Epidemiology and Economy: Covid-19 at One Year (Country Study: Japan), extension to April 2021

Epidemiology and Economy: Covid-19 at One Year and Beyond Country Study: Japan Raoul Kennedy ABSTRACT Policies to fully reopen economies are contingent upon perceived progress against SARS-CoV-2. This is an excerpt of a year-long Japan study. The analysis also aims to incorporate maxima and tail exposures. Estimates to February 2021 suggest a sustained rise in reported fatalities casting into doubt a thesis of transition into an endemic phase of the virus. A marked drop in daily cases classified as critically ill in the current period is noted.

Re-examination of the reporting and/or qualitative aspects of the underlying observations may be warranted.

Protecting the elderly and/or the immunocompromised remains paramount; persons aged 60+ comprise 96% of fatalities. Note: The Appendix has been expanded to update the findings to April 2021.

Introduction and Review This cursory analysis covers Japan country data from February 19, 2020 to February 20, 2021.

Background details to these findings, related topics and an extension to April 2021 are found in the Appendices.

Qualifications. It is important to emphasize that the data refer to daily reported cases which is distinct from daily actual cases; there may be reporting errors and delays such that a sudden apparent increase in cases is solely due to improved reporting rather than an actual increase in cases. Moreover, cases coded as Covid-19 are taken to be defined as RT-PCR ‘positive’ for unknown cycle threshold (Ct) values; official medical diagnosis of the disease or comorbidities are not clarified.

Context within All-Cause Mortality. The study’s findings should also be viewed with the perspective of all-cause mortality (ACM) in mind. The following chart shows daily Covidcoded fatalities as a percentage of ACM; e.g. on the x-axis the figure “1” refers to 1% of total daily deaths being coded as Covid-19. This figure reaches a peak of 3.28% recorded in Period 12 (January-February 2021). The centrality of the distribution (0.295, Fréchet) is indicated with a vertical line in the next chart. Exposure to a greater than order-of-magnitude (10x) rise in fatalities from this maximum of 3.28% is estimated at 0.0066; a higher ranked fit (Weibull) suggests lower exposure at 1.7660E-6 (both on a scale from 0 to 1). These distribution fitting

results are found in the Appendices together with the distributions of the study.

Indicators Used in Study. In total there are seven indicators as shown in the columns of Appendix Tables 1 and 2. They are as follows: Serious/Critically Ill Cases (four indicators): H=10x, H=Baseline, Centrality, H=0. Fatalities (three indicators): H=10x, H=Baseline, Centrality. For further definitions, refer to the Appendices.

Periods of Observation: Current Period for this report is Period 12 from January 18, 2021 to February 20, 2021. Note that “current periods” approximate a 30-day time interval. Cumulative Period for this report (also labelled “All” in the Tables): The year from February 19, 2020 to February 20, 2021 (n=368).

Preliminary Summary Findings at One Year Certain period estimates suggest a marked departure from the range of the fitted metadistributions. A concern is the fatality indicator for centrality in the current period at a record high estimate of 82.9 fatalities per day.

Even under the assumption of a fat-tailed -1-

distribution, this estimate is exceeded in only 0.3% of possible outcomes.

At this one-year juncture these divergences may cast doubt on the thesis that in this period we are simply entering an endemic phase of the virus.

The explanation for such a spike is not clear.

However, this raises questions that merit further attention as discussed in the Appendices.

Chart 2. Centrality. The current period estimate of -14.5 reported cases/day (Cauchy 0.04, left tail*) is the lowest estimate recorded since February 19, 2020, suggesting a marked daily decline in serious cases; this estimate is in the far left tail of this metadistribution somewhat off the chart as indicated by the arrow:

Detail: Infection Cycle Analysis Metadistributions are fitted based on the estimates obtained for each indicator during the period February 19, 2020 to November 15, 2020.

Both the highest-ranking fit and fat tailed distributions were reviewed in the comparative analysis. For both brevity and simplicity of comparison, the fitted metadistributions (pdf) of the data are compared to the period estimates (current period, cumulative period, or both).

Generally, the most notable departures into the tails of the metadistribution are shown.

Otherwise, the period estimates have been deemed to be approximately within appropriate bounds (e.g. mid-range) of the metadistribution.

Only the most notable departures into the tails are shown next. For the reported Serious/Critically Ill cases, the fitted metadistribution is typically Cauchy, while for the Fatalities, it is the Fréchet.

The format for the presentation of results is: estimate (metadist name, survival function) where the estimate is generated from the fitted distribution of either a) the current period or b) the cumulative period, followed in parentheses by the metadistribution’s survival function estimate.

(*) Note that the corresponding estimate for the higher-ranked Normal distribution approaches zero.

Reported Fatalities (Appendix 1 Table 2) vs.

