The Ethical Skeptic

Challenging Agency of Pseudo-Skepticism & Cultivated Ignorance

The Definitive Guide to Ethical Skeptic’s (TES/ES) Coronavirus SARS-CoV-2 (2019) Analysis

Below are some key terms, charts and principles which are necessary in a Wittgenstein-level understanding of what is occurring behind the scenes with Covid-19, as tracked and described by The Ethical Skeptic.

“Don’t you see that the whole aim of Newspeak is to narrow the range of thought? In the end we shall make thoughtcrime literally impossible, because there will be no words in which to express it.” ~1984, George Orwell

Foremost one should bear in mind that that name ‘The Ethical Skeptic’ is posed as a discipline of thinking and not in reality a personal appellation. Indeed it is framed in the impersonal, in such context as one might place The Creative Architect or The American Practical Navigator; more publication title than boast. With that now clear, during the Coronavirus-CoV-2 (2019) pandemic I have made an effort to lend my professional skills as best they could be applied to aid in its response. I have had the good fortune to be called to apply such skills in minor contribution to both the response effort itself, and as well on the Twitter social media platform. Therein, particularly in terms of moral and knowledge support to those at risk of both the virus impact and our over-reaction to/ignorance of the Covid-19 virus’ dynamics.

“MIT researchers found that Covid-19 skeptics — far from being “data illiterate” — often use sophisticated data visualization techniques to argue…”

“It’s a very striking finding,” says (MIT’s) Lee. “It shows that characterizing [Covid Caution] groups as data-illiterate or not engaging with the data, is empirically false.”

“We discovered that these groups leverage skills and tropes that are the markers of traditional scientific inquiry. “

Massachusetts Institute of Technology Study: How COVID-19 skeptics use public health data and social media to advocate for reopening the economy

During that time, I began to grow uncomfortable with the ways in which this pandemic was exploited to craft political power and allow extremists to enact harm on their opponents’ economics, well being and lives. Thus I began to apply my skills as well to see if the media and agency-bearing academics were indeed telling the truth. As it turned out, in critical significance, they were not.

Marketing disinformation as an academic, governmental or media authority,
to compel despair, panic or compliance inside a population under duress of epidemic, war or economic collapse –
these things are indistinguishable from war crimes. They are a violation of basic human rights, and as such
constitute acts of class harm, scienter, racketeering and oppression.

During this time frame and series of tweets, a number of key concepts and terms have arisen which have provided to be a source of confusion for new readers of The Ethical Skeptic. Terms however which were necessary in dispelling cultivated ignorance around the topic – both Orwell’s Newspeak and enforced Nelsonian ignorance. Since the account is accruing a number of new followers each day, I cannot possibly attempt to re-define those terms every day for every single new follower. I would never have time left to post a new idea or conduct my real life professional work as well.

Accordingly, here are my positions on Covid-19:

Covid-19 is a real, highly communicable, and dangerous virus; deadly primarily to our beloved fellow citizens over the age of 65 and those bearing specific medical vulnerabilities. Covid is not a hoax. It is a grave societal/pandemic concern. All my analysis and commentary has reinforced this concern from the very start. This danger mandates action on all our parts.

I advocated for a limited sequestration when knowledge level was low, Covid was escalating very fast, hospitals were overfull, and iCFR was uncertain early on (reported as being ~3 to 4%). I advocate for the wearing of N95 masks (I am not and have never been ‘anti-mask’ as MIT lazily bucket-characterized my work here: MIT Data Visualizations Behind Covid Skepticism – my firm designs labs and clean rooms as part of its service base, I am a pro-mask designer and planner, and am a mask advocate for Covid as well), social distancing, hygiene/cleanliness, modified HVAC and sewage/manure exposures, limited large gatherings and other NPIs as would help limit a peak surge of Covid-19. These actions will have limited effect on its eventual seroprevalence however. Our best course of action short of a safe and effective vaccine, is a level of herd immunity inside the 20% of our population who are connectors (most mobile and transaction-bearing citizens). Beyond this I have not made and do not make forecasts on what Covid will do as a virus. It remains a threat to our public health.

Once knowledge-level grew (CDC agreed with my iCFR estimate of 0.26%, flu being 0.14%) and smarter actions on our part could be outlined, certain parties blocked and obfuscated preventions, treatments and other mitigating opportunities for months, or made similar hasty panic-fueled bad decisions on behalf of our most vulnerable citizens. Up to 100,000 of our citizens died solely because of this jackboot ignorance. Moreover, these parties felt they could exploit misinformation, flawed testing processes, case-rate exaggeration, timeframe of reporting, underlying cause of death logical confusion, media messaging, and social coercion to impart panic and despair around Covid-19 into our at-risk constituency. This to enact political/economic power and quickly-altered 2020 election processes in 40 US States, all to their non-transparent favor. Another 200,000 citizens then died from iatrogenic gain boost, despair, and disruption from this purposed societal over-reaction.

These no-income-risk parties used their new-found political power to harm their at-income-risk political enemies under the guise of virtue, and did so with abandon and glee. Irrational business lockdowns were mandated/promoted without scientific backing, and continued well after studies demonstrated clearly their inefficacy. Small to medium private businesses were decimated, while globalist, oligarch, offshore-asset, socialist businesses were favored. 36 million Americans were left unemployed, and half of American private and small to medium businesses were irrevocably harmed or bankrupted. Up to 500,000 additional citizens will be killed by these actions of malicious intent, causing the death toll from deception/coercion alone to approach 800,000 US citizens when all is said and done. This pales by far, those deaths actually from and with Covid itself.

This constituted a human rights crime of historically unprecedented method and magnitude. The parties harmed in this process should be awarded class action and oppression restitution from the perpetrators including media, universities, their foundations/syndicates and the involved misrepresenting agencies. These actions constituted high crimes of terror and oppression, under U.S. Code § Section 2331, Title 18 and 18 U.S. Code § 35, as well as Johnson vs. Monsanto CGC-16-550128.

Accordingly, below are some key terms and principles which are necessary in a Wittgenstein-level understanding of what is occurring behind the scenes with Covid-19. It is not that I am forecasting, conspiracy spinning, nor that everything I say is correct – rather that these terms are necessary as underpinning to comprehend the risk entailed to the general stakeholding public – and the hazard presented by those who wish to exploit the tragedy as a means to abuse of their enemies and in furtherance of their political power.

Please note that general terms of Ethical Skepticism can be found at the following two links as well:

The Tree of Knowledge Obfuscation

The Ethical Skeptic Glossary

As well, you can hear The Ethical Skeptic speak about Covid-19 from his perspective, on the Todd Herman Show and 770 KTTH, from 3 September 2020 here:

The Ethical Skeptic on The Todd Herman Show – KTTH 770

Key Covid-19 Charts/Terms/Principles Employed by The Ethical Skeptic

Agency – an activated, intentional and methodical form of bias, often generated by organization, membership, politics, hate or fear based agenda and disdain. Agency and bias are two different things. Ironically, agency can even tender the appearance of mitigating bias, as a method of its very insistence. Agency is different from either conflict of interest or bias. It is actually stronger than either, and more important in its detection. Especially when a denial is involved, the incentive to double-down on that denial, to preserve office, income or celebrity – is larger than either bias or nominal conflict of interest. One common but special form of agency, is the condition wherein it is concealed, and expresses through a denial/inverse negation masquerade called ideam tutela. When such agency is not concealed it may be call tendentiousness.

ideam tutela – concealed agency. A questionable idea or religious belief which is surreptitiously promoted through an inverse negation. A position which is concealed by an arguer because of their inability to defend it, yet is protected at all costs without its mention – often through attacking without sound basis, every other form of opposing idea.

Tendentious – showing agency towards a particular point of view, especially around which there is serious disagreement in the at-large population. The root is the word ‘tendency’, which means ‘an inclination toward acting a certain way.’ A tendentious person holds their position from a compulsion which they cannot overcome through objective evaluation. One cannot be reasoned out of, a position which they did not reason themselves into to begin with.

Amphibology – is a situation where a contention may be interpreted in more than one way, due to ambiguous sentence structure. An amphibology is permissible, but not preferable, only if all of its various interpretations are simultaneously and organically/logically true (not semantically).

Anecdote – a story, recount or stand-alone observation which may or may not constitute epistemic data.

modus praesens – an anecdote to the presence. Often can constitute data in that it observes a presence supporting a contention. It is not proof, however neither can it dismissed by wave-of-the-hand false skepticism.

modus absens – an anecdote to the absence. This is not data, rather most often serves as an attempt to craft data-of-denial (an appeal to ignorance).

Aperçu – the signature terminology or catch-phrases that a person will employ to demonstrate that they are inside the approved club or academic circle around a particular topic. If you identify the same principle, however employ a different name for it, this will stir anger in this type of poseur.

Apparatchik – the opposite of being a skeptic. A blindly devoted official, follower, or organization member, of a corporation, club or political party. One who either ignorantly or obdurately lacks any concern or circumspection ability which might prompt them to examine the harm their position may serve to cause.

Arrival/Arrival Distribution – the novel and incremental pattern and count/quantity of how a given outcome or particular thing occurs over a series of consecutive time (days, weeks, minutes, months, etc.). New cases each day, or fatalities each day, etc.

