The Disease Models Were Tested and Failed, Massively
By Phillip W. Magness | AIER | March 19, 2021
One year ago this week the world embraced a lockdown strategy premised on the epidemiology modeling of Imperial College-London (ICL). In a March 16, 2020 report by physicist and computer modeler Neil Ferguson, the ICL team predicted catastrophic death tolls in the United Kingdom and United States unless both countries adopted an aggressive policy response of mandating social distancing, school and business closures, and ultimately sheltering in place.
Ferguson’s model presented a range of scenarios under increasingly restrictive nonpharmaceutical interventions (NPIs). Under its “worst case” or “do nothing” model 2.2 million Americans would die, as would 510,000 people in Great Britain, with the peak daily death rate hitting somewhere around late May or June. At the same time, the ICL team promised salvation from the coronavirus if only governments would listen to and adopt its technocratic recommendations. Time was of the essence to act, so President Donald Trump and UK Prime Minister Boris Johnson both listened. And so began the first year of “two weeks to flatten the curve.”
It took a little over a month before we saw conclusive evidence that something was greatly amiss with the ICL model’s underlying assumptions. A team of researchers from Uppsala University in Sweden adapted Ferguson’s work to their country and ran the projections, getting similarly catastrophic results. Over 90,000 people would die by summer from Covid-19 if Sweden did not enter immediate lockdown. Sweden never locked down though. By May it was clear that the Uppsala adaptation of ICL’s model was off by an order of magnitude. A year later, Sweden has fared no worse than the average European lockdown country, and significantly better than the UK, which acted on Ferguson’s advice.
Pressed on this unexpected result, ICL tried to distance itself from the Swedish adaptation of its model in May. The records from the March 21st supercomputer run of the Uppsala team’s projections belie that assertion, linking directly to Ferguson’s March 16th report as the framework for its modeling design. But no matter – the ICL team’s own publications would soon succumb to a real-time testing against actual data.
A second ICL report, attempting to model the reopening of the United States from lockdowns, wildly exaggerated the death tolls that were expected to follow. By July, this model too had failed to even minimally correspond to observed reality. ICL attempted to save face by publishing an absurd exercise in circular reasoning in the journal Nature where they invoked the unrealized projections of their own model to supposedly “prove” multiple millions of lives had been saved by the lockdowns. That study soon failed basic robustness checks when the ICL team’s suite of models were applied to different geographies.
Another team of Swedish researchers then noticed oddities in the ICL team’s coding, suggesting they had modified a key line to bring data from their own comparative analysis of Sweden into sync with other European countries under lockdown after the models did not align. A published derivative of this discovery showed that ICL’s own attempts to validate the effectiveness of its lockdown strategies does not withstand empirical scrutiny.
Finally, in November, another team of researchers from the United States compared a related ICL team model for a broader swath of countries against five other international models of the pandemic, examining the performance of each against observed deaths. Their results contain a stunning indictment: “The Imperial model had larger errors, about 5-fold higher than other models by six weeks. This appears to be largely driven by the aforementioned tendency to overestimate mortality.”
The verdict is in. Imperial College’s Covid-19 modeling has an abysmal track record – a characteristic it unfortunately shares with Ferguson’s prior attempts to model mad cow disease, swine flu, avian flu, and countless other pathogens.
After a year of model-driven lockdowns, we may also look back to the original March 16, 2020 report to see yet another failure of its predictive ability. Recall that this is the model that fueled the alarmist rush to shut everything down last March, all to avert a 2.2 million death toll that would presumably peak around June.
(Source: Imperial College Report #9, March 16, 2020)
As noted above, the 2.2 million figure for the US (and corresponding 510,000 figure in Britain) were “worst case” scenarios in which the pandemic ran its course. According to the underlying theory of the ICL model, these catastrophic totals could be reduced by the adoption of NPIs – the escalating suite of social distancing measures, business and school closures, and ultimately full lockdowns that we observed in practice over the last year.
Aside from its 2.2 million worst case scenario, ICL offered no specific projections for how its proposed mitigation measures would work in the United States. Ferguson did however tell the New York Times on March 20, 2020 that a “best case” American scenario would still yield “about 1.1 million deaths,” giving us a glimpse of what he believed to be possible under NPI mitigation. The March 16th report similarly “predict[ed] there would still be in the order of…1.1-1.2 million in the US” under the most optimistic mitigation strategy, barring a large increase in hospital ICU bed capacity.
By contrast, ICL did publish an extensive table showing the results of its model run for Britain over a group of four increasingly stringent NPI scenarios. These range from the “worst case” projection with half a million deaths (the figures vary depending on assumptions about the virus’s reproduction rate) to a more stringent model where four NPIs (public school closures, case isolation, home quarantine, and social distancing) are simultaneously enacted. The results are depicted below.
(Source: Imperial College Report #9, March 16, 2020)
Note that the UK enacted policies based on all four measures recommended by the March 16th report, as well as an even more stringently enforced general lockdown on three separate occasions. After one year of following and expanding upon the Imperial College strategy, an unusual result appears in the data: not only have the UK’s numbers come up far short of Ferguson’s most alarmist scenario (depicted in the first column), but the UK has actually done much worse than the other NPI mitigation models in the ICL report.