Metadistributions Chart 3. H=10x (Exposure to an Order of Magnitude Increase)

Current period. This estimate is near 0 (Fréchet, 1.0) suggesting that exposure is entirely contained within one order of magnitude above the current period centrality estimate of 82.9 (i.e., all possible outcomes are in theory estimated to be less than 829 per day; this current period estimate of zero is not indicated with an arrow in the chart). For perspective, an alternative hypothesis of an approximate doubling of daily fatalities (i.e. to 166 per day vs. current period of 82.9) is estimated to be 7.3198E-4 of possible outcomes given the fitted distribution for the current period (not the metadistribution).

Cumulative Period centrality estimate is 0.125 (Fréchet 0.076), located in the right tail of this metadistribution just outside the chart here:

Reported Serious/Critically Ill Cases (Appendix 1 Table 1) vs. Metadistributions Chart 1. H=Baseline: Current period estimate of 0.17 (Cauchy 0.39) is within the mid-range and is indicated with an arrow:

This 10x exposure estimate of 0.125 increases somewhat over the prior period cumulative

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estimate of 0.106, but remains well below that of Period 10 (December) at 0.285.

Appendix 1: One Year Study Shortcomings and Discussion. The sample sizes are small and in some cases data points may not be usable due to errors in the data reporting; however, modifications to the data are generally avoided so as not to assume what the data should have been. As noted above, reported cases is distinct from actual cases, such that a worsening could be simply due to improved reporting.

Changes in reporting or definitions also may occur, such as the discontinuation of reporting on age data for “serious/critically ill” cases and their definitions on May 7, 2020, and subsequently recommenced although definitions may vary by Prefecture. Refer to the Addenda below for qualifications regarding the study.

Regarding the highly divergent current period results of these tentative findings, particularly for the centrality of fatalities, the explanation is unclear. Possible scenarios include reporting issues (e.g., large batches of delayed data logged in the current period), environmental and seasonal factors, or the qualitative nature of the underlying observations (e.g., SARS-CoV-2 variant for which no cross immunity exists*, changes in numbers or proportion of comorbidities coded as positive for Covid-19, or changes in testing procedures including cycle threshold (Ct) values amplifying positives, etc.).

Demographics may play a role over the longer term as the population in higher-risk age groups rises. (*) Regarding recent variants see Frampton (2021) and Davies, et al. (2021). Note the possible need for adjustments in the data to account for seasonal surges. Re: T-Cell and preexisting antibody cross-reactivity see Grifoni, et al. (2021), Majdoubi, et al (2021).

Chart 4. Centrality. The current period estimate of 82.9 reported fatalities/day (Fréchet 0.00299*) is the highest estimate recorded to date and is over 1.5 times that of the prior period (period 11) and 3.42 times that of period 10. The arrow points to the far right of this metadistribution, off the chart:

*Note that the corresponding estimate for the higher-ranking fit (Gamma) is 2.9084E-8.

Age Distribution As of February 17, 2021, of the fatalities, 88% are seventy years of age and above, 96% are sixty and above, and 98% are 50 and over (any slight differences in the chart are due to rounding).

Chart 5 next shows the age distribution.

Chart 5. Age Distribution

Mortality from Delayed Treatments. A particular concern is increased non-Covid deaths (eg. cancer, heart disease) attributable to delayed or missed routine treatments during 2020 due to pandemic fears; to what extent such deaths were coded as Covid-19 based on PCR-positive results is not known.

APPENDICES The appendices consist of Appendix 1 for the one-year study to February 2021, and Appendix 2 that extends the findings to April 2021.

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Methodological Notes

Distribution Fitting Results (Parameters, Ranking) Cumulative Observation Period: February 19, 2020 to February 20, 2021.

Reported Serious/Critically Ill Cases.

Kolmogorov-Smirnoff statistic rank=1, Anderson-Darling statistic rank=1 and ChiSquared statistic rank=1) for Cauchy (sigma 4.37, mu 0.98); Min=-INF, Max=+INF, Mode=0.98; mean, variance and higher moments do not exist.

Reported Fatalities. Kolmogorov-Smirnoff statistic rank=16, Anderson-Darling statistic rank=16 and Chi-Squared statistic rank 1 for the Fréchet (alpha 0.72, Beta 3.01); gamma not shown due to 2-parameter distribution; Min=0, Max=+INF, Mode=0.88; mean, variance and higher moments do not exist. Note that in Appendix Table 2, Cauchy-based estimates are shown for Periods 1 through 3 and Fréchet for the remaining periods which are generally higher and reflect a more cautious interpretation of possible outcomes. Because of tail properties, a seemingly low cumulative centrality estimate for the cumulative period may appear to contradict the presence of high centrality estimates of multiple periods.

Percentage of All-Cause Mortality (ACM).

Note: Distribution shown in the Introduction. The ACM is an average daily figure based on country death rate per 1000 population (re: World Population Review). Data for this distribution are to April 2021. Kolmogorov-Smirnoff statistic rank=8, Anderson-Darling statistic rank=17 and Chi-Squared statistic rank 12 for the Fréchet (alpha 1.016, Beta 0.236, Gamma -0.043); Min=0.043, Max=+INF, Mode=0.077, Mean 14.77, variance and higher moments do not exist.