Attentodemic – a pandemic or other social malady which arises statistically for the most part from an increase in testing and observation activity. From the two Latin roots attento (test, tamper with, scrutinize) and dem (the people). A pandemic/tragedy, whose curve arises solely from increases in statistical examination and testing, posting of latent cases or detected immunity as ‘current new cases’, as opposed to true increases in fact. For a graphic depiction of the results of attentodemic practices inside Covid-19, see Salting/Juking Reported Cases below.

Backlog Stuffing (BS) – State departments of health may unilaterally, or through direction by higher agency or political intermediaries, choose to delay reporting of data inside one week’s period, and further then report several days of data as if it occurred in a single day. In this manner, a sufficient amount of backlog or infrequent report arrivals, can be exploited to craft the appearance a false trend, rise or record in data. When exploited by media to incite panic or despair inside the context of a population under risk, it is a human rights crime as well.

The Bricklayer’s Error – the presumption that academic, heuristic or deep single-function expertise (bricklaying) qualify one to stand as authority as to how the broader issue is to be managed (house is to be built or lived-in). Presupposing that a physicist who studies precession, should be the foremost expert on bicycling. Related to the phrase: ‘Experience trumps consilience. Consilience trumps heuristic.’

Bridgman Point – the point at which a principle can no longer be dumbed-down any further, without sacrifice of its coherency, accuracy, salience or context.

Bridgman Point Paradox – if you understood, I could explain it to you – but then again – if you understood I wouldn’t have to explain it to you.

Broken Window Parable (Bastiat Fallacy) – actually a counter to the broken window parable which proposes that even in disaster, an economy profits on the repair and recovery. The Bastiat Fallacy points out the logical failure of such reasoning.  Proposed by Nineteen Century French economist Frederic Bastiat, the fallacy states that the economic benefit derived from recovering from disaster is never superior to the economic benefit which was lost as opportunity cost, as a result of sacrificing the resources sacrificed in the disaster, nor committed to repair the damage or fix the disaster. The economic benefit of war is never compared to what was lost as a result of the war.

Broken Window Certainty Parable (Bastiat’s Certainty Fallacy) – a modified form of the Broken Window Parable, wherein the claim is made that harm imparted by a bad actor, or disaster cannot be claimed to ‘have been going to happen anyway, even if good decisions were made’. If benefit from such a disaster cannot be claimed as a positive credit for the disaster (Broken Window Parable), then neither can an argument that ‘harm would have happened anyway’ stand as a permissive nor partial exoneration of the disaster or bad action/decisions. Covid upheaval deaths, even though they might have happened under circumstances of good decision making, cannot be therefore deducted from the set of deaths which resulted from a reality of bad decision making.

Case Adjustment Methodology – case raw reporting data was not acceptable to The Ethical Skeptic, as it contained too much agency to be trustworthy. The data was refined by an algorithm, and then those results were tracked for accuracy over time. This algorithm performed very well when compared to other data results and when used as a calculation touch point (consilience). Reported cases were factored by the rate of hyper-testing (T) after April 5, and then also by hospitalization census decreases (H) and increase in hospital dwell time (h) after May 6, by the following formula:

Casuistry – the use of clever but unsound reasoning, especially in relation to moral questions; sophistry. Daisy chaining contentions which lead to a preferred moral outcome, by means of the equivocal use of the words within them unfolding into an apparent logical calculus – sometimes even done in a humorous, ironic or mocking manner. A type of sophistry.

Catalyseur – a third party or media member who seeks to instigate conflict between science and its at-risk public – who further then exploits such conflict to attain career or club advancement, money or power. A conflict exploitation specialist, or any entity which stands to gain under the outcome of a lose-lose conflict scenario which they have served to create, abet or foment. Someone who acts as a third party to two sides in an argument or conflict, who advises about the ‘truth’ of the other party involved, respectively and urges an escalation of factors which drove the conflict to begin with.

CDC Excess All-Cause Deaths Chart 1 – those deaths each week, as tracked in the CDC MMWR database, which are in excess or rise above the typical number of deaths for that same week over the average of a set of previous years. The database used to derive this can be found here:

In the associated chart, we compile each week’s total reported deaths and then track how low weeks -1 through -12 are relative to the final number they arrive at on week 13. This is called the lag curve, and is used to normalize or adjust each week’s Morbidity and Mortality Weekly Report (MMWR) death report by the CDC. Any lag which surprises us above this projected level of cases is termed ‘supralag’. Each week the CDC Lag Curve is adjusted if consistent supralag is observed, in an effort to make sure that supralag is indeed an exception each week.

‘Lockdown Deaths’ are estimated separately by means of the Full Covid Death Accountability Chart each week, and the net figure (net of reduced car accidents and iatrogenic accidental medical deaths) is published as the new baseline (solid beige line), a modified average of each week from 2014 – 2017 (2018 was an exception year and threw the average off to a mis-representative level).

Died ‘With’ calculations come from overlap with the 14 major causes of death in the US, and their commensurate surge with Covid-19 deaths week for week. These death statistics for primary mortality are derived from the National Center for Health Statistics; Weekly Deaths by State and Cause of Death; 12 Aug 2020;

Epidemic Threshold is determined to be 7.2% of all MMWR deaths after Lockdown Deaths have been netted out. The CDC uses anywhere from 5.9% to 7.2% for more flu-like illnesses. Thus this latter figure was chosen for Covid-19.

CDC Excess All-Cause Deaths Detail Chart 2 – those deaths each week, as tracked in the CDC MMWR database, which are in excess or rise above the typical number of deaths for that same week over the average of a set of previous years. The database used to derive this can be found here: This chart shows the calculations which drive and feed Chart 1. Each MMWR weekly report is adjusted for measured CDC lag, compared to last week for determining supralag, netted down by Lockdown Deaths, and then is compared to the actual number of Covid deaths reported by the states 7 days after the date of the MMWR report which Chart 2 is based upon.

At the bottom of the chart resides the latest CDC lag curve, exhibiting the math used to adjust and normalize weeks 1 – 21 of the CDC MMWR report each week. As well, an estimation is made of how many excess deaths have not yet been reported by the states as of 7-days later (green tally at bottom). Finally the full tally of estimated deaths from Lockdown is shown in the green calculation at the bottom.

CDC Wonder Database – a database managed by the CDC which provides comprehensive breakouts of US fatality data by year and MMWR week, along with a query access tool – all of which can be found here:

Close-Hold Embargo – is a common form of dominance lever exerted by scientific and government agencies to control the behavior of the science press. In this model of coercion, a governmental agency or scientific entity offers a media outlet the chance to get the first or most in-depth scoop on an important new ruling, result or disposition – however, only under the condition that the media outlet not seek any dissenting input, nor critically question the decree, nor seek its originating authors for in-depth query.

Chart – as distinct from a ‘graph’, in which two orthogonal measures are compared to each other mathematically, a chart is a demonstration of a set of relationships (mathematical, non-math and/or both) which are being considered for contribution to a specific inference. A graph contains a mathematical function displayed across two labeled axes (abscissa and ordinate). A chart does not necessarily conform to this simple principle. Forcing a chart one does not understand, to become a graph, is a common sign of inexperience.

Consilience – a form of derivation of inference which is stronger than mere inductive inference. A method of deriving confidence in a hypothesis from disparate analyses, sources, media, methods, heuristics, calculations and perspectives – which then without prior manipulation, bear symmetry or agreement in their conclusions. If the banker, butcher, brick-layer, baker and barber all agree that the economy is bad – it is probably bad.

Consilience Touch Point – one instance of consilience between two independently derived observations which bear symmetry or agreement.

Covid-19 Fatality Full Accountability Charts 1 and 2 – this chart attempts to break out the entire body of excess deaths, as reported in the CDC Excess All-Cause Deaths Chart 1 and 2, into the significant type of fatality concerned

  • Died of Covid-19 – self explanatory
  • Died with Covid-19 – significant not that it is implying these are not real Covid fatalities, rather that a pull-forward effect will happen later in the year wherein deaths are actually lower than average
  • Deaths Not Reported – potential Covid-19 deaths, still as such unreported by state DHS/DOH offices
  • Covid-19 Reaction Fatalities – avoidable non-Covid deaths which were actualized either past (or future, but this is not tallied on the charts above) through politically-motivated, propaganda-based, irrational, money-opportunistic, virtue-symbolic, or hate-fueled decision making
  • Covid-19 Upheaval Fatalities – non-Covid deaths which were an unavoidable consequence of a reasoned, well planned, and limited-scope pandemic response
  • Net Accident Reduction – reduction in auto fatalities and iatrogenic accidental deaths

Finally, the total deaths are compared between Covid caused and Covid Reaction cause deaths. Life-years lost are estimated by the following calculations

Excess Cardio/Diabetes x 15 years remaining
Alzheimer’s x 4 years
Stroke Access x 6 years
Flu & Pneumonia x 4 years
Cancer/Medical Access x 20 years
Suicide Addiction Abandonment & Abuse x 40 years

This is compared to an average years remaining for the average Covid fatality of 5.6 years (from the FL risk of death by Covid-Age Chart). The sources for this data are many, and include:

  1. CDC Wonder – Weekly Counts of Deaths by State & Select Causes, 2014-2020;
  2. National Cancer Institute;,and%20139.6%20per%20100,000%20women).
  3. CDC Heart Disease Facts;
  4. CDC Stroke Facts;
  5. CDC Vitals Signs: Suicide on the Increase in US;
  6. Scientific American: COVID-19 Is Likely to Lead to an Increase in Suicides;‘Cries for help’: Drug overdoses are soaring during the coronavirus pandemic;
  7. CDC Drug Overdose Death Statistics; & Talbott Recovery: Alcoholism Statistics You Need to Know
  8. CDC FluView Weekly Influenza and Pneumonia;
  9. Association of Adverse Effects of Medical Treatment With Mortality in the United States; & Wikipedia: Annual Motor Vehicle Fatalities;
  10. Dierenbach: The coronavirus response has been deadly;

Cultivated Ignorance/Cultivation of Ignorance – If one is to deceive, yet also fathoms the innate spiritual decline incumbent with such activity – then one must control and abstract a portion of the truth, such that it serves and sustains ignorance on the part of the general population – a dismissal of the necessity to seek what is unknown.​ The purposeful spread and promotion or enforcement of Nelsonian knowledge and inference. Official knowledge or Omega Hypothesis which is employed to displace/squelch both embargoed knowledge and the entities who research such topics. Often the product of a combination of pluralistic ignorance and the Lindy Effect, its purpose is to socially minimize the number of true experts within a given field of study.

Daily Case Arrivals Chart (Covid-19) – depicts four independently derived elements in comparison, so as to evaluate consilience between those elements and their base assumptions, constraints and calculations. The core arrival form depicted is then used as the basis to derive other charts, which test its validity.

Element 1 – Daily Case Arrivals (Blue Vertical Columns) – These are daily new cases reported by the states at The Covid Tracking Project. These reported cases are actuals as-reported, up until 4/5, whereupon every daily report is then divided by a factor derived from the percentage increase in testing after 4/5. For instance, 10,000 positives derived from a 150% testing rate relative to 4/5, would result in 6,667 positives ‘relative to positives measured on 4/5’. This is called the ‘strike date’. Any date between 4/5 to 4/29 may be chosen as the strike date, and essentially the same curve manifests. A strike date is a point, reasonably close to an inflection point, in which three things occur: 1. we are past a cases peak, 2. testing of only-the-sick has ended, and 3. test kits have just begun proliferating to all areas of the nation in large quantity. This is known as an Indigo Inflection Point. In this way, the true arrival distribution of cases is estimated, and the gain-amplification of testing increases is filtered out of its arrival distribution. As are all constraints, calculations and observations, this arrival distribution is then monitored against other indices over time, to evaluate its performance. It is not take as immediate and final gospel.

After 5/6, the same formulaic principle is utilized, factoring reported cases against hospital admissions – so that an increase in hospitalization admissions can manifest in the case arrival data and not allow the increase in testing alone, to falsely mask or hide a regional surge or outbreak (as did occur in south border counties during the July time frame). Admissions were estimated using state reported daily hospital census, factored by Hospital Dwell Time (see its entry in this lexicon). The following formula encompasses both of these factoring principles after 5/6.

Element 2 – China Reported Case Arrivals (Green Vertical Columns) – These are the actual new Covid-19 cases per day reported by China to the international community. A green dotted line is fitted as an estimate of a more likely case level, based upon the rates of transmission observed by other nations. This dotted line is not further tested for validity, however is also not used in any successive calculations or models.

Element 3 – WHO Consensus Report on SARS 2003-Cov-1 Arrival Form – At the bottom of the chart is a cut and paste of the WHO Consensus raw new cases arrival form. Raw numbers are usable here because it is the only data we hold, and the context of measure is essentially the same throughout its horizon. The arrival form is not valid in the vertical dimension, however is valid in terms of its seasonality (horizontal dimension). The reason this is used in the chart is that coronaviruses are considered to be ‘sharply seasonal’. A quote from one of the studies which concluded this (Monto, et al. Journ of Infect Dis), is contained in the window for this WHO arrival form.

Element 4 – CDC Excess All-Cause Deaths Curve (Yellow Arrival Curve) – This is the number of excess deaths in the US, as derived from the CDC’s weekly MMWR Report. These deaths are then partitioned into Covid ‘with and of’, and ‘Lockdown’ deaths, by separate methodology. This resulting curve of death arrivals by date is then used here to match to case arrivals by date and to examine for consilience in both magnitude and function. Such consilience was consistently observed throughout the modeling period.

Data : Information : Intelligence – data does not inform on its own; especially raw data – which most often will serve to dis-inform one who is not used to being held accountable for the results of their analytical work. A professional principle which cites that ‘data must be denatured of its noise, into information. Information must be then transmuted through consilience and deduction, into intelligence. Intelligence is then the only basis from which to infer or take action. Be wary of agency which exploits the appearance of raw data.’

Death ‘from’/Death ‘with’ Covid – an important principle in that there are those who died of Covid, who simply/trivially had Covid RNA in the nostrils while they were already dying of something else, as opposed to actually dying from Covid as a primary or secondary cause of death. The reason this is important is that the former group causes a ‘pull forward’ effect which will artificially make it appear that suddenly Americans are not dying of other diseases any longer, either now or in the future. This effect as raw data, will serve to mislead.

Deductive Argument/Inference – an argument which uses premises and logic to eliminate all reasonable alternative considerations, or sets of possible contribution/consideration, through comparison to the strength of its primary assertions. The conclusion is contended to follow with logical necessity from the premises and reductions. Reductions can exist as either elimination of alternatives by hypothesis falsification research, or simply by set constrainment. For example, All men are mortal. Plato is a man. Therefore, Plato is mortal.

Demoveogenic Shift – a condition wherein amateurs of a science are proactive, well versed and investigate more depth/critical path, while in contrast the academic fellows of the discipline are habitually feckless, cocooned and privileged.

Dunning-Kruger Abuse – a form of ad hominem attack. Inappropriate application of the Dunning-Kruger fallacy in circumstances where it should not apply; instances where every person has a right, responsibility or qualification as a victim/stakeholder to make their voice heard, despite not being deemed a degree, competency or title holding expert in that field.

Embargo Hypothesis (Hξ) – an idea or dissent which must be squelched at all costs – even and especially unto the sacrifice of the integrity of science itself.

Epidemic Threshold – when a virus is ‘in season’ – the point at which fatalities from that virus exceed 7.2% of all fatalities in a given week.

epoché (ἐποχή, “suspension”) – a suspension of disposition. The suspended state of judgement exercised by a disciplined and objective mind.

Equivocation – the misleading use of a term with more than one meaning, sense, or use in professional context by glossing over which meaning is intended in the instance of usage, in order to mis-define, inappropriately include or exclude data in an argument.

Eristic Argument – an argument which is posed with the goal of winning and embarrassing an opposing arguer, as opposed to seeking clarity, value or common ground. Usually stems from the arguer’s past psychological injury, narcissism and combative habituation.

Ethical Skeptic’s Dictum of Rhetoric – what is posed in the rhetorical, can only be opposed with the rhetorical. One cannot answer a rhetorical question with objective reason and evidence.

Ethical Skeptic’s Conspiracy Razor – never ascribe to happenstance or incompetence, that which coincidentally, consistently and elegantly supports a preexisting agency. Never attribute to a conspiracy of millions, what can easily arise from a handful of the clever manipulating the ignorance of the millions.

Ethical Skeptic’s Razor – dangerous demands greater level of evidence than does crazy. Any claim which exposes a stakeholder to risk, ignorance or loss of value – regardless of how ordinary, virtuous or correct – demands extraordinary evidence. For a skeptic, a person who claims to represent science and uses ‘facts’ to lie, misrepresent, manipulate, and push politics – is worse than someone who claims there is a Galactic Federation of Aliens out there. Dangerous is much worse than crazy.

Ethical Skeptic’s Five Laws of Risk – in order of progression of application logic, five laws frame the ethics of risk in a social context:

  1. A system which imparts risk upon stakeholders, perpetually bears the burden of proof of any reasonable or implicit claim to have mitigated that risk.
  2. In absence of a reasonable accounting of risks, there is no such thing as a claim to virtue.
  3. A peer reviewing a risk strategy must also bear that risk them self.
  4. Stakeholders placed at risk, are peers in its review.
  5. An ignorance of risk or absence of risk strategy, is itself a risk strategy.

Experience Trumps Consilience/Consilience Trumps Heuristic – means that a consilience of having tested/failed at everything which does not work (deductive experience) is more powerful in its inference than is a conslience of suggestive (inductive ‘might be’) observations alone. However, consilience of inductive observations is stronger than the sophistication, reliability or academic correctness of any single given heuristic. A method of detecting the purely academic or poseur inside a topic. See Jamais l’a Fait.

Fallacy of Interest Conflict – a condition wherein a stakeholder bearing an opinion inside a legitimately plural scientific or public-impact disagreement, is falsely accused of bearing a conflict of interest for any form of desire to protect from harm or ruin their family, business, home, or those they hold dear. Ironically, the accusation of ‘conflict of interest’ in such circumstance, often itself constitutes a suppression of human rights (an action which can itself bear a conflict of interest).