As of the 1-year anniversary, the UK had a little over 125,000 confirmed Covid-19 deaths. By implication, the UK death toll has exceeded the mildest of the other three NPI scenarios from the ICL model (column 2) and blown past its heavier NPI recommendations (columns 3 and 4), even while operating under a more stringent set of lockdowns than ICL originally contemplated.
The implications are clear. While Ferguson wildly exaggerated the “worst case” scenario for the UK, he also severely overestimated the effectiveness of NPIs at controlling the pandemic.
By building its policy response around the Imperial College model, the UK government delivered the worst of both worlds. It imposed some of the most severe and long-lasting lockdowns in the world based on the premise that NPIs would work as Ferguson’s team predicted, and that such actions were needed to avert a catastrophe. Except the lockdowns did not work as intended, and the UK also ended up with an abnormally high death count compared to other countries – including locales that did not lock down, or that reopened earlier and for longer periods than the UK.
Why were the Ferguson/ICL predictions so far off base on both ends? The answer likely derives from two central flaws in their model design.
First, Ferguson adapted the model directly from a 2006 influenza pandemic model that he published in the journal Nature. As with the March 16th Covid report, this study aimed to predict the spread of a virus across the general population, subject to a suite of increasingly stringent NPI countermeasures. As the second-to-last paragraph of the study reveals though, it only modeled general population spread. In doing so, the authors acknowledged that “Lack of data prevent us from reliably modelling transmission in the important contexts of residential institutions (for example, care homes, prisons) and health care settings.”
With Covid-19 however, nursing homes have emerged as one of the greatest vulnerabilities in the pandemic. In many locales, nursing home deaths alone account for almost half of all Covid-19 fatalities despite housing only a tiny fraction of the population. While the latest nursing home figures for the UK are as of yet hard to come by, reports from last year suggest they are not only a large share of the country’s Covid-19 deaths but also severely undercounted in official records. Using a preliminary count from last year, the UK had one of the worst nursing home shielding ratios in Europe – a measure that compares a country’s death toll in its care facilities to the general population. The ICL projections likely missed this problem entirely due to a defect in the 2006 model it was built upon.
Second, Ferguson’s model severely overstated the effectiveness of NPIs at mitigating general population spread. Part of the appeal of the ICL report from last March came from its succinct portrayal of the available policy options and their claimed effects. The modelers presented political leaders with a menu of escalating measures to adopt with mathematical precision, each linked to an associated projection of its effectiveness at staving off the pandemic. All the politicians had to do was select from the menu and implement the prescribed course.
Except it wasn’t that simple in practice. ICL’s recommended NPI measures baked assumptions about their own effectiveness into the model. In reality, most of these assumptions had never been tested or even minimally quantified. As a key chart from the March 16th report illustrates, the supposed effect of each NPI was little more than a guesstimate – a set of nice, round numbers that purported to show the change in social interactions after its adoption.
A 2019 report by the World Health Organization (WHO) warned of the flimsy empirical basis for epidemiology models such as the one developed by ICL. “Simulation models provide a weak level of evidence,” the report noted, and lacked randomized controlled trials to test their assumptions. The same report designated mass quarantine measures – what we now know of as lockdowns – as “Not Recommended” due to lack of evidence for their effectiveness. Summarizing this literature, which included the same 2006 influenza model that Ferguson adapted to Covid-19, the WHO concluded: “Most of the currently available evidence on the effectiveness of quarantine on influenza control was drawn from simulation studies, which have a low strength of evidence.”
The UK’s experience under the ICL model therefore demonstrates not only Ferguson’s propensity toward wildly alarmist disease forecasting – it also illustrates the abject failure of lockdowns and related NPI measures to mitigate the pandemic. As a revealing point of comparison, the UK’s population-adjusted daily death toll under lockdowns has been consistently higher than no-lockdown Sweden for most of the pandemic, despite both countries following a nearly identical pattern of timing in both the first and second waves.
The relevant question, then, is not whether the UK failed to lock down stringently enough, but whether lockdowns offer any meaningful benefit whatsoever in mitigating the pandemic. A growing body of empirical data strongly suggests they do not.
The repeated failures of the Ferguson/ICL model point to a scientific error at the heart of the theory behind lockdowns and similar NPIs. They assume, without evidence, that their prescriptive approach is correct, and that it may be implemented by sheer will as one might achieve by clicking a check-box in a Sim City-style video game. After a year of real-time testing, it is now abundantly clear that this video game approach to pandemic management ranks among the most catastrophic public health policy failures in the last century.
Phil Magness is a Senior Research Fellow at the American Institute for Economic Research. He is the author of numerous works on economic history, taxation, economic inequality, the history of slavery, and education policy in the United States.
Rights group slams exclusion of Israel, Saudi-led coalition from UN’s children-in-conflict blacklist
Press TV – March 19, 2021
A group of child rights experts has strongly criticized the UN secretary-general for excluding eight parties, including the Israeli regime and the Saudi-led coalition, from a global blacklist of parties accountable for harming children during conflicts.