PCR Testing Detail. As of February 20, 2021 (i.e.,

tested positive based on PCR testing), the reported a) serious/critically ill cases to positives ratio and b) fatalities to positives ratio are approximately 0.1% and 1.77%, respectively.

Additional figures on PCR-tested positive cases in Japan are provided as follows: 1.Asymptomatic Positives:95.8%; 2.Non-Critical and Asymptomatic Positives: 99.9% 3.Current Positives Rate* (PCR-based testing) 5.1% *Note: Defined as the ratio of “tested positive” to All PCR tests.

Methodological notes are consolidated in Kennedy (2021a) and subject to periodic revisions (re: stress testing, cautionary tails framework). The criticality level of exposure or vulnerability is assigned based on critical zone estimates (typically in the tail) and refers to the extent to which a given scenario could in theory become a reality. However, the results should not be equated with predictions or forecasting; at best they can be viewed as imprecise indicators of vulnerability to specified scenarios. A default assumption of fat tails for the theoretical models is generally made in favor of higher-ranking distributions for reasons detailed in the methodology. Tail Risk. A point-estimate IFR overlooks the distributional properties of pandemics. Epidemiological models may not customarily account for maxima and tail exposures. The danger posed by heavy-tailedness of the distribution is that if diverse segments of the population are succumbing, subsequent fatalities may not only be prolonged indefinitely but may spike repeatedly with no clear upper limit. This report acknowledges the importance of recognizing an Extreme Value Theory approach for pandemics, further detailed in the policy response discussion below. For criticism of single point forecasts such as IFR given fat-tailed properties of pandemics (re: Taleb on singlepoint forecasts, June 14, 2020). Also see Collins and Wong (2020) on fat-tailed properties.

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Notes: 1. In periods 4, 5, 7 and 12, daily reported cases turned negative. Therefore, the H=10x column estimates are not comparable to periods with positive figures; this is because a hypothetical 10fold increase of a negative value naturally tends to result in an increase in the number of possible outcomes exceeding the 10x negative value.

2. The original definition of “critically ill” was requiring ICU or ventilator, but since May 7, 2020 the definition may vary by Prefecture.

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Periods Detail 3/1-3/17 Period 4 5/16-6/16 Period 8 9/17-10/15 Period 12 1/18-2/20 Period 1 4/1-4/17 Period 5 6/17-7/15 Period 9 10/16-11/15 Period 2 4/18-5/15 Period 6 7/16-8/16 Period 10 11/16-12/14 Period 3 All (Cumulative) 2/19-2/20 Period 7 8/17-9/16 Period 11 12/15-1/17 Serious: defined as requiring inpatient treatment, ICU or ventilator.

Reported vs. Actual: Discrepancies may exist between reported and actual cases.

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space of possible outcomes that exceeds H (H as delimiter) that in theory corresponds to no (all) possible outcomes.

Daily Reported Fatalities H=10x H=Baseline Centrality Obs. Period 82.9 1 0 Period 12 52.8 1 0 Period 11 24.2 0.89 0.85 Period 10 7.0 0.99 0 Period 9 5.1 1 0.0084 Period 8 10.3 1 0.003 Period 7 1.9 0.85 0.056 Period 6 1.1 0.701 0.018 Period 5 4.4 0.995 0.048 Period 4 16.6 0.82 0.012 Period 3 3.8 0.14 0.016 Period 2 0.8 n/a 0.044 Period 1 5.0 0.93 0.125 All (Cumulative) Notes: Baseline: The centrality of the initial period (Period 1); H is abbreviation of "hypothesized value" as a delimiter.

H=10x: Criticality given hypothesized value H of 10x same period centrality (e.g. 2 cases/day x 10 = 20 cases/day) H=Baseline: Criticality given the hypothesized value H being the Baseline Centrality: Central estimate [x(P) where P=0.5] of reported cases per day, per fat-tailed distribution.

H=0: Criticality given that H=zero cases/day reported as "serious/critically ill".

Criticality (survival function): Estimate between 0 and 1. E.g. An estimate of zero (one) refers to the

f(x)

Periods Detail Period 1 3/1-3/17 Period 4 5/16-6/16 Period 8 9/17-10/15 Period 12 Period 2 4/1-4/17 Period 5 6/17-7/15 Period 9 10/16-11/15 Period 3 4/18-5/15 Period 6 7/16-8/16 Period 10 11/16-12/14 All (Cumulative) 2/19-2/20 Period 7 8/17-9/16 Period 11 12/15-1/17 Serious: defined as requiring inpatient treatment, ICU or ventilator.

Reported vs. Actual: Discrepancies may exist between reported and actual cases.

1/18-2/20

space of possible outcomes that exceeds H (H as delimiter) that in theory corresponds to no (all) possible outcomes.