False Positive Rate – a PCT RNA test of high sensitivity and tolerant specificity is designed for purposes of mercy – to not miss diagnostic cases. This is done specifically to minimize suffering from missed illness. However, it is well established that tests of such design may also produce false positive outcomes as part of their assay design. When a population is tested by PCR tests, and 99% of that population is well, then there will be a high number of false positives arising from the testing of that population, even and especially compared to false negatives. In addition, beside the issue of test design, is the reality that testing labs may suffer from laxity in procedure, kit contamination or employee error or malfeasance. All of these factors combine into what is known as a ‘False Positive Rate’ for a particular set of tests.

If we have a 1% rate of false positives, inside a population which is testing at 1% prevalence, in theory almost all of the positives being detected, are indeed false. As of late August the US was conducting on average about 680,000 tests per day. A 2.3% false positive rate would yield 15,640 false positives per day. The average positives detected during that same time was around 45,000 positives per day. Thus, potentially 35% of those reported positives in late August 2020, were indeed false. A study by Cohen and Kessel, updated and re-printed 18 August 2020, cited a measured median false positive rate of 2.3% for Covid RT-PCR testing. They confirmed the reality that “the likely sources of these false positives (contamination, human error) are more directly connected to laboratory practices and layouts than to which particular assay is used.”

False Tail (The Principle of) – the condition at the end of the 2020 Covid-19 pandemic where cases began to dwindle to the point where the content of false positives, number of persons conducting a second validation test after a positive first test, and RNA-shedding detections (both trivial and 12 week shadow of infection) – all three of these circumstances comprised a significant portion of daily reported cases by the states. In the chart to the right one can see the false tail depicted as the horizontal line progressing over time to the right.

The charts to the upper and lower right depict the Cohen-Kessel measured rate of false positives overlain on the positive case detects reported by the states and tallied at The Covid Tracking Project. This is a significant issue of concern, and citizens should be highly upset that this raw data, was passed to the media as ‘truth’. Raw data is never ‘truth’ even though it can be sold as fact (which in fact it is not). We do not use raw data when analyzing the flu each year. Instead we take raw lab reports and use them to project actual cases of the flu each season. We’ve gotten pretty good at this. This exhibits clearly the delineation between ‘data’ and ‘information’. We owed our population at risk ‘information’ and not mere ‘data’.

On this particular day in the upper right hand chart, it was estimated that 56% of the reported cases that day were from either

1. The 12 week shadow of RNA PCR detectability

2. Duplicate testing to confirm positive or detect recovery at 35%

3. The lower-band rate of false positives at 0.8 – 2.3% (avg = .0155, see Cohen-Kessel Study in ‘False Positive Rate’)

In additional example, two of these three false tail contributors are then deducted from the positive test result total in the second chart to the right, resulting in the intelligence that actual cases were flat, despite a 22% weekly increase in testing. This amplified caseload lent false support to the notion of keeping society partially shut down – a condition which was fatal to small and medium-sized businesses in the US, but not their conglomerate competitors.

Florida Risk of Death Case Study – a tiered-risk study by age, done on Covid-19 deaths in the state of Florida, which compared the frequency of cases and deaths by age tier for Covid-19 with the general nation’s actuarial risk of death by age. There were two right hand y-axes, the first indicated the risk by age from US actuarial tables (gathered from US Social Security Administration Actuarial Life Table Risk of Death by Age;, while the second calculated a comparable risk of death by age among those in Florida who had contracted Covid-19 and died at any time during that year of age (gathered from Florida Dept of Health Open Data;

What this served to show was, that those who were dying of Covid in Florida were dying much later in life than the average person in the US dies, by age tier for all causes. This demonstrated that the vast majority of those dying of Covid-19, were indeed dying only months earlier than they normally would have. This is still tragic, but constituted critical information we should have had early in the Covid response effort. This allows for the calculation of life-years lost comparatives between Covid-19 fatalities and fatalities from overreaction to Covid-19.

Gaussian Blindness (see medium fallax) – the tendency to characterize an entire population by both the mean (μ) of the population as well as a Normal Distribution profile or other easily applied distribution, as being descriptive of the whole body of a set of data. I’ve got my head in the oven, and my ass in the fridge, so I’m OK.

Gompertz Curve – is a compound mathematical model for a time series arrival form, named after Benjamin Gompertz (1779–1865). It is a sigmoid function which comprises and describes a normal distribution of one activity, blended with a Poisson or similar distribution of delay or bureaucratic effect – producing a unique form which hints at a blending of a natural arrival distribution, combined with a human arrival distribution (shopcraft).

The Great Repression – the period of severe depression economic fallout greater than that of the Great Depression, characterized by 38 million jobs (11.5%) and 500,000 or more lockdown lives lost and 6 million famine lives lost, which resulted from an over-reaction to and/or incompetent understanding of coronaviruses and Covid-19 in particular. Marketing disinformation as an academic, governmental or media authority, in order to compel despair, panic or compliance inside a population under duress of epidemic, war or economic collapse – these things are indistinguishable from war crimes. They are a violation of basic human rights, and as such constitute acts of class harm, scienter, racketeering and oppression.

Within the context of an impingement of human rights, incompetence and malice are indistinguishable.

Heat/Heat Map – a map showing where the hottest US Counties are located or showing where the greatest increase in cases or fatalities has been recently (the ‘heat’).

Herd Resistance vs Herd Immunity – a 70% herd immunity ‘threshold’ is overly simplistic to a systems engineering or market analytical mind. Societies are not a homogeneous soup of peer-equals. For the purpose of introducing heterogeneity into the estimation of herd immunity, we propose two modifications to the idea. First, because the principle carries an enormous amount of mathematical slack (single elements) and elasticity (overall), it cannot be called a ‘threshold’ – as that is simply a term of ignorance as to how systems work and function. Therefore, a resistance range-band is more appropriate for this type of analytical estimation. Second, this resistance range band must be developed through a heterogeneous schema of both society and the type of detection of infection. For instance ‘asymptomatic-connectors’ are the highest HRT-sensitivity group in the genera. They both conduct the highest number of transactions between groups of processors, and also feature the greatest ‘reach’ (horizontal = days of active transmission and vertical = the environment in which they transact, ship, building, prison, school etc.) in viral exchange rate.

Accordingly, in our estimation of the 14 – 18% herd (or community) resistance band, we used the following genera to stratify the math (note that ‘Connector’, ‘Processor’ and ‘Sink’ are simulation/modeling entities). This banding estimation turned out to be correct at the end of the pandemic season months later.

Connector – an individual who, because of their role in society performs a high number of transactions per day. Airline counter agents, cash handlers, janitorial staff, delivery people, medical staff, food workers & preparers, point of sale operators, etc. Overall, infections come from primarily this group.

Processor – an individual who interacts on a regular basis with a Connector population yet less often with other Processors, and with only a specific Sink population each day. They may not however do so with as many transactions and may spend significant time between Connector and Processor/Sink transactions, such that a virus either dies or is detected before a Sink or other Processor transaction is encountered. Mothers, fathers, laborers, teachers, doctors, staff workers, etc.

Sink – an individual who does not leave their residence as a normal part of their day, save for one to three trips per week at most. They rarely transact with Connectors, and most often do so with a specific small group of Processors. The elderly, work-at-home, household managers, infirm and disabled or ill, etc. Overall, deaths come primarily from this group.

Symptomatic – those cases who showed symptoms of Covid-19 as confirmed subclincally or higher, by a medical office or doctor.

Asymptomatic – those cases who detected positive for Covid-19 by antibody or PCR testing, however who do not recall being sick.

RNA-Dirty – those who detected positive for Covid-19 however never really were infected with the virus at all, rather simply carried dead fragments in their mucus or clothing. Also those who were infected up to 12 weeks earlier and still shed the dead RNA fragments.

False Positive – a false detection of Covid-19 by

– sensitivity error
– excessive CT threshold >35
– failures of process and lab design
– individual errors/contamination
– malfeasance/maliciousness

Through conducting a weighted average analysis of each of these schema groups, and assuming that the most exposed group, Connector-Symptomatic achieved a 70% herd-segment immunity, math can be constructed to show how an 18% resistance range can be expressed inside such a population. The elasticity around this range is enormous, with herd resistance ranging from 5% in farming communities, all the way to 70% in prisons, for instance.

Histogram – a graph showing the density/breakout of distribution or arrival of one variable against time or another variable.

Hope-Simpson Latitude Effect – Hope-Simpson was a doctor who in 1965 established that childhood chickenpox virus reactivates in adults as the painful condition called shingles. He also became noteworthy by documenting that his hometown in Southwest England came down with epidemic influenza at the same time every year as Prague, which shared the same latitude. While Hope-Simpson attributed this to sun exposure and Vitamin D, with regard to the flu – it is clear that coronaviruses have sharply marked seasonal patterns, which also differ by tropical and northern latitude bands.

