area 6, by calculating ei′ and e0″ from qi′ within a narrow observation area Ri (1≦i≦N), s′ and s″ within area 6 can be measured to estimate a′, and infection control measures can be implemented for the entire area 6. In addition, the accuracy of a′ can be confirmed by applying the method described in the <Method for Confirming Approximation Accuracy> of the second embodiment to area 6.

[0224] [Fourth embodiment] In this embodiment, the first embodiment is extended (the second and third embodiments can also be extended in a similar manner) to describe a method for optimizing the processing of the determination unit 32. In the first determination process in the first embodiment, since there are a wide variety of infection control methods, a general-purpose framework is shown and one example is explained. In this embodiment, too, it is not the subject matter to show which of the wide variety of optimization methods is best. Therefore, a general-purpose framework is shown and a simple optimization method is explained as an example.

[0225] As in the first embodiment, there is only one collection unit 21 (i=1), and the propagating agent is only one type, pathogen type A. It is preferable to consider that A does not exist in an environment where there are no infected people.

[0226] In the embodiments up to now, the judgment unit 32 has output values ​​such as L=20 or L=0 as countermeasures. Here, the effectiveness of the countermeasures is verified at a later date, and optimization is performed by increasing the value of L if the effect is smaller than expected, or decreasing the value of L if the effect is larger. In this embodiment, the effectiveness is verified using the specific emission amount ei', but this can also be modified to verify using the specific suspended amount qi' or specific absorption amount ai' (however, it should be noted that, unlike ei', qi' and ai' are amounts that increase as the number of people in the observation area Ri increases). For simplicity of explanation, it is assumed that the lockdown will not be ended early.

[0227] Assume that when the determination unit 32 executed the determination process at a past determination time Td, the four values ​​saved in the final step S107 were (Td, i, j, L), and L > 0. Then, a lockdown for L days begins from time Td+1, and ends at time Td+L. The update process is executed at an appropriate timing thereafter (for example, the day after the lockdown ends, i.e., time Td+L+1), and at that time, the four saved values ​​are passed as arguments.

[0228] <Update process> The update process will be explained using the flowchart in Figure 12. This update process calls the fourth infection quantification function in Figure 13 as a subroutine. In the following, the following symbols will be used: measurement value VAi, specific airborne volume qi', specific release volume ei', specific residual volume si', airborne threshold Qi', and release threshold Ei'. For accuracy, brackets [ ] will not be omitted.

[0229] The update process begins with arguments Td (determination time), i (collection unit number), j (entry number), and L (countermeasure). Next, the fourth infection quantification function is called (S302). For time t, where 0≦t≦Td+L, qi'[t]=VAi[t] and ei'[t]=qi'[t] / si'[t] are calculated (S3021). Next, using j' as an index, the following process is repeated from the first entry (j'=1) of the update criteria 42 to the last entry (j'=n, where n is the number of entries in the update criteria 42) (S303). First, it checks whether the condition for entry j' (condition j') is true. At this time, the condition can refer to Td, i, j, L, qi', ei', Qi', Ei', environmental data, etc. (S304). If the condition j' is true, the update method for that entry is set to ΔL (S306), and then the countermeasure L for entry j in the judgment criterion 41 is updated to Max(L+ΔL, 0) (Max(a, b) represents a if a>b, and b otherwise). Note that this formula means that ΔL is added to L, but if L is a negative value, 0 is substituted (S307). On the other hand, if the condition j' is false, the process returns to the beginning of the loop (S305).

[0230] For example, consider a continuation of the example of the determination process in the first embodiment. At this time, for example, assume that (Td, i, j = 1, L = 20) are saved as four values. Also, assume that r = ei'[Td + L] / ei'[Td], and the update criteria 42 is composed of three entries as follows: (1) condition = "r < 0.3", update method ΔL = -1; (2) condition = "r ≥ 0.6", update method ΔL = +1; (3) update sentinel.

[0231] The update process performed in this case is as follows: if r<0.3, the lockdown period was too long, so reduce the countermeasure L for entry j in criterion 41 to 19; if r≧0.6, the lockdown period was too short, so increase L to 21; otherwise, leave L at 20.