In a report, experts from Watchlist on Children, an international child rights group, said at least eight parties to conflicts were found responsible for killing and maiming more than 100 children in a single year but were excluded from the blacklist.
There were “numerous discrepancies and omissions in listing decisions, as well as unwarranted delisting decisions,” the report said, noting that the UN Security Council’s 2001 resolution establishing the list to protect children from the horrors of war “is being seriously undermined” and could result in “putting children at even greater risk.”
“The secretary-general’s 2015 annual report acknowledges that the number of children killed by Israel in 2014 was the third highest in the world and the number of schools damaged or destroyed was the highest anywhere in the world that year, and yet Israel has not been listed in the report’s annexes,” the report said.
It added that at the time the news media reported that Israel and the United States conducted intense lobbying to prevent the regime’s blacklisting.
The child rights experts also cited annual reports that have repeatedly found Israeli forces responsible for high levels of Palestinian child casualties, including over 1,525 killed and maimed in 2020 alone.
“Yet Israeli forces have yet to be included in the annexed list of violators,” they added.
According to the AP, co-author Yanghee Lee, former chair of the committee that monitors implementation of the UN Convention on the Rights of the Child, said: “We are calling on the secretary-general to urgently address these problems and commit to hold all countries and groups responsible for violations against children accountable without fear or favor.”
In response to the report, UN spokesman Stephane Dujarric said Secretary-General Antonio Guterres stands by the report’s conclusions, expressing willingness to engage with relevant partners in how to improve the system.
Benyam Dawit Mezmur, another co-author of the report urged the UN to ensure that the list of perpetrators it issues is “more credible, accurate, complete, and evidence-based, or risk pulling apart this unique tool for the protection of children caught in war.”
The report also said the Saudi-led war coalition against Yemen was delisted in 2020 for killing and maiming children in the Arab country, despite being responsible for a documented 222 child casualties during the reporting period.
“With this delisting, the Saudi-led coalition has been removed from the Secretary-General’s list altogether,” it noted.
Citing the Secretary-general’s reports that said the Saudi-led coalition was delisted due to a “sustained significant decrease” in the number of violations they committed, the rights experts said such argument runs counter to the established delisting criteria.
“This runs counter to the delisting criteria established in 2010 and the Security Council’s stated expectations that listing is based on whether or not grave violations against children took place in violation of international obligations,” they maintained.
According to the UN Office for the Coordination of Humanitarian Affairs (OCHA), the Saudi war on Yemen has killed a quarter of a million people, including women and children, since it was launched in March 2015 with the aim of restoring the Riyadh-friendly government of former President Abd Rabbuh Mansour Hadi.
Some Observations On the Efficacy of Masks in a #COVID19 World
By Kevin Kilty | Watts Up With That? | March 16, 2021
Some weeks ago, Pat Frank suggested that I might consider writing an essay about the efficacy of masks and mandates to wear masks during this pandemic. I hesitated doing so at first, but March 8th I noticed another research effort on the part of the CDC to justify masks as a prophylactic strategy.[1] This effort seems very deficient in my view and so this essay resulted. What I write here is a summary of a much larger work in progress.
Lincoln Moses and Frederick Mostellar long ago suggested that public policy be organized as experiments so that we might learn of its effectiveness, or lack thereof, and avoid successive failures.[2] When the COVID-19 pandemic arrived last spring, I wrote that we didn’t need to go through successive battles with exponential processes, but that we appeared not ready to gather useful data and evidence about the effectiveness of social distancing and other advice in this battle.[3] Considering the tendency of people to don a mask against all sorts of bad air is so universal that even screen writers employ it to add realism to a disaster scene, one would think we would know something about their effectiveness.[4] We do and we don’t. While I am told by some people employed in medicine along with many amateurs that masks are essential to controlling spread of SARS-COV-2; highly reputable authorities, many of them, thousands of them, make much more modest and even opposite claims.[5]
How might we analyze these competing claims? I see three avenues of attack: First, we can examine theoretical reasons for and against masks from a mechanical perspective. Second, there are limited experiments known as randomized clinical trials available, all of which have some deficiencies and limited pertinence. Third, we can examine observations of the progress of this epidemic as shown by cases in the light of local mandates. These observations and the methods used to evaluate them are quite deficient in many ways, but they do tend toward similar conclusions.
Mechanical Considerations
The CDC, WHO, and local departments of health have issued a variety of advisories about masks which they update periodically. A typical advisory begins as follows:
“Because the virus is transmitted predominantly by inhaling respiratory droplets from infected persons, universal mask use can help reduce transmission.”
As a rationale for masks this fails because it does not mention a necessary prior element. In order to work, masks have to attenuate the guilty aerosols. The individual aerosols involved could be only a micrometer or few micrometers in size. The individual virions are in the range of 50-130 nanometers.[6] I have looked at a number of cloth masks that one can purchase and found their pore sizes to be 0.05 to 0.15 millimeters. This is 1000 times larger than virions and hundreds of times larger than small aerosols. No wonder these packages of masks should come with disclaimers. Adding to this issue of excessive pore size is that cloth masks are not made of certified materials, are manufactured to no standard, are often ill-fitting displaying gaps aside the nose and on the cheeks, or pulled down below the nose, and sometimes placed over a beard. Flat surgical masks do better at times with the excessive pore size problem but still present issues with poor fit and gaps.