This effect is shown by the US Covid-19 progression inside the chart to the right. The blue vertical column bars indicate case arrivals each day in the northern 37 states, while the orange vertical bar arrivals show the patterns in the southerly hot 50 border counties or hot 13 states. While part of this orange bar surge was attributable to cross-border activity and migrant labor/wet food supply chain, there still transpired a bonafide increase in cases along the US south border states. This surge in cases peaked on July 16th, 13 or 14 weeks after the north latitude peak (one quarter of the year). As indicated in the chart, each case peak adhered to Farr’s Law, and as well conformed to the arrival pattern of SARS-CoV-1 (2003) as documented in the Consensus Report on SARS-2003 by the World Health Organization.

Hospital Dwell Time – a factor derived by a multiple of the increase in the average length of stay (ALoS) of a hospital census and the rate at which nosocomial cases were added to hospital census, both after May 6. This was derived through a sampling of states who either reported admissions, or reported enough data from which admissions could be calculated, matched against those states progressions in hospital census. An example is shown to the right. Average length of stay is estimated from reported discharges and census for certain sample states (AZ FL GA). This drift between admissions and census is then projected into the future, and then is corrected if it begins to depress estimated case count below what the Grand Conslience Chart might support, as compared to CDC all cause and state reported fatalities. One such adjustment was done on August 20th, to correct for an over-aggressive ALoS calculation from 4/29 – 6/09. Nosocomial cases are estimated as an addition to this factor – and is watched as well for its net effect on normalized cases over time, as well as the matching of ICU census versus admissions. So nosocomial factoring and ALoS are as much outputs of the model as they are its contraints.

F(DT) = ((ALoS(0)/ALoS(t)) x (EV admissions/(Nosocomial + EV admissions))

Hospitalization Admissions/Census/Dwell Time Chart – admissions is those census being admitted to a hospital for a given malady (and not a nosocomial instance) on a given date. The chart on the right is derived from the hospital census figures given in ‘Hospitalized – Currently’ column of the Covid Tracking Project’s Daily State 4pm SitRep Report. The blue line on the chart reflects this count of individuals and matches to the blue numbers on the left hand side y-axis. The orange line however, is what is used in most TES models as it reflects an estimate of real admissions to hospitals for a Covid-Like Illness on each given day. This number is estimated by means of a factor relative to census based upon total Dwell Time calculations (see Dwell Time above and its associated formula).

Hospitalization Census – those people lying in beds in a hospital on a given date. When discharges and census are compared, the two can be used to estimate admissions by state, thereby allowing further for an estimate of Dwell Time drift between admissions and census – which we have sampled by 3 key states (AZ FL and GA).

Hospital ICU Census – ICU census is those people lying in intensive care unit beds in a hospital on a given date. The chart on the right is derived from the hospital ICU census figures given in ‘In ICU – Currently’ column of the Covid Tracking Project’s Daily State 4pm SitRep Report. The orange line on the chart reflects a 7-day moving average count of ICU individuals and matches to the orange numbers on the right hand side y-axis. When states or territories are added into and removed from these numbers, a normalization adjustment is made so as to now allow the number to whipsaw accordingly. This provides for a better leveling than even the 7-day moving average can stand-alone, and has resulted in good velocity indicators for this important statistic.

The blue line is a 7-day average of total fatalities calculated from the differential between daily fatalities by state, as reported in the ‘Deaths’ column of the Covid Tracking Project’s Daily State 4pm SitRep Report. The comparative between ICU Census and deaths allows the astute analyst to observe that reported deaths can contain legacy data up to 12 weeks old in some cases, and most often in excess of 3 weeks old. This must be corroborated however by examining fatality epicurve or aging reports by those states who disclose such information. Through the analysis horizon, these two figures have held in common agreement. This is key in estimating Legacy Death Laundering (LDL2) in the Grand Consilience Chart.

Hot 50 Counties – the top 50 counties in terms of 7-day most recent growth in cases or fatalities during the July 4 – August 15 period of the pandemic tail. Typically ascertained by an average of the last three days’ reports divided by the same 3 days from last week (to avoid weekend effect), or by comparing an average of this week to the same average from last week. The Hot 50 County tracking data was scraped from this source each day of tracking: USAFacts: Coronavirus Locations: COVID-19 Map by County and State; What this chart served to show was twofold:

First, this latter surge in cases was not a ‘second wave’ as much as it was a sympathetic overflow from southerly latitude patterns of the same virus. Hence the 50 county concentration of the surge’s hottest case growth, shown as of 17 July in the map to the right. These were border or border-trade counties handling the wet food supply chain into the US (cut flowers, rendered pork-chicken-beef, vegetables and fruits). This latitudinal effect is called a Hope-Simpson subtropical latitude effect. One key weakness of our preparation for any form of pandemic, was our complete ignorance around this effect. This should have been known before we decided policy. Most predictive models for the pandemic were merely regression models, with high and low banding. None of them predicted the distinct Farr’s Law shape of this latitudinal surge.

Second, this surge in cases formed a peak around July 16th, which was immediately called by The Ethical Skeptic by means of the charts to the lower right, but took a full 3 weeks to be acknowledged by official channels and media, most of whom still did not even mention the tail off by early September. In the chart to the right, the 42-day growth to a peak was tracked for the hottest 50 counties in the US, regardless of which counties composed that grouping each day. The curve continued an upward progression until it lost that momentum on 16 July and never recovered it. The Ethical Skeptic was watching for a confirmatory rise in fatalities 28 days after the initial rise in cases – and indeed a mild rise in fatalities formed, confirming that this reported rise in cases was about 37% real, 63% salting through new practices of case detection. This salting was filtered out for the Case Arrival Curve Chart (see Salting/Juking of Reported Cases).

iCFR (often ‘IFR’ for short) – infectious case-fatality rate (risk). The total ‘died of’ and ‘died with’ fatalities from a given pathogen, divided by the estimated total population which was infected by that pathogen. Has nothing to do with symptomatic rates.

In Extremis – a condition of rising or extreme danger wherein a decision which is dependent upon an outcome of scientific study, must be made well in advance of any reasonable opportunity for peer review and/or consensus to be developed. This is one of the reasons why science does not dictate governance, but rather may only advise it. Science must ever operate inside the public trust, especially if that trust requires expertise from multiple disciplines.

Indigo Point (Inflection Point) – the early-on inflection point in a time series, system or set of events which is the compliment or impetus behind a Tau Point Inflection or ‘tipping point’. The indigo point is that event or mechanism which is manipulated early in a process, often surreptitiously and often representing an insignificant or underappreciated aspect of the system in question, which will alter/tip subsequent events towards a specific final outcome. It is the magician’s unremarkable sleeve.

ingens vanitatum Argument – citing a great deal of expert irrelevance. A posing of ‘fact’ or ‘evidence’ framed inside an appeal to expertise, which is correct and relevant information at face value; however which serves to dis-inform as to the nature of the argument being vetted or the critical evidence or question being asked.

Inchoate Action – a set of activity or a permissive argument which is enacted or proffered by a celebrity or power wielding sskeptic, which prepares, implies, excuses or incites their sycophancy to commit acts of harm against those who have been identified as the enemy, anti-science, credulous or ‘deniers’. Usually crafted is such a fashion as to provide a deniability of linkage to the celebrity or inchoate activating entity.

Inductive Argument/Inference – an argument in which if the predicates are true and the relative quality or structure of logic is sound, then it is more probable that the conclusion will also be true. The conclusion therefore does not follow with logical necessity from the predicates, but rather with an increase in likelihood, hopefully converging to certainty. For example, every time we measure the speed of light in various media, it asymptotes to 3 × 108 m/s. Therefore, the speed of light in a medium-less vacuum is 3 × 108 m/s. Inductive arguments usually proceed from specific instances to the more general. In science, one usually proceeds inductively from data to laws to theories, hence induction is the foundation of much of science. Induction is typically taken to mean testing a proposition on a sample, or testing an idea on an established predicate, either because it would be impractical or impossible to do otherwise.

Jamais l’a Fait – Never been there. Never done that. Someone pretending to the role of designer, manager or policy maker – when in fact they have never actually done the thing they are pretending to legislate, decide upon or design. A skeptic who teaches skepticism, but has never made a scientific discovery, nor produced an original thought for themself. Interest rate policy bureaucrats who have never themselves borrowed money to start a business nor been involved in anything but banks and policymaking. User manuals done by third parties, tax laws crafted by people who disfavor people unlike themselves more heavily, hotel rooms designed by people who do not travel much, cars designed by people who have never used bluetooth or a mobile device, etc.

Lag/Delay/Lag Curve – a delay is the period of time between when something actually occurs and when it is reported or cataloged as data. Lag is the mathematical description of how those instances of delay perform over time, on the part of one organization or measuring mechanism. A lag curve is the mathematical factoring which aids analysis by compensating for this characteristic set of delays/lag. An example CDC characteristic lag curve is shown below:

Laundering (of Legacy Cases and Fatalities) – partly a term from data management and partly coined for the Covid-19 response. Legacy data is any form of old data which involves work in transcribing into a new system, approach or context. Laundering is the process of removing the old undesirable context for an item transacted in a market (information is a market), and fabricating a new beneficial context of use – free of the old one. Inside the context of a pandemic, this constitutes a method of misrepresentation to at-risk stakeholders. When conducted inside the context of a population under risk, or when exploited by media to incite panic or despair, it is a human rights crime as well.

