Summary
This study centers on the relationship between deaths coded from tested “positives” and all-cause mortality (2020-2022).
This relationship is examined over the two-plus years from February 2020 to March 2022 in Japan, although additional commentary is country non-specific.
For additional reports on other countries including South Korea (Berenson 2022) and Taiwan (Chudov 2022), see the Comments section or References.
Timeline (Chart 1). The following chart shows the daily deaths coded from tested “positives” as a percentage of daily average all-cause mortality (ACM) from February 19 2020 to March 19 2022, together with timeline indicators of key events in red font. The x-axis shows the dates of the series, totaling 760 data points. The y-axis is the percentage of deaths coded from “positive” test results such as RT-PCR as a percentage of the average daily total deaths by all causes.
The timeline is divided into three major sub-periods or phases:
1. Testing (Early) (beginning with the data reporting on February 19, 2020). This period was marked by mass media amplification of the “positives” results, redefining “positives” to mean “cases” (also see the Clarifications and Shortcomings section below on the coding of “positives.”)
2. (Mass) Injections: Injections #1 and #2 refers to the public rollout period approximately April through September 2021 (although the earliest injections appear to have been administered on a limited basis for healthcare and other personnel beginning February 2021). Also, on August 13 2021 Japan reportedly relaxed restrictions on use of Ivermectin for treatment; this has been attributed to a possible decline in “positives/cases” beginning two weeks later (Campbell, 2021). For more on issues surrounding coding and “positives” see the Clarifications and Shortcomings section.
3. Injection #3 (aka “booster” beginning in February 2022).
Peaks. A first peak of 3.8% of all-cause mortality was recorded in early 2021 (January 18 and February 20); this early peak is referred to as the baseline in subsequent discussion below. A second peak occurred shortly after the start of the first series of mass injections on May 18, 2021 at 6.79% of ACM, seen as a single towering line approximately at the center of the chart). During the 3rd injection period beginning approximately February 2022, there has been a cluster of values near the May 18 2021 peak with a maximum of 7.1% on February 22 2022. This dataset ends on March 19 2022.
Distributions and Key Metrics
The distributions are divided by the sub-periods described in the previous section; the distributions selected presume fat-tailed characteristics that aim to derive more cautious exposure estimates (Fréchet).
Sub-period 1. Early Testing Phase (beginning with the data reporting on February 19 2020 to April 30 2021 approximately prior to the beginning of the mass injection rollout).
Sub-period 2. (Mass) Injection Phase: Injections #1 and #2 from May 1 2021 to January 30 2022. This phase primarily refers to the mass public rollout period (approximately April through September 2021) although as noted above the earliest injections appear to have been administered on a limited basis for healthcare and other personnel beginning mid-February).
Sub-period 3. Injection Phase #3 beginning February 1 2022 to the most current data as of March 19 2022.
The x-axis of each theoretical distribution shows daily “positive”-coded fatalities as a percentage of average daily all-cause mortality; for example, on the x-axis the figure “1” would refer to 1% of total daily average deaths being “positive”-coded as Covid-19. For reference, over the two-year plus period from February 19, 2020 to March 19 2022 this figure ranges from a minimum 0%, to a maximum of 7.1% on February 22, 2022 after the start of the third sub-period.
Chart 2. Sub-period 1: Early Testing Phase (February 19 2020 to April 30 2021)
Chart 3. Sub-period 2: Injections #1 and #2 May 1 2021 to January 30 2022
Chart 4. Sub-period 3: Injection #3 February 1 2022 to March 19 2022
Relative Spatio-temporal Centrality (RSC). For the first two sub-periods, RSC (abbreviated centrality) is estimated at between 0.24% (i.e. ¼ of 1% of all-cause mortality) and 0.304% (Fréchet). For the last sub-period (Sub-period 3 Injection #3, Chart 4 above) this RSC estimate jumps to 4.36% of ACM, a 14 -fold increase over the maximum previous sub-period of 0.304%.
Exposure Metrics. Four estimates of exposure above a given value are made as follows:
1. Exposure >3.28% of ACM (Baseline). Recall that this was a maximum established in Jan-Feb 2021 during the early testing phase, used as a baseline here.
2. Exposure >32.8% of ACM. This represents an order of magnitude increase over the baseline of 3.28% (#1 above, or 3.28%x10).
3. Exposure >50% of ACM. This describes a hypothetical scenario in which “positive”-coded deaths exceed half of all-cause mortality.
4. Exposure >99.9% of ACM. This describes a hypothetical scenario in which “positive”-coded deaths exceed 99.9% of all-cause mortality.
Table 1. The estimates for both RSC and exposure metrics for each of the sub-periods are summarized in the following table.
Interpretation. Sub-period 3 which corresponds to the 3rd round of injections is very distinct from the other sub-periods, both in terms of centrality and exposure estimates. Moderate increases in some exposure metrics from sub-period 1 to sub-period 2 are seen, but a major shift occurs in sub-period 3 with an estimated 78% of outcomes expected to exceed 3.28% of all-cause mortality. Interestingly, for the remaining three exposure metrics (>32.88%, >50%, and >99.9%) the distributional characteristics for sub-period 3 suggest less tail obesity than for the other periods, to such a degree that the exposure estimates result in nearly zero possible outcomes. Moreover, for reasons unknown, the best fitting distribution (Wakeby, not shown here) has a maximum possible value of 7.33 (rounded), a clearly established cap.