There is a mask that corrects most of these deficiencies. The N-95 mask is made of qualified materials and manufactured to a standard. These masks attenuate 95% of particles in the size range of 0.3 to 0.5 micrometers. However, they still require attention to fit to reduce gaps, and they are not guaranteed to halt very small aerosols the size of individual virions. A news article last summer in the Japanese newspaper, The Asahi Shimbun,[7] summarized measurements that researchers made on particle attenuation of cloth, gauze, and N-95 masks, supports what I have summarized here. Cloth and gauze masks have zero effectiveness; while N-95 masks perform to specification, but only if fitted and worn properly. And even then there is no guarantee they prevent the transmission of disease.
There is one more mechanical aspect to ponder. Often in a crisis people will offer what expertise they can – they recycle their expertise. Something I am doing here. Recently a number of researchers in the field of fluid dynamics have weighed in with measurements and simulations (as one would expect) using computational fluid dynamics (CFD). The AIP journal Physics of Fluids produced a special issue in October 2020 highlighting the physics of masks. One study uses CFD to model persons wearing masks inside and outside, in various conditions of air flow, to address ability of masks to attenuate aerosols ejected from a cough or a sneeze.[8] They state in conclusion…
“… our results suggest that, while in indoor environments wearing a mask is very effective to protect others, in outdoor conditions with ambient wind flow present wearing a mask might be essential to protect ourselves from pathogen-carrying saliva particulates escaping from another mask wearing individual in the vicinity.”
This means, I presume, that masks are useful in a situation when all around are sick, and sneezing, wheezing, and coughing — in other words, in a Covid ward of a health care facility. What does “very effective” mean? If it means a very great attenuation of particles, greater than 95% say, then this still has to be interpreted in the light of findings that as few as 300 virions can lead to disease.[9] However, one would think that if coughing and sneezing are the issue, then covering a cough or sneeze should do as well, or perhaps even better when one considers the problem of ill-fit and aerosol escaping through gaps. My experience since March 2020 is that I never encounter anyone in public who are so sick that they are simply sneezing and coughing with abandon.
This computational fluid dynamics approach to determining the efficacy of masks resembles the equivalent modeling approach to climate change. They imply that models define reality when, in fact, it should be that observations and measurements do. There is no means to turn CFD models into clinical outcomes.
In summary, there are mechanical reasons to suppose that masks could reduce the spread of virus in some settings, but none appear pertinent to the materials used to construct masks, or to the ways the public wear them in about 98% of situations. Opposed to supposing that masks might work, or modeling how they might work, we can only learn what efficacy they have by making experiments or observations.
Experiments
The closest thing I have found to true experiments regarding masks are a small number of randomized clinical trials (RCTs). A surprisingly few RCTs involving masks and respirators have been done.[10] I will summarize only two of these. Of these one is pre-COVID-19 and not controversial, and the other is post COVID-19 and subject to controversy and censorship.
There are many respiratory diseases which circulate in the human population. The recent epidemics of MERS, SARS, Ebola and influenza provoked a search for effective non-pharmaceutical interventions. In one example, a group of doctors became interested in how well cloth masks performed for preventing infection in hospitals because such masks are in wide use in the developing world. This trial involved 1607 volunteers at 14 hospitals in Hanoi, Vietnam working in high-risk wards. There were three arms in this RTC: cloth masks, surgical masks, and a control arm of “standard practice” which involved some mask usage but at about one-half the compliance rate of the two treatment arms. The study took place over a four week period, and was to the authors’ knowledge, the first RCT involving cloth masks. Among their findings were that particle attenuation was virtually nil in the cloth masks (97% infiltration), and surprisingly poor in these particular medical masks (44% infiltration). The rate of infection in the cloth mask wearers was double that in the medical mask wearers; medical masks showed some effectiveness, but this contradicted earlier studies showing no efficacy to the medical masks.[11] The researchers conclude that cloth masks should not be advocated for health-care workers, at least until a much better design of such is produced.[12]
The second RCT was performed in Denmark last spring and was subject to censorship by our social media as well as facing some publication resistance.[13] It involved 4862 participants who completed the study. It is more pertinent to this essay because it addressed the efficacy of masks outside of a health care setting. Participants were divided into a control group asked to refrain from wearing masks when out of their home and a treatment arm asked to wear a mask when out of the home for three hours per day. Both groups were ask to follow other social distancing guidelines in order to prevent confounding of masks and distancing which have similar if not identical effects. The primary measured outcome was the number of participants showing SARS-CoV-2 or other respiratory viral infections after one month as determined from PCR testing or hospital diagnosis.