Daily Reported Cases defined as "Serious" or "Critically Ill" Obs. Period H=10x H=Baseline Centrality H=0 Period 12 0.98* 0.17 -14.5 0.19 Period 11 0.026 0.77 11 0.8 Period 10 0.02 0.8 9.5 0.83 Period 9 0.035 0.61 2.64 0.75 Period 8 0.015 0.35 0.51 0.57 Period 7 0.95* 0.22 -2.78 0.3 Period 6 0.025 0.729 4.77 0.8 Period 5 0.96* 0.139 -1.29 0.265 Period 4 0.97* 0.144 -3.61 0.199 Period 3 0.213 0.105 0.801 0.544 Period 2 0.020 0.110 6.270 0.819 Period 1 0.039 n/a 1.740 0.740 All (Cumulative) 0.146 0.445 0.98 0.57 Notes: Baseline: The centrality of the initial period (Period 1); H is abbreviation of "hypothesized value" as a delimiter.

H=10x: Criticality given hypothesized value H of 10x same period centrality (e.g. 2 cases/day x 10 = 20 cases/day) H=Baseline: Criticality given the hypothesized value H being the Baseline Centrality: Central estimate [x(P) where P=0.5] of reported cases per day, per fat-tailed distribution.

H=0: Criticality given that H=zero cases/day reported as "serious/critically ill".

Criticality (survival function): Estimate between 0 and 1. E.g. An estimate of zero (one) refers to the

Appendix 1 Table 1.

Reported Serious/Critically Ill Cases

Appendix 1 Chart 1 Fitted Distribution (Cauchy), Reported Serious/Critically Ill Daily Cases: Centrality= (14.5), Cumulative (n=368) 0.072

Probability Density Function

0.064

0.056

0.048

0.032

0.04

0.024

0.016

0.008 0

x 20

Cauchy (4.3677; 0.98041)

Appendix 1 Table 2. Reported Fatalities

Appendix 1 Chart 2 Fitted Distribution (Frechet), Reported Daily Fatalities: Centrality=5, Cumulative (n=368) Probability Density Function

herd immunity might be achievable at a lower rate of infections than customarily assumed.

A recent study featuring a mathematical model also confirms the possibility of lower herd immunity--achievable at a population-wide infection rate of 40%, considerably lower than previous estimates (re: Britton, Ball et al., August 14, 2020). The authors also warn that strict nonpharmaceutical interventions may interfere with the development of natural herd immunity and contribute to an abnormal resurgence of infections when these measures are removed.

Cell-mediated Immunity. Seronegative antibody tests may not reveal an additional source of immunity in populations from cellular immune response. While T-cells may not lead to full sterilizing protection, the level of population immunity to SARS-CoV-2 may be underestimated by antibody testing (re: Hendrik Streeck; Karolinska Institute). Three variables from which immunity originates are hypothesized (as summarized by Martensen, August 18, 2020): 1.Pre-existing immunity. This refers to individuals unexposed to the virus with pre-existing crossreactive T-cell or other memory due to exposure to other coronaviruses; mapping of T epitopes (i.e. surface shapes of proteins) across the SARS-Cov2 genome, yields estimates of 20-50% of the population. 2. T-cell/other immunity. Exposure without antibody response, estimated at up to 40% of population. 3.Traditional B-cell antibody response (10-20% of population, averaged at 15%).

Regarding T-Cell and pre-existing crossreactivity against SARS-CoV-2 also see Grifoni, et al. (2021), Majdoubi, et al (2021).

Environmental Factors. In making comparisons, results may vary among observations due to changing environmental factors affecting the respiratory system, including but not limited to humidity, temperature, variations in pollen, dust, and air pollution. Also, to be considered is the prevalence of other viral infections at the same time, as well as possible adverse effects from vaccinations: This includes standard flu shots that can increase the risk of contracting coronaviruses (re: Cowling, Fang et. al., 2012), or harm caused by vaccinating individuals against the virus that have an active or recent Covid infection.

0.18 0.16 0.14

f(x)

0.12 0.1 0.08 0.06 0.04 0.02 0 0

5

10

15

20

25

x Frechet (0.71471; 3.0062) Excerpts of Prior Reports (Part 2 and 5)

Population Factors and Differential Susceptibility.

The IFR may vary considerably according to the demographics (e.g. proportion of elderly and/or immune-compromised in the population), population density, general health of citizens including metabolic health/obesity, sunlight exposure, proportion of population spending time principally indoors or in confined areas, proportion of households with a single member, prevalence of activities in high viral load “superspreader” events, basic hygiene practices such as hand-washing, ventilation systems within buildings, and nutritional factors including vitamins (e.g. Vitamin D3), borders, island nations, incoming traveler count and origin, etc.

Regional differences within a single country can be significant (re: ENE-COVID study, Spain).

Herd Immunity and Effective Susceptible Population. While coronaviruses may remain

within populations indefinitely, herd immunity is often stated to be attainable at a 60-80% infection rate. However, due to genetic factors a certain proportion of the population (50-80%) may not be susceptible to the disease according to statistician Karl Friston, as well as Kyoto University professor Hiroyuki Miyazawa. Miyazawa states that natural DNA resistance to the virus in the population suggests that a lower rate of infection would be sufficient to prevent further transmission; Streeck (2020) notes the possibility of genetic traits contributing to non-infectability.