Commentary. The reasons for the marked change observed in sub-period 3 compared to the other periods are not clear. In additional to routine seasonal factors (e.g. additional debris from flus and colds, etc., being detected as “positives”), another explanation may be an upward calibration of the cycle threshold values (Ct values) of the PCR tests which might be expected to yield more positives than otherwise; the reasoning behind a re-calibration is not known. It is also possible that the injections play a role (re: Kirsch 2021 in reference to the Burkhardt autopsies, or other pathologies as identified by Klok, et al. 2022). Possible immune system depletion resulting from injections and resulting pathogenicity is also a concern (re: Murphy and Longo 2022). The existence of such adverse events leads to a possible source of confusion: Injection-induced immune deficiency could render injectees more vulnerable to illness and infection (including coronaviruses) and the likelihood of being coded as “positive,” obscuring the underlying cause which may be the injections; further investigation on this possibility may be warranted. Also see data shortcomings in the next section.
Clarifications and Shortcomings
Distributions. Note that goodness-of-fit distribution fitting results for the selected distribution used in a study may be inferior to higher-ranking thinner-tailed distributions, but the importance of a conservative estimate is deemed to outweigh higher ranked fits.
All-Cause Mortality (ACM). The ACM is an average daily figure based on country death rate per 1000 population (re: World Population Review for 2020 and Macro Trends for 2021). In Japan the incidence of all-cause mortality is a daily average of approximately 3700 and 3800 in 2020 and 2021, respectively. To illustrate this relationship, in 2021 a reported figure of 38 daily “positive”-coded deaths would result in 38/3800 or 1% of daily average all-cause mortality. Because the “positives” are reported daily figures and not averages, comparing them to a daily average of all-cause mortality introduces a bias that overstates the ratio of positives to ACM when ACM is in fact elevated, while understating the ratio when ACM is in fact low. An alternative computation is made to compare the variables when they are both averaged. The average positive-coded deaths range between 23 to 52 daily, with an average for the entire period from February 2020 to March 2022 of 35 per day. The resulting ratio (%) of “positive”-coded relative to ACM ranges between 0.006 (0.6%) and 0.014 (1.4%), with an average for the entire period from February 2020 to March 2022 of 0.0094 (0.94%). These averaged ratios can roughly be compared to the figures for centrality in Table 1 above: 0.24, 0.30 and 4.36 for sub-periods 1, 2 and 3, respectively.
Coding based on “Positives.” Positive results from testing are often confused with diagnosis of an illness. The public may have been deceived by the redefining of “positives” as “infections” or “cases,” implying an association with disease. This strategy of widespread testing could have misled many given that what was “detected” was not a disease or disease-causing agent but markers based on a predetermined sequence (or set of sequences) of genetic material which may include viral debris and dead nucleotides. Moreover, analysis of such “positive” samples does not support the thesis that a “positive” result predicts the presence of target genetic material within cells (re: Jaafar, et al., 2020; Bullard, et al., 2020). Cycle Threshold (Ct) values are another variable in the number of “positives” reported. Ct values correspond to the number of cycles to amplify the genetic material obtained. Amplification can be misleading by turning a minute quantity of targeted item(s) into a large quantity that is defined as “detection” of a “positive.” The setting of these Ct values can also be used to alter the frequency of “positive” results. At Ct values of Ct>35 the results are likely to be scientifically meaningless (Borger, et al. 2020). However, the recommendation by Corman-Drosten (2020) to the WHO was a Ct value exceeding 40; such a high Ct value standard may have been adopted by many countries despite the likelihood of inflating the number of “positives.” For example, the Ct value in the US was in the range of 37-40 and that of Japan between 40-45 cycle amplifications (Takahashi, et al. 2021 p.38). However, even at low Ct values of (e.g. Ct<25), “detection” is distinct from diagnosis of a condition or illness (Re: Mina, et al., 2021, Stang, et al., 2021).
Data: Reported vs. Actual. The daily reported figures of “positive”-coded deaths were sourced from Toyo Keizai (re: Ogiwara 2020-22); as noted in the previous section “positives” are generally referred to as 感染者 (“infected/infections” or “cases”). It has been recognized from the outset that what is referred to as the” data” refer more precisely to daily reported numbers which is distinct from daily actual numbers; there may be reporting errors and delays such that a sudden apparent increase in some number of x is solely due to improved reporting rather than an actual increase of the number of x. This may be unavoidable to a certain degree and may be less of an issue that other points detailed above.
These results are shared as a public service; if helpful consider paying it forward by adding something extra to any donations made to reputable charities, preferably with priority given to the most vulnerable, including defenseless animals. Organizational reputations may be researched through sites such as charitynavigator.org.
The author may also hold positions in securities of companies, including through ETFs, that may have been covered herein. The discussion and any visuals may contain significant errors, are subject to revisions and are provided 'as is' solely for informational purposes, not for trading or investment advice. This preliminary analysis is exploratory; no claims are made as to the validity of data, assumptions, theoretical models, and methodologies; results may be based on prior data that do not reflect the most current market or other events.
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For a report on South Korea, see https://alexberenson.substack.com/p/the-light-at-the-end-of-the-mrna/comments
For a report on Taiwan see https://igorchudov.substack.com/p/depopulation-of-taiwan