The outcome produced an infection rate of 2.1% in the control arm against 1.8% in the treatment arm. However, the confidence interval of odds ratio (CI of 0.53 to 1.23) included a value of 1.0 almost at its center, suggesting no significant difference in outcomes. If one were to yet insist that the small difference in attack rate (42/2392=1.8% versus 53/2470=2.1%) is nonetheless an important risk reduction, the absolute risk reduction implied (0.003) translates into 30,000 hours (90 hours/0.003) of mask wearing to prevent one case of COVID-19 when community prevalence is around 2.0%. Take that as you may.
There is an interesting series of response letters to this study that are published along with it. These make some legitimate points about design deficiencies. It is certainly true that a study involving masks cannot be a “true RCT” because one cannot blind a study involving masks to a clinical end. The wearer knows they are wearing a mask, and so does the rest of the public. I won’t belabor this point by describing what can go wrong in an unblinded study. Another criticism focuses on using PCR tests, with their false positives and negatives, to measure outcome – a problem which will return in the next section about observations. However despite some criticism, one might note that the outcome of the CHAMP study, in which U.S. Marine Corps recruits were subjected to rigorous social distancing, hygiene and mask wearing resulted in just about the same attack rate as found in this study.[14] I doubt it is possible in the present politicized and hysterical atmosphere to do an RCT on any non-pharmaceutical intervention that could satisfy critics, but none that I know of have shown significant effectiveness of masks.[15]
Observations
Before launching into a discussion of what observations concerning the epidemic may mean, a brief segue into the incubation period and other influences on reporting is instructive. The incubation period of Sars-CoV-2 is probably ten or fourteen days long. Following exposure there is a probability on each successive day of someone becoming a case with half of the ultimate cases developing by day five or six.[16] The process behaves like a low pass filter with a delay. Figure 1 shows this. One-hundred exposures on day zero, presuming all result in cases, produces rising numbers until 19 cases occur on day five. Then they decline to zero.
This has two important considerations. First, it smooths the results of any factor producing a change to R, the reproductive ratio, and makes such changes harder to detect. That is, it reduces resolution. Second, it produces a correlation of cases day to day, so that counts of cases on successive days are not independent of one another, and this has the effect of reducing the degrees of freedom in observational data.[17]
Add to this the distortions resulting from common graphing options like 7 to 21 day averaging done with one-sided (causal) filters; and distortions which resulted from switching from clinical diagnosis to “lab confirmed” cases resting on PCR tests, and what one has is a mess. It is easy to reach a point where what a graph shows today is what might have happened three weeks earlier.
Figure 1. From a single exposure event cases climb for many days afterward in the incubation period. This behaves like a low-pass filter with a delay.
One does not have to search extensively to find evidence suggesting that epidemics proceed unhindered despite all sorts of mandates. I know of no epicurve showing a clear effect. Figure 2, using data drawn from the Covid Tracking Project, for example, shows a comparison among Colorado, New Mexico, and Utah. Despite mandates of various rigor, introduced at different times, the epicurves are virtually the same.[18] The Swiss Policy Research Group produced a nice twelve-paned panel, found here, which makes comparisons among various countries, with the same result – masks have no obvious benefit. A more detailed time series of cases in four German cities during April, 2020 also shows no benefit;[18] however, I would criticize these time series as being of such short duration following the mandatory mask order as to have possibly missed the period of greatest effect, if there is one, just over incubation delay.
Figure 2. Comparison of epicurves from three neighboring states, with timing of mask mandates shown. This was done by @ianmSC on Twitter using data drawn from the Covid Tracking Project.
The global data firm Dynata reported that by the first of July mask wearing in Houston and south Florida was likely to be 80% even before mandates; yet these places saw multiple large waves of infection thereafter.[20] California and New York applied rigorous mask mandates, yet still went through several large waves in the summer and autumn. The USA as a whole, in which 39 states imposed mask mandates in April or before, exhibits an epicurve almost identical, except for vertical scale, to Wyoming, the smallest state, even though Wyoming applied no state-wide mandate until November 9. The CDC reported that most people contracting COVID had worn masks, although self-reporting is notoriously inaccurate.[21]
There are many problems with our observational data. Death counts have been biased by incentives provided to hospitals over payments for COVID-19 deaths.[22] While many states tried to build useful epicurves by placing cases on date of symptom onset, many publically available data sets were built by date of case report and become dominated by the cycle of bureaucratic testing and reporting rather than by characteristics of the disease. To see how these differ Figure 3 shows Colorado data from 08/02/20. The difference is stark with a dominant seven day cycle which some people have confused with a dynamic of the disease and which disappears in the date of onset rendition. A subtle effect like mask usage is likely to be lost in these extraneous influences.
Figure 3. Comparison of epicurves by date of onset vs. report date.