Given this possible phenomenon, some degree of

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Infection Fatality Rates IFR. The “infections” of

to disclose the specifics of unrelated conditions or circumstances (e.g. immune system deficiencies, serious comorbidities, and treatment complications leading to life-threatening inflammation).

Japanese Seroprevalence Study (April 2020) IFR Estimate, Tokyo. An antibody seroprevalence study was conducted in Tokyo from April 21 to 28, 2020. Using conservative assumptions of an extended incubation period to May 15, the IFR is estimated between 0.049% and 0.1%. This roughly places the Covid-19 IFR between the high-end and twice the IFR of the flu. A figure of 0.1% is roughly comparable to the Stanford study of Santa Clara County, California’s low-end estimate of 0.12% (Bendavid, et al. 2020). The antibody seroprevalence was estimated at 4.8% for the general population, and 9.1% for medical personnel; this suggests that herd immunity was far from being reached at the time. Note that random sampling cannot be assumed. For further details, see the Methodological Notes next.

Methodological Notes: IFR Estimation. Note the difference between PCR testing and antibody testing: The former can determine if an individual is currently exposed to the virus, while the latter indicates whether the person has ever been exposed to the virus. Chunichi News (April 30, 2020) reports on a Tokyo study using antibody tests (not PCR tests) finding that 5.9% tested positive for SARS-CoV-2 antibodies (n=202). The entire sample (n=202) consisted of two distinct segments: A "general population" (non-medical personnel) sub-sample (n=147), of which 7 tested positive (4.8%) and a medical personnel subsample (n=55), of which 6 were seropositive (9.1%). These samples were not stated to be random.

Using the seropositive general population subsample (n=147, 4.8%) and assuming that the sample was concentrated in Shinjuku Ward and Tachikawa City, Tokyo where the testing clinics were located ("sample area"), an Infection Fatality Rate for Tokyo was estimated. Using a conservative assumption of a longer incubation period (21+ days), the IFR estimate uses the total reported fatalities for the Tokyo region of 219 as of May 15, 2020. The assumed sample area

the IFR refers to having been exposed to the virus, including asymptomatic cases, based on seropositivity in antibody testing. Erroneous comparisons of Covid-19 with the flu are often made using an IFR for the flu of 0.1%; however, this 0.1% figure is consistent not with an IFR but with a case fatality rate “CFR” based on a definition of “serious cases” (i.e. requiring hospitalization/inpatient treatment). A more accurate IFR for the seasonal flu is closer to 0.025% and 0.05% (re: Epidemiologist Cecile Viboud of the NIH, as quoted in Achtenbach 2020). This range is used as the seasonal flu benchmark here.

A “true” IFR will only be known by the end of the infection cycle, a long-term eventuality (re: Hendrik Streeck, 2020). This makes comparisons on an interim basis problematic because countries adopting harsh lock-down policies to "flatten the curve" possibly producing a lower early case count but prolonging the infection. However, this policy may come at the cost of second and subsequent spikes that will lead to more deaths of the elderly and/or frail in those countries without stepped-up measures to protect them. Low estimated seropositivity in many populations suggests that the infection may endure for an extended period, possibly mitigated by cellular immunity and natural resistance as noted above.

For a lineup of IFRs and antibody seroprevalence worldwide, see Table 3 further below.

Age Gradient. Given wide variations in IFRs across age groups as well as by geographic regions (re: ENE-COVID study), it might be informative to evaluate IFRs by age groups, geography as well as other specific factors noted above. However, identifying unexplained shifts of the disease to other segments of the population is essential (e.g. younger and healthier individuals falling sick, but not attributed to identifiable factors such as high viral load superspreader events attended by those groups, or compromised health and immune systems due to high-risk behavior). It is recognized that reports on PCR-positive Covid-coded cases in the young (Re: Retraction, Dreher, Spokesman 6/2021) for medical privacy or other reasons may be unable -7-

constitutes approximately 5.7% of the total population of Tokyo's 23 wards (the wards exclude outlying areas of the Tokyo Metropolitan area). Using this population proportion of 5.7%, we can construct scenarios based on the assumption that this small geographic area of the sample likely does not contain all 219 fatalities for Tokyo, but some (smaller) proportion thereof, beginning with a 1:1 "proportionate" scenario (i.e. sample area proportion of 0.057 x fatalities of 219). Next, a "2x scenario” that assumes that the proportion of fatalities is twice the proportionate figure of 0.057 (i.e.

0.057x2=0.115), and upwards to the maximum worst-case scenario, in which all 219 fatalities are assumed to occur in this small sample area. As of May 15, 2020, the Tokyo IFR is estimated at 0.049% for the proportionate scenario, and 0.1% for the 2x proportionate scenario. This roughly places Covid-19’s IFR between the high-end to twice the IFR of the flu (as noted above per Viboud in Achtenbach 2020).