Legacy Data Laundering (LDL1) – state departments of health may unilaterally, or through direction by higher agency or political intermediaries, choose to report cases or fatalities over 7-days in age, as if they occurred on the day of reasonable reporting. In this manner, a sufficient amount of old data or newly converted old data (cases or fatalities), can be exploited to craft the appearance a false trend, rise or level.

Lockdown Death Laundering (LDL2) – state departments of health may unilaterally, or through direction by higher agency or political intermediaries, choose to designate past CDC excess all cause mortality deaths which were not attributed to Covid-19 on a death certificate, as ‘suspected’ or ‘probable’ Covid-19 deaths. In this manner, deaths from lockdown, access and despair can be attributed either as direct Covid deaths or ‘death from Covid upheaval’ – and be reported at a later date as a current Covid-19 fatality. A sufficient amount of these fatality conversions can be exploited to craft the appearance of a false trend, rise or level of fatality.

Law of Large Numbers – a fallacy wherein an arguer does not perceive that a perceptibly large effect on a small population might serve to produce rather small numbers of outcomes, while a very small or subtle effect on a very large population, may well serve to produce surprisingly large numbers in outcome.

Lemming Cycle (of Continuous Impairment) – both a parody and satire extended from an antithetical version of the operations model called the Deming Cycle of Continuous Improvement (named after its developer, Dr. William Edwards Deming) – the parody ‘Lemming Cycle of Continuous Impairment’ is also a satirical commentary upon how panicked state level government officials served to foment continued panic and despair around Covid-19. Also derived from the allegory of fictitious lemmings who run off a mythical cliff en masse, in their hysteria to achieve a given inductively derived or panic-fueled goal. The cycle wherein panic over perceived cases of Covid-19, drove demands inside certain state government levels for more testing and track-and-trace team funding, which resulted in increased PCR tests run and false/3-month-old positives being generated, which resulted in ever higher positive case-detects being reported as raw data by the states, which resulted in more panic/despair. Incompetence. Forgetting the data grooming methods employed with annual flu analytics and opting instead to simply report raw data – a scientific error.

The chart to the right depicts how The Lemming Cycle (boosted as well by false positive PCR testing results which were left unchallenged), fueled a feedback cycle of panic inside the media and administrative government decision-makers, regarding Covid-19.

Linear Affirmation Bias – a primarily inductive methodology of deriving inference in which the researcher starts in advance with a premature question or assumed answer they are looking for. Thereafter, observations are made. Affirmation is a process which involves only positive confirmations of an a priori assumption or goal. Accordingly, under this method of deriving inference, observations are classified into three buckets:

  1. Affirming
  2. In need of reinterpretation
  3. Dismissed because they are not ‘simple’ (conforming to the affirmation underway).

Lockdown – the term ‘lockdown’ is a twitter compression – a concise way of identifying a very complex set of intent and action, so that people understand what is being described in contrast to normal societal function. Very few nations actually locked-down, and when they did, it was not for very long. In the context used within this analysis, a lockdown is any coercion or social mandate which will serve to reduce moderate-sized businesses revenue by more than 25%, by more than three weeks. It is also any change in availability of medical services which serves to increase deaths which otherwise would not have occurred, by more than 2% of all-cause deaths in any given week.

Lockdown/Great Repression Fatality – a fatality reported by a state department of health and cataloged by the CDC in which the person’s death was caused by lack of access to medical services (diabetes, heart disease, stroke, other illnesses, etc.), lack of adequate diagnosis (cancer, etc.) or Suicide Addiction Abandonment & Abuse.

Logical Calculus – the quality of an argument or the ability of its objective features to commensurately lend support to its inference in terms of clarity, salience, soundness, critical path and depth.

MMWR Week – the US Centers for Disease Control ‘Morbidity and Mortality Report’ fiscal week of accounting for all US fatalities each year. For 2020, week 1 of the MMWR was the week ending January 4th 2020.

melochi kupets (Russian: мелочи купец) – trivia merchant. One who feigns competence or intimidates curious outsiders through display of detailed mundane knowledge of the industry in which they operate. One who cannot differentiate the distinction between a peripheral or irrelevant detail and a critical path element or principle.

missam singulia shortfall in scientific study wherein two factors are evaluated by non equivalent statistical means. For instance, risk which is evaluated by individual measures, compared to benefit which is evaluated as a function of the whole – at the ignorance of risk as a whole. Conversely, risk being measured as an effect on the whole, while benefit is only evaluated in terms of how it benefits the individual or a single person.

Mobsensus – the inferential will of media, club, mafia, cabal or cartel – in which a specific conclusion is enforced upon the rest of society, by means of threat, violence, social excoriation or professional penalty; further then the boasting of or posing such insistence as ‘scientific consensus’.

NCHS – National Center for Health Statistics.

NiCFR – net infectious case-fatality rate (risk). The total ‘died of’ and ‘died with’ fatalities from a given pathogen, minus those who died as a result of over-reaction or ignorance around that pathogen, divided by the estimated total population which was infected by that pathogen. Has nothing to do with symptomatic rates.

Nelsonian Knowledge – Nelsonian knowledge takes three forms

1. a meticulous attentiveness to and absence of, that which one should ‘not know’,
2. an inferential method of avoiding such knowledge, and finally as well,
3. that misleading knowledge or activity which is used as a substitute in place of actual knowledge (organic untruth or disinformation).

The former (#1) is taken to actually be known on the part of a poseur. It is dishonest for a man deliberately to shut his eyes to principles/intelligence which he would prefer not to know. If he does so, he is taken to have actual knowledge of the facts to which he shut his eyes. Such knowledge has been described as ‘Nelsonian knowledge’, meaning knowledge which is attributed to a person as a consequence of his ‘willful blindness’ or (as American legal analysts describe it) ‘contrived ignorance’.

Nosocomial – an illness which is contracted or occurs while one is a patient in a hospital, having been admitted for a completely separate condition.

nulla infantis – a pseudo-argument, sometimes cleverly disguised or hidden inside pleonasm, which basically is the equivalent of saying ‘nuh-uhhh’…  Latin for child’s ‘no’. Usually followed by an appeal to have the opponent shut-up or be silenced in some manner.

Ockham’s Razor – “Pluralitas non est ponenda sine neccesitate” or “Plurality should not be posited without necessity.” The words are those of the medieval English philosopher and Franciscan monk William of Ockham (ca. 1287-1347). This principle simply means that, until we have enough evidence to compel us, science should not consider outsider theories. But it also means that once there exists a sufficient threshold of evidence to warrant attention (plurality), then science should seek to address the veracity of a counter claim. SSkeptics bristle at the threat of this logic and have sought to replace this tenet with their shade-change version, “Occam’s Razor.”

Omega Hypothesis (HΩ) – the argument which is foisted to end all argument, period. A conclusion promoted under such an insistent guise of virtue or importance, that protecting it has become imperative over even the integrity of science itself.

Original Sin – the justification which was used to explain slavery worldwide, until the United States and non-religious minds changed that thinking in the 1800’s. The idea that a race, culture or skin color bears an inferiority or debt to another one, and that therefore it is the right of the superior/debt-holding culture to abuse the former in tyrannical rule, economy, taxation, representation and servitude. In absence of the power to enact this, the original sin stands as justification for violence against the group condemned under the original sin (quo facto malo).

Outbreak (see Epidemic Threshold) – when an epidemic threshold is attained in a given set of counties/cities, yet is not spreading geographically as did its original outbreak, or as would a new virus/season.

Pace Daily Cases & Fatalities Chart (Grand Consilience Chart) – the chart which compares extrapolated (estimated) actual infections of Covid-19, with both CDC excess all-cause and daily state SitRep-reported fatalities – to detect when one or more of these elements begins to drift out of sync or no longer make sense. This also allows for a reasonable estimate of infectious case-fatality rate (iCFR). This approach allowed TES to estimate an accurate iCFR of 0.26% weeks before the CDC published their ‘Best Scenario’ iCFR of the same magnitude. It brings together multiple results and evaluates their congruence for a moderate level of accountability in inference – superior to induction, but not yet fully deduction or falsification.

Element 1 – Extrapolated Total Infectious Cases (Blue Vertical Columns) – this is the estimated total infections among US citizens in all 50 states and 6 territories. It is scaled up by a fixed ratio of actual cases to detected cases (see Daily Case Arrivals Chart) based upon a moving index of seroprevalence across 11 studies which sampled this index in a variety of states and countries (see Seroprevalence Antibody IgG/IgM Studies Chart). The arrival form is from the Daily Case Arrivals Chart, while the numeric magnitude is calculated from the 6.9% seroprevalence sampled in the Seroprevalence Chart. The rest is simply a ratio calculation, to project actual US infectious cases.