The case data is a mess because when it began early in 2020 cases were confirmed through symptoms or at least a probable contact with another case, but eventually became dominated by mass testing of people without symptoms using PCR tests. Once this mass testing took hold even states trying to maintain an epicurve by date of onset could no longer do so. Figure 4 shows the curve for the state of Wyoming which became dominated by the weekly cycle of PCR testing which began at the University in Laramie in mid-august, but really took effect with return of students around September 1. Because so many of the “lab confirmed” cases had no associated symptoms a full one-third of cases remained always under investigation and the date of report became the de facto date of onset.[23]
This university provides an interesting case study in itself. The total number of cases from the start of the epidemic to the 31st of August in the entire county was134 – less than one case per day. The university instituted a very rigorous set of rules for reopening including mask wearing in all settings inside and out, rules for limiting number of persons in university vehicles, foot traffic patterns inside buildings, dedicated entrances and exits, periodic sanitation of all surfaces, social distance guidelines and even a web site to report persons not following rules. I did a few informal surveys around campus in September and October and thought mask compliance was between 80 and 90%.
Nevertheless by October 15, six weeks later, the county had added 780 cases of which 551 (71%) were connected to the U.W. campus. The rules and masks appeared to present no barrier to the spread of our mini-epidemic.[24]
Figure 4. Confirming cases using lab PCR tests caused the appearance of a seven day period in the epicurve.
Evidence provided to support mask mandates consisted mainly of a single study.[25] There have been many criticisms of this study, including one which suggested it be retracted.[26] However, ignoring its controversy for the moment, let’s just focus on what the authors have to say.
They state, first of all, that masks may have effectiveness as large as 85%, but that this estimate has low confidence – precise number but narrow confidence interval. Second, they notice a diminished effectiveness between N95 respirators on the one hand and cloth masks with 12 to 16 plies on the other. No one wears cloth masks with even one-fourth as many plies. Thus, this can’t be an endorsement of cloth masks. No one has unlimited access to N95 respirators,[27] and couldn’t because there is not enough manufacturing capacity to supply them to the public in general. Thus, this “essential” study does no more than reiterate what the other sources of information, including the measurements of particle attenuation reported in the Asahi Shimbun article, have to say. Its recommendations are not pertinent to reality of mask wearing by the general public. This is an unscientific rationale.
A more recent effort to promote masks as essential to controlling the pandemic appears to me to have many shortcomings.[28] This is a retrospective study of the history of the epidemic on a county level, referenced to timing of mask mandates and orders to close or limit restaurant traffic between March 2020 and October 2020. It is what economists would call an “event study”.[29] Problems with the study include:
- The event involved in an event study should be independent of the data. It is not in this case. Mask mandates were generally applied through political pressure during a pandemic wave. Often applied when the wave had begun to wane.
- Mask mandates are probably hopelessly confounded with other orders such as closure of restaurants. According to the researchers themselves, the mask mandates began in April in 39 states, and restaurant closures began in 49 states in March and April. Two influences atop one another. The claim to having a mask measurement unconfounded by closures cannot be true, or there was a lot of data sorting involved which becomes another confounder.
- The paper is missing details about the statistical methods and calculation of significance.
- Even if significant in a statistical sense, the effect seems very small.
The worst flaw seems to me to be a subtle one. The underlying data of the CDC study are curves of cumulative cases and deaths, which I have already explained are flawed to begin with. However, the typical cumulative curve, being a logistic curve, has a particular shape that begins as an almost exponential rise but quickly passes through an inflection with constantly diminishing slope as it approaches a horizontal asymptote. Such a curve will display a long sequence of days in which the case rate declines. An average of daily changes over segments of this decline, even with noise added, which are then referred to an earlier time period, will produce results just like those in the CDC study. No matter what the cause of the limit to an epidemic, the result is the same. What has happened is the CDC has chosen a statistic having a nearly perfect expectation to the characteristics of a logistic curve from any limiting influence, and cannot draw a distinction between the null hypothesis and a particular alternative. It is like circular logic.
Conclusions
There are situations, health care settings mainly or situations of extreme community prevalence with a lot of coughing and sneezing in public, where masks serve a useful purpose. Yet, people who insisted last spring that the epidemic would go away with mask mandates could not have been more wrong. Every consideration shows this.
Nearly all the masks we see people wearing are constructed to no standard, made of varying sorts of cloth, are poorly fitting, are worn with near complete disregard for effectiveness, reused who knows how many times, used for what else we know not, and are often completely open at the cheeks, nose, chin and beard. They appear mainly useful for making a person touch their face constantly.
How about experimental or observational evidence from the present pandemic? The only experimental evidence is consistent with the benefits being so small they cannot be distinguished from occurrence by chance. Probably no new experimental evidence will become available for the following reason: People have probably changed their behavior drastically during this pandemic leading to too many confounding factors to identify the effect of just one. As the epidemic wanes recruiting sufficient subjects for RTCs becomes difficult.
Masks mandates are not a risk free intervention. They have a poor effect of civil society, they absorb resources, they possibly carry health risks of their own, and they certainly contribute to mistaken notions of safety and risk. Masks seem to me like a solution to a political problem which should alone raise skepticism about all claims.
References/Notes:
1- Gery P. Guy,Jr. et al, Association of State-issued Mask Mandates and Allowing On-Premises Dining with County-level COVID-19 Case and Death Growth Rates, https://www.cdc.gov/mmwr/volumes/70/wr/mm7010e3.htm?s_cid=mm7010e3_w, last accessed 3/8/2021.