Appendix Table 3. Lineup of Estimates Worldwide (IFR, Seropositivity to 6/2020)

Tail Risk A point-estimate IFR overlooks the distributional properties of pandemics. Epidemiological models may not customarily account for maxima and tail exposures. The danger posed by heavytailedness of the distribution is that if diverse segments of the population are succumbing, subsequent fatalities may not only be prolonged indefinitely but may spike repeatedly with no clear upper limit.

This report acknowledges the importance of recognizing an Extreme Value Theory approach for pandemics, further detailed in the policy response discussion next. For criticism of single point forecasts such as IFR given fat-tailed properties of pandemics (re: Taleb on singlepoint forecasts, June 14, 2020).

Caveat on Definitions. The potential for inflation of “Covid” statistics can occur by means of the definition of Covid infections/cases and deaths. When defined as (or coded as) “PCR positive,” Covid statistics can be misleading and erroneously conflated with cases of severe illness (per prior case definitions) and fatalities that may be entirely unrelated to infection with SARSCoV-2. Reliance on PCR-positives means that a clear alternate cause of death can still be coded as a Covid death (re: Melendez 4/20). Moreover, adding to the confusion is the cycle threshold (Ct) values of the PCR tests if the Ct values are set at levels that are likely to produce a higher number of false positive ‘cases.’

Public disclosures and protocols suggest Ct values having been set as high as 37 to 45 (re: Corman-Drosten) which may be scientifically suspect (Borger, et al., 2020) due to amplification leading to detection of high levels of noninfectious and dead viral debris. While Ct values of below 25 are more likely to indicate actual infection of some kind and generate fewer false positives, RT-PCR results cannot be viewed as a confirmed diagnosis of Covid-19 or of infectiousness. (re: Bullard, 2020; Mina, et al., 2021, Stang, et al., 2021) Isolates. For those identified as PCR ‘positive’, lab investigation have obtained SARS-CoV-2 isolates in as little as an estimated 15% of such positives (Jaafar, et al., 2020). Moreover, any -8-

conclusion of a correspondence between positives matched to any isolates (positives-toisolates) and an official medical diagnosis of Covid-19 remains unclear.

If a pathogen with multiplicative effects has been identified as the source, it also must be properly and fully isolated. On the question of isolation and methodology re: Akhwale, et al. (2019), Li, et al. (2020), Giannessi, et al. (2020), and Morell et al. (2021).

A challenge in identifying unknown biologicallyengineered pathogens (re: gain-of-function research) concerns technological developments that allow for elimination of nucleotide residues and construction of chimeric viruses featuring a backbone such as RaTG13. (re: Martensen, 2020). On possible origins of SARS-CoV-2, re: Andersen, et al., 2020 and Lam, et al., 2020

parties are insulated from the costs and liability for their actions while able to reap financial or other rewards. As biotechnologies advance, a disturbing incentive expands for entities to manufacture and offer some form of ‘antidote’ for a real (or even fictitious) bioengineered pathogen that they or related parties produce; moreover, the threat may be binary, e.g. the antidote to the pathogen may itself be a bioweapon. Such actions may originate differently, either from a ‘rogue’ or hostile source, or officially and unknowingly promoted and funded by public policy through corporate and state rent-seeking arrangements.

Although beyond the scope of this brief discussion, there is a bioethical imperative to address the proliferation of potentially catastrophic moral hazard while protecting populations, economies, and human rights.

Treatment, prophylactics, and protocols grounded in evidence-based medicine, including proper nutrition and supplements, can play important roles in combatting pathogens. There is a moral imperative that vital information to protect health not be suppressed as political favors to certain special interest groups.

Policy Response This section on policy response is incomplete and speculative but highlights some key areas including the crucial issue of moral hazard. The juxtaposition of epidemiology and economy does not suggest that these are necessarily in opposition. An initial and crucial challenge is confirming the truth about a reported outbreak: Initial reports may be erroneous, and causes may not be necessarily pathogenic. Other factors may play a role such as false information, pollution, chemical toxicity, nutrition and lifestyle, comorbidities and pre-existing conditions, substance abuse, etc. Information distortions can be driven by incentives to promote false narratives or to suppress information for financial, political, or other gain.

If a novel virus is suspected, a better understanding of the viral genome, the progression of infections, the natural capabilities of the human immune system, as well as evidence-based medicine for effective, early treatment of diseases can help us better and more ethically manage epidemics or pandemics to respect human rights.

Moral Hazard. A critical issue is formulating a policy response that balances the economy and human rights amid pathogenic and biomedical risks. The danger of moral hazard exists when

Appendix 2: Extension to April 2021 This extended analysis covers Japan country data over 14 months between February 19, 2020 to April 18, 2021. Current Period for this report is Period 14 from March 16, 2021 to April 18, 2021.

Cumulative Period for this report (also labelled “All” in the Tables): The period from February 19, 2020 to April 18, 2021 (n=425).