[0232] <Summary of this embodiment> As mentioned above, by rewriting the judgment criteria 41 through the update process, even if the infection control method initially adopted is not optimal, it can be gradually improved to become optimal. Therefore, for example, even if there is insufficient information about the properties of the pathogen, infection control can be started first and then gradually improved as it is implemented. Furthermore, even if the situation changes over time (for example, the properties of the pathogen change and its infectivity increases or decreases, or the number of people in the population who have acquired antibodies increases, etc.), infection control can be kept optimal by adapting to these changes.

[0233] [Fifth embodiment] In general, a quantity Ii (hereafter referred to as an "indicator") that indicates the infection status or is useful for infection control is expressed as Ii[t]=Ii(t; VAi,si',si'';x,y,...;ui,vi,...) by a function that takes time t, measurement value VAi, specific influx si',si'', environmental data x,y,..., and parameters ui,vi,... as arguments (note that semicolons represent grouping of arguments, but are simply separators like commas). Here, the environmental data x,y,... and parameters ui,vi,... are constants or quantities that change over time. Furthermore, the function Ii may be expressed as a specific formula, or may be realized by a computer simulation that inputs arguments and outputs a result.

[0234] In the embodiments described so far, the indicators used have been the floating amount qi, the specific floating amount qi', the emission amount ei, the specific emission amount ei', the absorption amount ai, the specific absorption amount ai', the number of new infections pi, etc. For indicators Ii expressed as more general functions, if parameters ui, vi, ... can be identified by performing regression analysis or machine learning from the actual measured value of Ii, then Ii[t] can be estimated.

[0235] For example, in the second embodiment, pi was calculated under the assumption that the amount of pathogens taken into the body is proportional to the infection probability, but even if the probability has a more complicated relationship, pi can be estimated once the function Ii is determined.

[0236] Furthermore, once the function Ii is determined, solving Ii for one of its arguments, such as environmental data x, can then express x as a function of the other arguments of the function Ii and Ii[t]. In other words, x can be found inversely from Ii[t]. This allows, for example, estimating the proportionality coefficient (coefficient k in the equation FKX) between the amount of pathogen ingested into the body and the probability of infection from qi',si'',pi. Furthermore, by monitoring the change in this proportionality coefficient over time, it is possible to detect signs of a change in the situation (such as the emergence of a new mutant strain). It is also possible to estimate si' from qi',ei', i.e., to estimate the number of people in the observation area Ri using the amount of pathogens in the air.

[0237] [Sixth embodiment] The infection status of a population may temporarily worsen or improve depending on time attributes (day of the week, time of day, such as morning or afternoon). For example, on Mondays, people may become fatigued quickly after work because the previous day was a holiday, and may shed more pathogens than on other days of the week.

[0238] To control infection without being overly sensitive to temporary fluctuations, one method is to control not only the value at time t but also, for example, the average for the most recent week (for example, for the specific emission amount ei', the average of ei'[t-6], ei'[t-5], ..., ei'[t]). Alternatively, countermeasures can be output taking into account time attributes. For example, the values ​​of the floating threshold Qi', emission threshold Ei', and absorption threshold Ai' explained above could be changed according to the day of the week (for example, Ei' for Monday could be set higher than for other days of the week). More generally, entries that take into account time attributes (not limited to the judgment time Td, but also attributes of other times, such as time Td-1) can be added to the judgment criteria 41 and update criteria 42.

[0239] [Seventh embodiment] According to the inventor's observations of bodily reactions, in the spread of infection caused by the new coronavirus in Japan since early 2020, the amount of pathogens in the air is low in early hours such as the morning and afternoon, and increases over time.

[0240] When restricting people's movements while minimizing damage to the economy as part of infection control, one option is to restrict them only during certain periods of the day, rather than restricting them 24 hours a day. Taking advantage of the above characteristics, if the length of time restrictions are the same, the effect will be greater in the evening than in the morning or afternoon. (Specific absorption amount ai' can be expressed as ai' = ei' * si' * si'', so if the number of people is the same, it will naturally be less when ei' is small. Here, ei' is the specific emission amount, and si' and si'' are the specific residence amounts.)