2-Lincoln Moses and Frederick Mostellar, Experimentation: Just do it!, In Statistics and Public Policy, Bruce D. Spencer Ed., Oxford U Press, 1997.
3-Futile Fussings: A history of Graphical Failure from Cattle to #coronavirus https://wattsupwiththat.com/2020/03/31/futile-fussings, last accessed 03/13/2021.
4-Close Encounters of the Third Kind, for example.
5-I have a collection including about three-dozen essay, opinion pieces, and research papers, discussing the topics of social distancing, mask mandates, lockdowns, school closures. These include contributions by Dr.s Scott Atlas, John Ioannidis, Paul Alexander, Donald Henderson, Jay Battacharya, Sunetra Gupta, Carl Henehgan, Tom Jefferson, Martin Kulldorff, and others; and almost all of these have been ignored, scorned, or censored in some way.
[6]-Individual virions are mentioned as having various sizes ranging from 50 to 130 nanometers in various internet sources. Corona viruses are pleomorphic which means they have a variety of shapes.
7- Cloth face masks offer zero shield against virus, a study shows, Nayon Kon, The Asahi Shimbun, July 7, 2020.
8-Ali Khosronejad, et al, Fluid Dynamics simulations show that facial masks can suppress the spread of COVID-19 in indoor environments, AIP Advances 10, 125109, (2020); https://doi.org/10.1063/5.0035414;
9-Referenced in Imke Schroeder, COVID-19: A Risk Assessment Perspective, J Chem Health Saf., 2020 May 11: acs:chas.0c00035
10-Tom Jefferson, and Carl Heneghan, Masking lack of evidence with politics, Center for Evidence Based Medicine, July 23, 2020. In particular the authors note the surprisingly small number of RTCs considering the great importance of controlling respiratory disease.
11-C. Raina MacIntyre, et al, A cluster randomized trial of cloth masks compared with medical masks in healthcare workers. BMJ Open 2015;5;e006577. doi.org/10.1136/bmjopen-2014-006577. Two earlier studies conducted in China by same group found no effectiveness for medical masks.
12-By significant in this context the authors mean a 95% confidence interval that does not enclose a relative risk of infection of 1.0, but is entirely above or below 1.0.
13-Henning Bundgaard, et.al. Effectiveness of adding a mask recommendation to other public health measures to prevent SARS-CoV-2 infection in Danish mask wearers, Annals of Internal Medicine, 18 November 2020. https://doi.org/10.7326/M20-6817
14-Andrew G. Letizia, et al, SARS-CoV-2 Transmission among Marine Recruits during Quarantine, N Engl J Med 2020; 383:2407-2416. DOI: 10.1056/NEJMoa2029717
15- Not finding significant protection, significant in the statistical sense, does not mean masks are completely ineffective, or counter-effective, but rather that their effect was not so large that it could be distinguished from a chance outcome at some level, usually 95%, of confidence.
16-P.E. Sartwell, The distribution of incubation periods of infectious disease, Amer. Jour. Hyg., 1950, 51:310-318. Sartwell lists coronaviruses as having a log mean of 0.4 (2.5 days) and dispersion of 1.5. However, a recent training class stated a median of 5-6 days for SARS-CoV-2. I used 5 days for purposes of producing Figure 1.
17-swprs.org/2018/10/01/covid-19-intro/ search for the English language version.
18- This panel of four German city graphs can be found at swprs.org/face-masks-evidence/ last accessed on 3/12/2021
19-This is well known, but see for example, chaamjamal, Illusory Statistical Power in Time Series Analysis, April 30, 2019, https://tambonthongchai.com/2019/40/30/illusory-statistical-power-in-time-series-analysis/ last accessed 1/18/2020
20-WSJ July 29, 2020.
21-CDC report referenced in article at The Federalist, CDC Study Finds Overwhelming Majority Of People Getting Coronavirus Wore Masks, October 12, 2020 https://thefederalist.com/2020/10/12/cdc-study-finds-overwhelming-majority-of-people-getting-coronavirus-wore-masks/
22-Payments for covid deaths, but not for others is incentive enough to bias results.
23-My attempts to learn how many cycles were being employed to report PCR results revealed that no one at any responsible agency in my state knew. All they would do is refer me to a misleading and wrong page at the supplier of the tests. However, a news item reported that researchers at Wayne State University a variety of cycle numbers are used to report results nationally including numbers from 25 to above 37. Viral Loads In COVID-19 Infected Patients Drop, Along With Death Rate, Study Finds Researchers find “a downward trend in the amount of virus detected.” Joseph Curl, DailyWire.com, Sep 27, 2020
24-UW to implement enhanced covid-19 testing program Monday, UW press release, Oct. 15. Data from this also mentions the university expects to perform 15000 tests per week. Yet my asking questions revealed that no one seemed to know what to expect from false positive and negative results. Amazingly few people recognize that interpreting the outcomes of PCR tests is a matter of conditional probability and cannot be done reliably without other information. Even one-half of the faculty and students at Harvard medical school did not know this according to an example from Julian L. Simon in his book “Resampling: The New Statistics, 1997.”