The two tables for Appendix 2 are found at the end of the document.

Reported Serious/Critically Ill Cases (Appendix 2 Table 1) vs. Metadistributions The centrality of serious/critically ill cases has shifted from a record low of -14.5 reported cases/day (Cauchy 0.04; Normal near 0; left tail) in Period 12 to February to +9.3 reported cases per day: (Cauchy, 0.07). While a worsening, the current period estimates are roughly within the range of the Period 10 (December 2020)

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estimates for both centrality and order of magnitude exposure.

Reported Fatalities (Appendix 2 Table 2) vs.

Metadistributions Exposure to an order of magnitude increase remains close to the prior period estimates for both the current and cumulative period estimates.

Chart 1. Centrality. The current period estimate of 28.4 reported fatalities/day (Fréchet, 0.025) drops back to the level of Period 10 (Dec 2020) of 24.2, but with a lower order of magnitude exposure to near zero compared to 0.85 in Period 10, suggesting improvement. The current period arrow indicating the estimate of 28.4 lands just beyond this metadistribution chart cutoff at 25.

Addenda This research is shared as a public service; if helpful, readers are encouraged to pay it forward by adding the imputed value of this service to a reputable charity, preferably with priority given to the most vulnerable, including defenseless animals. Organizational reputations may be researched through sites such as charitynavigator.org.

This preliminary analysis is exploratory, may contain significant errors and is subject to revisions; the author makes no claims 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 events.

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Appendix 2 Chart 1. Fitted Distribution (Cauchy), Reported Serious/Critically Ill Cases: Centrality=0.95/day, Cumulative (n=425)

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Appendix 2 Chart 2. Fitted Distribution (Frechet), Reported Fatalities: Centrality=9.7/day, Cumulative (n=425) Probability Density Function

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Note: The tables for this Appendix 2 are found at the end of the document.

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Note on references: Online references may exclude “https.” Population and mortality data are also sourced from worldpopulationreview.com. Some of the studies cited may be pre-prints and not peer-reviewed; references to media reports may be followed by the name of the associated author and/or major source if known. Some dates listed may vary slightly according to time zone differences.

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Note: Any remaining addenda and tables for Appendix 2 (Table 1 and Table 2) are attached to the remaining pages of this document.

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Pollán, Marina, M.D.., Pérez-Gómez, Beatriz, M.D., et al., “Prevalence of SARS-CoV-2 in Spain (ENECOVID): A nationwide, population-based seroepidemiological study,” The Lancet, July 2, 2020 DOI://doi.org/10.1016/S0140-6736(20)31483-5 Sayers, Freddie., “German Virologist: Covid-19 Less Deadly than Feared,” The Post (Unherd), May 5, 2020 (//unherd.com/thepost/german-virologist-finds-covidfatality-rate-of-0-24-0-36/) Sayers, Freddie, “Karl Friston: Up to 80% not even susceptible to Covid-19,” The Post (Unherd), June 4, 2020. (//unherd.com/2020/06/karl-friston-up-to-80not-even-susceptible-to-covid-19/) Schraer, Rachel., “Coronavirus: Immunity may be more widespread than tests suggest,” BBC News Health, July 1, 2020. (re: Karolinska Insitute) Stang, Andreas., et al., "The performance of the SARS-CoV-2 RT-PCR test as a tool for detecting SARS-CoV-2 infection in the population," Journal of Infection, May 30, 2021.

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TABLES FOR APPENDIX 2: EXTENSION TO APRIL 2020 Appendix 2 Table 1: Reported Serious/Critically Ill Cases

Notes: 1. In periods 4, 5, 7, 12 and 13, daily reported cases turned negative. Therefore, the H=10x column estimates are not comparable to periods with positive figures; this is because a hypothetical 10-fold increase of a negative value naturally tends to result in an increase in the number of possible outcomes exceeding the 10x negative value.

2. The original definition of “critically ill” was requiring ICU or ventilator, but since May 7 the definition may vary by Prefecture.

Appendix 2 Table 2. Reported Fatalities Daily Reported Fatalities Obs. Period H=10x H=Baseline Centrality Period 14 3.55E-12 1 28.4 Period 13 2.00E-15 1 50 Period 12 0 1 82.9 Period 11 0 1 52.8 Period 10 0.85 0.89 24.2 Period 9 0 0.99 7.0 Period 8 0.0084 1 5.1 Period 7 0.003 1 10.3 Period 6 0.056 0.85 1.9 Period 5 0.018 0.701 1.1 Period 4 0.048 0.995 4.4 Period 3 0.012 0.82 16.6 Period 2 0.016 0.14 3.8 Period 1 0.044 n/a 0.8 All (Cumulative) 0.111 0.99 9.7 Notes: Baseline: The centrality of the initial period (Period 1); H is abbreviation of "hypothesized value" as a delimiter.

H=10x: Criticality given hypothesized value H of 10x same period centrality (e.g. 2 cases/day x 10 = 20 cases/day) H=Baseline: Criticality given the hypothesized value H being the Baseline Centrality: Central estimate [x(P) where P=0.5] of reported cases per day, per fat-tailed distribution.