[0241] [Eighth embodiment] In this embodiment, there is only one sampling unit 21 (i = 1), and only one type of pathogen, pathogen type A, is considered. In FIG. 15, the infection restriction process begins with arguments Td (determination time) and i (sampling unit number). The following loop process is then executed (S402). First, the first infection quantification function is called to calculate the specific airborne amount qi' and the specific release amount ei' (S403). However, it is not necessary to call the first infection quantification function; a second infection quantification function, for example, may be called. Next, it is determined whether the infection status has sufficiently improved. Here, it is determined whether qi' [Td] is smaller than a predetermined threshold Qi', similar to the airborne threshold in the first embodiment (S404). However, it is not necessary to check the specific airborne amount qi'; the specific release amount ei' or the specific absorption amount ai' may also be checked. Furthermore, rather than simply comparing with a threshold, complex conditions, such as the "condition" in the determination criterion 41, may be checked. If the infection situation has improved sufficiently as a result of the determination, the infection restriction process ends. If not, a lockdown is implemented for L days (S405). L is a positive value, and the same value (for example, L = 3) can be used, or it can be changed each time the loop is repeated. The lockdown is then lifted for one day, and people return to normal life (S406). Note that this does not necessarily have to be one day; an appropriate number of days, such as two, can be selected. Finally, when the first infection quantification function is called in the next loop, L + 1 is added to Td so that the infection situation on the day the lockdown was lifted in step S406 can be calculated (S407). This infection restriction process has the effect of deliberately lifting the lockdown for just one day during the lockdown, temporarily resuming economic activity and making it easier to grasp the infection situation (deliberately returning people going out to normal levels to increase the amount of virus in the air, so that the measured value VAi reflects the infection situation of more people). [Explanation of symbols]

[0242] 1. Infection control system 2 Observation Section 21 Collection section 22 Measuring part 3 Analysis section 31 Storage section 32 Judgment section 33 Update section 41 Criteria 42 Update Standards 43 Area Information 5 Analysis device 51 Processing equipment 52 Input Device 53 Output Device 54 Storage device 6 Area

Claims

1. an observation unit that collects the propagation material (X) in the region Ri and measures the amount of the propagation material; an analysis unit that calculates one or more indicator values ​​or approximations of the indicator values ​​for infection control measures for the entire area based on the measured amount of the infectious agent (VXi); An infection control system comprising:

2. The infection control system according to claim 1 , wherein the observation unit collects the propagating material using a collection method based on the accuracy required for infection control, and measures the amount of the propagating material.

3. The infection control system according to claim 1 , wherein the measured value is a quantity different from the air concentration of the infectious agent present in the area.

4. The infection control system according to claim 1 , wherein the analysis unit calculates the value of the index or an approximation of the value of the index using the relationship between the measurement value in the region Ri and the change in the infection status.

5. The change in the infection status is a change in the infection status in an area that is the target of infection control measures, The infection control system of claim 4, wherein the area Ri is the same as the area that is the target of infection control measures, or an area that includes part or all of the area, or a part of an area included in the area that is the target of infection control measures.

6. The change in the infection status is a change in the infection status in an area that is the target of infection control measures, The infection control system of claim 4, wherein the area Ri does not overlap with the area targeted for infection control, and the infection status of the area Ri is similar to the infection status of the area targeted for infection control.

7. The measured value is used as a specific levitation quantity (q') related to the number of the transmitting material per unit volume in the region Ri, The infection control system according to claim 4, wherein the analysis unit calculates the value of the index or an approximation of the value of the index using the relationship between the specific airborne volume (qi') and the fluctuation of the infection status.

8. The infection control system described in claim 7, wherein the analysis unit calculates the value of the index or an approximation of the value of the index using information regarding the amount of population in the region Ri and the specific floating amount (qi') and the relationship with fluctuations in the infection status.

9. The infection control system described in claim 1, characterized in that the analysis unit outputs countermeasures for infection control for the entire area based on the calculated value of the index or an approximation of the value of the index.

10. A step of collecting a propagating substance (X) in a certain region Ri and measuring the amount of said propagating substance; calculating one or more indicator values ​​or approximations of the indicator values ​​for infection control measures for the entire area based on the measured quantities of carriers (VXi); An infection control method comprising:

Images