Element 2 – CDC Excess All-Cause Fatalities (Yellow Line) – excess all cause fatalities (from the CDC Lag Adjusted CDC All Cause Fatalities vs CV19 Fatality by MMWR Week Chart) are assembled into a yellow line and superimposed over both the state SitRep-reported deaths each day, and the estimated daily infectious case arrivals identified in Element 1 above. These three curves should move in a given ratio throughout the horizon, unless an exception has occurred such as Legacy Data Laundering LDL-1 or Lockdown Death Laundering LDL-2.

Element 3 – Daily State SitRep Reported Fatalities (Orange Vertical Columns) – these are the deaths reported by the states each day in their 4:00pm SitReps and as reported by The Covid Tracking Project. Simple transcription of the number reported, is employed for this chart element. The degree of Lockdown Death Laundering (LDL-2) can be ascertained by comparing the height of each day’s orange column, versus the yellow CDC excess all-cause deaths line. Since that yellow line is both adjusted for documented CDC lag, and is matched consistently to the blue columns (case arrivals) it is considered a much higher confidence figure on actual deaths each day.

EOS (End of Season) – this is the threshold which indicates where deaths were first seen as beginning an accelerated increase for Covid-19 in late March 2020, and as well a percentage of first peak deaths/illnesses (not detections), which demarks the end of the annual influenza season had these statistics been seen inside a pneumonia and influenza incidence curve in recent years. There is argument that each of the Covid surge peaks are actually different ‘seasons’ under a Hope-Simpson perspective. This demarcation however does not constitute a forecast, nor a declaration as to the end of Covid-19 as a pandemic. It only offers another reference perspective to consider, especially when each distinct peak is compared to county-level geographic heat maps. This makes the data much more informative than simply raw PCR detection counts – which are misinformative or terror motivated.

Paradox of Virtue (Covid-19) – if leadership had not shut down, all 150 k Covid deaths would be blamed on that mistake. This flawed virtue signal argument, then forces us to conduct activity which is 5 – 10 x more damaging. Like a bad SAW movie. We are exploited by evil.

Pluralistic Ignorance – most often, a situation in which a majority of scientists and researchers privately reject a norm, but incorrectly assume that most other scientists and researchers accept it, often because of a misleading portrayal of consensus by agenda carrying social skeptics. Therefore they choose to go along with something with which they privately dissent or are neutral.

Plurality – adding complexity to an argument. Introducing for active consideration, more than one idea, construct or theory attempting to explain a set of data, information or intelligence. Also, the adding of features or special pleading to an existing explanation, in order to adapt it to emerging data, information or intelligence – or in an attempt to preserve the explanation from being eliminated through falsification.

Pneumonia, Influenza, Covid-19/Lockdown (PIC) Fatalities – a scheme on the part of second tier government officials, including the US Centers for Disease Control, to conflate the upcoming 2020 influenza and pneumonia season as being one-in-the-same as the tail of the Covid-19 outbreak. By this mechanism, the dwindling 1.6% excess Covid deaths characteristic of the October 2020 timeframe, which did not classify as ‘epidemic level’ (5.7%), could be mixed equivocally with annual P&I deaths and were gain-boosted artificially back to the 7.0%+ range (CDC announced ‘7.2%’ on October 16th, 2020). In this manner, oppressive lockdown mandates could remain in place because of the epistemic doubt as to whether or not Covid-19 was beyond its end of season. Moreover, economic slow-downs and shelter in place orders could be extended until April 2020, when the flu season naturally ends.

This scheme was identified in the data released by the CDC, by The Ethical Skeptic on October 16th, and was cited as a ‘crime against humanity’.

Precautionary Principle Burden of Proof – when money or human rights are at stake, a claim to risk of exposure to corruption does not bear the burden of proof – the entity managing that money or human right (eg. voting or reporting on a pandemic risk) bears the active and ongoing burden of proof that their system bears 99.9997% integrity.

Principle of Peerhood – (‘peer’ is a word derived from nobility ranking and matching) – I shall not tell an epidemiologist his business, unless he infers from his work that I should necessarily undertake a harm or ruin – at that point, I am now a peer. The stakeholder placed at risk is the peer review.

Probative – a measure of a quality of a data or observation set such that it serves to inform a given critical path of reason or investigation, as opposed to generally informing about peripheral or circumstantial issues or not informing at all. This measure of the quality of data is orthogonal to the issue of the reliability of such data or observations. Observations which are salient to the question being asked tend to bear greater probative potential than do ignoratio elenchi observations. Observations which allow for deductive inference tend to bear greater probative potential than do inductive ones. Inductive observations are superior to abductive inferences, etc. The goal of systems intelligence is to assemble probative observations and derive perspectives/questions which improve their reliability, not assemble reliable information and attempt to make it therefore then probative (armchair intelligence or streetlight effect).

Pseudo-Theory – a catch-all explanation or critique, construct, belief or overarching idea which explains anything, everything and nothing – all at the same time. That which explains everything, likely explains nothing.

Pull-Forward Effect/Fatality – also see ‘Death from/Death with Covid’. The group of people who were already in the process of dying (age and/or illness related) – who died 1 to 40 weeks earlier than they would have, because they caught Covid-19. This shows as a dip in the death rate after Covid is over. The pull-forward effect can be seen in the chart to the right. In this case, two significant segments of time exist. First is the period in which Covid directly or indirectly precipitated an increase in deaths attributable to known natural causes (9,079 fatalities overage versus normal). Second is the period thereafter where these additional deaths served to also create a hole in the death rate starting 8 weeks later and onward (13,429 fatality deficit versus normal). In this instance, Covid-19 impacted death rates by ‘pulling forward’ deaths which would have occurred in July and August, and forced those to occur in April and May. As of the publication of this chart to the right, it is estimated that we have experienced possibly 40-45% of the pull-forward deficit. The full pull-forward deficit will not be known until as far out as May of 2021. This data was obtained from pivot analysis of the Big 14 mortality database scraped from the National Center for Health Statistics; Weekly Deaths by State and Cause of Death; 29 Aug 2020;

quo facto malo – Latin for ‘having done this evil’. When a person desires to do evil to another, they will manufacture or fantasize in their mind, offenses their target has committed, which serve to therefore justify their actions; harm which they had conducted or intended to conduct from very beginning, but were simply waiting for the right excuse to blame it upon. See ‘original sin’.

Reason (The Three Forms)

Abductive Reason (Diagnostic Inference) – a form of precedent based inference which starts with an observation then seeks to find the simplest or most likely explanation. In abductive reasoning, unlike in deductive reasoning, the premises do not guarantee the conclusion. One can understand abductive reasoning as inference to the best known explanation.

Inductive Reason (Logical Inference) – is reasoning in which the premises are viewed as supplying strong evidence for the truth of the conclusion. While the conclusion of a deductive argument is certain, the truth of the conclusion of an inductive argument may be probable, based upon the evidence given.

Deductive Reason (Reductive Inference) – is the process of reasoning from one or more statements (premises) to reach a logically certain conclusion. This includes the instance where the elimination of alternatives (negative premises) forces one to conclude the only remaining answer.

RNA/Virus Detection Half-Life – the period after which half of those who were infected at one time, can no longer present detectable dead or inactive Covid-19 RNA. The half-life period has been evaluated to be as much as 7 weeks. The entire period of detectability is 14 weeks. This noise in data collection was exploited by nefarious political and power hungry forces during the pandemic.

Salting/Juking Reported Cases – one or more of a variety of methods of boosting reported cases of Covid-19 to make the pandemic seem hotter, growing when actually in decline or larger than is its reality. Includes methods such as

legacy data laundering,
backlog stuffing,
AB testing results mixed with PCR testing,
focus on hot spots,
posting track and trace %-pos results only,
testing prisons care facilities and factories only,
temperature screening selected testing,
report of ‘suspected’ cases,
non-reporting of negative tests,
delay or lag exploitation,
multiple tests on one person,
cross-border cases,
hospital comprehensive screening,
nosocomial cases,
paying people who test positive,
batch swab testing followed by individual testing, etc.

In the chart to the right, the gentle rise in percent positive at the right hand side of the blue trend line was 37% real cases in our 50 hottest counties composing the July 16th surge, while 63% of that rise was crafted through salting/juking of reported cases.

sCFR (often ‘CFR’ for short) – symptomatic case-fatality rate (risk). The total ‘died of’ and ‘died with’ fatalities from a given pathogen, divided by the estimated total population which presented symptoms of that pathogen. Has nothing to do with infectious rates.

Semantic vs Logical Truth – a semantic truth only applies some of the time or inside a specific context (a semantic principle/doctrine). A logical truth (or law) applies to all conditions of the domain under discussion. A form of equivocation involves exploiting a semantic truth as a logical truth – through sleight-of-hand, changing the context of its employment without the soundness of first addressing whether or not the principle actually applies inside that new context.

Scienter – is a legal term that refers to intent or knowledge of wrongdoing while in the act of executing it. An offending party then has knowledge of the ‘wrongness’ of their dealings, methods or data, but choose to turn a blind eye to the issue, commensurate to executing or employing such dealings, methods or data. This is typically coupled with a failure to follow up on the impacts of the action taken, a failure of science called ignoro eventum.