H=0: Criticality given that H=zero cases/day reported as "serious/critically ill".

Criticality (survival function): Estimate between 0 and 1. E.g. An estimate of zero (one) refers to the space of possible outcomes that exceeds H (H as delimiter) that in theory corresponds to no (all) possible outcomes.

Periods Detail Period 1 3/1-3/17 Period 4 5/16-6/16 Period 8 9/17-10/15 Period 12 1/18-2/20 Period 2 4/1-4/17 Period 5 6/17-7/15 Period 9 10/16-11/15 Period 13 2/21-3/15 Period 3 4/18-5/15 Period 6 7/16-8/16 Period 10 11/16-12/14 Period 14 3/16-4/18 All (Cumulative) 2/19-2/20 Period 7 8/17-9/16 Period 11 12/15-1/17 Serious: defined as requiring inpatient treatment, ICU or ventilator.

Discrepancies may exist between reported and actual cases; "case" definition assumed PCR "positive," Ct value undefined.

Addendum: Epidemiology and Economy Note The analysis relied on observations over 15 months between February 19, 2020, to May 17, 2021. General concerns raised, which are not limited to the country under study, culminated in the decision to pause the study. Ten issues are summarized: 1. Reported vs. Actual. It has been recognized from the outset that what is referred to as the” data” refer more precisely to daily reported cases which is distinct from daily actual cases; there may be reporting errors and delays such that a sudden apparent increase in cases is solely due to improved reporting rather than an actual increase in cases.

2. Case (Re)definitions. Cases appear to have undergone an expansive redefinition such that the definition of “case” is no longer limited to critically ill patients. Cases have been redefined to include positive results based on RT-PCR testing, including for asymptomatic individuals. As RT-PCR ‘positives’ cannot be equated with official diagnosis of Covid-19 (Re: Mina, et al., 2021, Stang, et al., 2021), such a broad definition of ‘case’ may have biased statistics towards a higher apparent prevalence of Covid-19.

3. Isolates. Another factor contributing to possible overstating of actual cases of infection concerns detection of isolates of SARS-CoV-2; international study findings do not support the thesis that PCR ‘positive’ results are synonymous with presence of the SARS-CoV-2 virus in cells (re: Jaafar, et al., 2020; Bullard, et al., 2020).

4. Ct Values. Complicating the evaluation of ‘cases’ are the cycle threshold (Ct) values. If these values are set at certain levels (e.g., Ct>35) the results may be scientifically meaningless (Borger, et al. 2020). Even for Ct<25 which likely suggests presence of infection, a ‘positive’ PCR result at a low Ct threshold could indicate infection with something other than SARS-CoV-2 and cannot categorically be equated with a diagnosis of Covid-19.

5. Comorbidities of those coded as Covid-19 are not clarified. If a positive PCR result is obtained and used to report a case as Covid-19, other health conditions may be under-reported and Covid-19 data may be over-reported.

6. Differential Susceptibility. A major concern is any failure to clearly identify special cases of increased susceptibility to infection among certain individuals (e.g., comorbidities, immune system, nutritional or other deficiencies, obesity, etc.) especially for younger age groups. If no distinction is made, it may be assumed that those specific types of cases are representative of others in the same age group (i.e., this assumes uniform susceptibility rather than differential susceptibility; re: Britton, 2020; Friston, 2020). This assumption not only can misleadingly inflate the number of cases of Covid-19, but potentially distorts the assessment of risks of contracting Covid within that age group, overstating their risks that could be used to justify unwarranted therapies leading to adverse events in youth.

7. Treatment Delays (2020). Any recent surges in mortality may have resulted from delays in treatment of these comorbidities (e.g cancer, heart disease) that nevertheless may be coded as Covid-19 due to PCR ‘positive’ test results; sadly, the cause may more accurately be attributed to lack of proper care during the pandemic period.

8. Treatment Suppression. Although no evidence is cited here, a concern is that some number of fatalities coded as Covid-19 were the result of the withholding of treatments that were known to be effective but for regulatory or other reasons, were not provided to the patients in a timely manner or at all, leading to an increase in reported Covid deaths although the patients could have recovered.

9. All-Causes. The analysis should also be viewed within the context of all-cause mortality (abbreviated here ACM): The centrality of the fatalities is estimated to be 0.3% of ACM (i.e., 3/10th of one percent of all daily fatalities.

Given this relatively small proportion, a question is raised as to the singling out of this coronavirus for continuous scrutiny when other health conditions may warrant at least as much attention.

10. Seasonality. A further complication which may remain unaccounted for is the seasonal rises of infections-including influenza A, B, other coronaviruses or other illnesses aggravated by cold Winter weather. For reasons stated above in Item 2, any such surges unrelated to Covid-19 may nevertheless have been coded as Covid-19 based on ‘positives” from RT-PCR testing, further inflating the number of Covid ‘cases.’