Seroprevalence/Seroprevalence Escalation Chart – the prevalence (usually expressed as a percentage) of Covid IgG and/or IgM antibodies in the general population of a compartment, indicative of how extensive the spread of Covid-19 (or possibly an unknown similar and previous virus) has been at some point in the past (4 weeks for IgG and 1 week for IgM). It is important to remember that this number is always growing during a pandemic or outbreak. In the chart to the right, a strike point for seroprevalence was established using a weighted average from 11 key studies up until that date, which bore applicability to United States’ demographics in which Covid-19 was prevalent. An estimate was made of a 6.9% seroprevalence (95% CCI [5.01, 8.79]) for the nation as a whole, as of April 20th. This was then grown over time under a number of escalation scenarios, to watch for consilience with other models and observations. Current seroprevalence extrapolations based upon this, and which match CDC iCFR and TES models well, place total seroprevalence at 16.3% as of the end of August 2020. Most of these cases were asymptomatic.

Shopcraft – traits, arrival forms and distributions of data which exhibit characteristics of having been produced by a human organization, policy or mechanism. A result which is touted to be natural, random or unconstrained, however which features patterns or mathematics which indicate human intervention is at play inside its dynamics. A method of detecting agency, and not mere bias, inside a system.

Slapping the Grizzly/Bear – if someone implies to you that they know exactly the outcome of slapping a grizzly bear to get it to go away – they themselves are more dangerous than a virus by far – no matter how many risk PhD’s they may hold. The act of becoming robust to a constrained small-risk of harm/death bearing sound epistemic precedent (a virus with 4 months of observation and multiple precedents) – while exposing to an unconstrained large-risk of ruin/death, derived from our actions (an economic Great Repression), which bears no epistemic precedent …is like slapping a grizzly bear to compel it to go away.

Stakeholder Ethics – a principle or condition wherein those who bear the negative impact of a decision can hold those who make that decision, accountable. Further then may dissent, and reverse that decision or remove the decision maker, even if they claim to be an ‘expert’. A claim to science is not a free pass to tyranny.

Statistician – one who collects into a database salient and valid raw data and further then pulls selected features from that raw data to highlight to users, observers or participants therein.

SupraLag – that CDC lag in posting of actual deaths (shortfall in all-cause death count) by date in the MMWR weekly data, which exceeded by a large amount, the typical lag which the CDC had historically exhibited for that same week (current week minus x MMWR weeks). Ideally if both the CDC and analyst tracking them are performing well in their duties, SupraLag should be minimal.

Symmetry – the innate commonality of two independent objects to bear the same form or to look alike as a result of causal and not accidental inputs or constraints.

Systems Intelligence – in a system, it is not statistics nor the precision of any single measurement which provides for analytical confidence. Rather it is the consilient agreement of dozens of independently derived indicators in concert, which provides for intelligence. One does not drive a car by means of tape measure, physics text and calculator – as pretend precision, credential, correctness and reliability (the perfect) are the catastrophic enemy of the effective. Rather, a system is grasped through inductive consilience inside a neural nexus of simultaneous probative inputs and relationships. Experience also trumps such consilience, while consilience trumps any single heuristic. The process of collecting raw data and framing or denaturing that raw data such that it begins to offer information. This information is further then transmuted into intelligence: feedback, ergodicity, arrival distribution, confidence, constraint, sensitivity, consilience, discrete/continuous symmetry/asymmetry and input-mechanism-causal hypotheses. The goal is to increase the reliability of inference derived from probative data, not attempt to make reliable data then also probative (also called fake skepticism, ‘torturing the data’ or ‘streetlight effect’).

Wittgenstein Error (Contextual) – employment of words in such as fashion as to craft rhetoric, in the form of persuasive or semantic abuse, by means of shift in word or concept definition by emphasis, modifier, employment or context.

Wittgenstein Error (Descriptive) – the contention or assumption that science has no evidence for or ability to measure a proposition or contention, when in fact it is only a flawed crafting of language and definition, limitation of language itself or lack of a cogent question or (willful) ignorance on the part of the participants which has limited science and not in reality science’s domain of observability.

Describable: I cannot observe it because I refuse to describe it.

Corruptible: Science cannot observe it because I have crafted language and definition so as to preclude its description.

Existential Embargo:  By embargoing a topical context (language) I favor my preferred ones through means of inverse negation.

Yule-Simpson Paradox – a trend appears in different groups of data can be manipulated to disappear or reverse (see Effect Inversion) when these groups are combined.

The Ethical Skeptic, “The Definitive Guide to Ethical Skeptic’s (TES/ES) Coronavirus SARS-CoV-2 (2019) Analysis”; The Ethical Skeptic, WordPress, 9 Aug 2020; Web,

August 9, 2020 - Posted by | Ethical Skepticism |

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Have been following you on Twitter since May ‘20 and appreciate this guide.
If you could expound on your December ‘20, February ‘21 tweets re flu vaccines, ALZ/dementia upswing, “something’s not right here”, Lewy Barr Philippine research, that would be helpful.
Also, any wisdom regarding the number of routine childhood vaccines (including flu when discharged from the hospital after birth, and soon, I fear, covid) would be also be appreciated. Thank you.


TES, what’s your opinion on the the (mRNA) vaccines?
Even young people I know will be happy to get anything that is “approved” but I don’t see the cost-benefit being close to there for anyone who isn’t old or otherwise high risk. Especially I worry about young healthy people and potential long-term side-effects from such a new mechanism of vaccine.
P.S. Really appreciate this blog and your COVID work, both have shaped a lot of how I’ve thought this year.


Would such studies be able to be done in an accelerated timeframe as the one we’re in?
My thinking is that they would have to take years if not decades to truly assess (e.g., auto-immune) impacts over time. So it goes to the practical question of should healthy young adults get a vaccine next year or not given all this uncertainty.

Tommy Schopenhauer

The language of this time is what worries me most – in fact, sometimes it is outright disturbing. It is hard to think rationally in this climate, with the constant feeling of (maybe?) being cheated on by some (false?) semblance of rationality …
Reality nowadays closely resembles an X-Files story arc, if you ask me – and that is just a popular culture reference …


I like what you write. May I recommend a writing method to simplify, clarify, and improve your written content? Omit the unnecessary use of “in order” in all instances of “in order to…” to read just “to…” as writing best practice. For reference, see

[…] Here is an item from August that comes at the issue in a different way but has similar findings. That link goes to an anonymous […]

ray j wallin

I have read through this page a number of times over the past few weeks to better grasp your methods and my understanding of this COVID-19 debacle has increased substantially. Thank you especially for your quite readable graphs. I agree that, in too many ways, we have handled and continue to handle COVID incorrectly, but while most analysis focuses on what is being done incorrectly, little is said about a proper response to a virus. Briefly, how you think the USA (or any other country) should have responded to COVID in Feb/March of 2020? Keep everything open? Protect the elderly?… Read more »

David Grawrock

This is fantastic. Now i’m going to read your other posts. One comment, and hopefully you will take this as constructive criticism, why in the world don’t you use the Oxford comma? :)

Adrian Ward

I am having a bit of trouble understanding your figures. You have Died ‘Of’ numbers at 138,148 and Died ‘With at 57,034. Those 2 added together add up to 195,182 – which you list as CDC All-Causes Excess Total, but then you also have ‘Lockdown Fatalities at 50,094. Shouldn’t the All-Causes Total be the 195,182 + 50,094.


I didn’t notice that ‘5 million famine deaths’ number among this list on my first read. Thinking about it, it is just crazy. The same people who went into a frenzy at Trump for ‘locking down too late'(Of course, they weren’t screaming at him to do it until he’d already cut off china travel but whatever), and at the tens of thousands of corona deaths, are pretty silent about literally MILLIONS of people now being dead…I’m reading that correctly, right? 5 million people worldwide already died of famine as a result of the lockdowns affecting world economies? Although I’m assuming… Read more »


Great stuff, it really helped me learn! My favorites were the Great Repression term and Law of Large Numbers. My own little take on that Law of Large Numbers is that there is one key mistake which makes it all possible. People making these hysteria claims of doom should explain what threshold of death counts should occur for it to count as doom and use other examples of pandemics that are deadly. They need to have a clear set of standards rather than keeping all the criteria that make something a pandemic a mystery. That way they can be held… Read more »


The Gaussian Blindness definition is GOLD: “I’ve got my head in the oven, and my ass in the fridge, so I’m OK.” :-)

mark lavigueur

I hope this isn’t a trap to catch out folks trying to think without state supervision. Thanks for all the work you put in on this. You’ve corrected a few errors in my own thought. I’m grateful.


The CDC database is still inaccessible, and the CDC site is wrong. We are not at “7.6% PIC”, but rather 4% Covid – 5 weeks now below the epidemic threshold of 5.8% for MMWR Week 42.

We are reducing unreported Covid Reserve again for the 3rd week straight – encouraging…

You wrote the above on twitter. How are you getting from 7.6% PIC to 4%. I am hoping to learn. I would very much like to believe we are out of pandemic/epidemic conditions.


Completely makes sense.

I am hoping that we are below the level where this is a pandemic. Maybe the graph is complex. Can you do a graph that just shows two data points? The actual Covid deaths and the other?


You do amazing work on this by the way. Sorry.


Can you explain how the 5.8% floor number to qualify as a pandemic is arrived at?


Teach me.

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