25-Derek K Chu, MD, et al, Physical distancing, face masks, and eye protection to prevent person to person transmission of SARS-CoV-2 and COVID-19: a systematic
review and meta-analysis, The Lancet, v 395, issue 10242, p1973-1987, June 27, 2020 https://doi.org/10.1016/S0140-6736(20)31142-9
26-For example, the Center for Evidence Based Medicine (CEBM) at Oxford University objects to its social distancing conclusions.
27-The term “N95 Respirator” is ambiguous. These respirators are designed to be tight fitting, but most N95s are manufactured for construction, while there are N95s specifically manufactured to prevent disease transmission. Unfortunately the studies cited do not present a clear picture of which N95s were employed.
28-Refer to note #1 above. But in addition to my concerns listed here more were raised in Paul E. Alexander, The CDC’s Mask Mandate Study: Debunked, AIER, March 4, 2021 https://www.aier.org/article/the-cdcs-mask-mandate-study-debunked/ last accessed 3/13/2021
29-John Staddon, Scientific Research: How Science Works, Fails to Work, and Pretends to Work, Routledge, 2018, p. 124.
Magic Hour for Gates
By Gary Jordan | Wake The F*** UP | March 15, 2021
Bill Gates wants to know everything about you.
He wants to control every aspect of your life.
His insatiable thirst for the supervision and scrutiny of every human being alive has sparked a US$1 billion investment that will blanket the earth in video surveillance satellites, providing real-time feedback with a mere one-second delay. He advocates for Orwellian digital tools to track your every move and has bankrolled US$1.7 billion to provide artificial intelligence that will be used to scan each and every visitor to major event venues worldwide. He wants to do away with end-to-end encryption, so he can snoop on your private conversations. He wants to know when and where you travel and who you travel with. He wants you to eat whatever he says you should eat, drink whatever he says you should drink, think how he wants you to think and know only what he wants you to know. Plus he wants to dictate what goes inside your body.
But he doesn’t want you to know a lot about him.
For instance, in spite of the countless hours he spends babbling on mainstream media, as he preaches to the masses – letting them know what’s good for them – with the aid of obsequious hosts and fawning presstitutes, he consistently fails to mention (or be questioned) of his links to predatory paedophiles. He doesn’t want you to know about any of that. He still believes in personal privacy after all it would seem – especially when it’s to his advantage.
Gates hasn’t, for example, revealed very much about his links to Jeffrey Epstein and the Lolita Express. He also keeps mum concerning his connections with a collector of child pornography and a suspicious video production business, based out of Seattle, known as Magic Hour.
Not a word. Deafening silence.
Joining a long list of fellow filthy rich and filthy minded miscreants such as former US President Bill Clinton, former Israeli President Ehud Barak, Hollywood stars Alec Baldwin and Kevin Spacey and celebrity lawyer, Alan Dershowitz, Bill Gates has indeed boarded the Paedo Plane and spent considerable periods of time in the company of Jeffrey Epstein. Although you would never have thought so, given the lack of media attention it receives, the flight manifests do indeed show that the world’s number one vaccine vendor was a passenger. On top of this, we know that Gates also donated US$2 million to the Massachusetts Institute of Technology, which the University described as a ‘gift’ from The Gates Foundation, ‘directed’ by Jeffery Epstein.
What’s more is that Bill Gates knew, throughout several of their meetings, what Jeffrey Epstein truly was – a paedophile and a trafficker of children for the rich and famous. He knew of the crimes Epstein was guilty of committing. Regardless, Gates continued to meet him and continued to conduct business with him.
Another curious link between Bill Gates and Jeffrey Epstein is Boris Nikolic. This is something else that Gates has not been so quick to discuss live on air. Nikolic is a former science and technology advisor to Bill Gates and the two men were involved in a biotech venture capital firm in which Gates invested money. On the death of Epstein, it was discovered that Nikolic was named in his will as a successor executor and would be responsible for taking care of his estate, in the event that the first two named executors were unwilling or unable to do so.
There were also the Transhumanist and Depopulation Agendas that clearly existed between the deviant physics teacher-cum-hedge fund manager and Bill Gates, which raised an eyebrow or two. Gates, who comes from a family renowned for their active involvement in eugenics, and Epstein, who once expressed a wish to seed a race by impregnating women at a ranch in New Mexico, seemed suitably joined at the hip in their bizarre and disturbing world views. Epstein was a generous donor to the World Transhumanist Association and was reported to have lashed out at scientists from Harvard University, who he criticized for attempting to eliminate poverty and hunger, stating that they were contributing to the overpopulation of the planet by doing so. Gates himself has never shied away from openly declaring his burning desire to eliminate this perceived issue, and has even suggested that the use of vaccines may be the way forward in solving it.
Birds of a feather flock together? The softball media approach to Gates fails to enlighten us on these topics.
Lack of journalistic attention, deliberate or otherwise, was evident when arrests at the Gates’ mansion in 2013 in Washington State went unreported for over nine months after they had taken place. These arrests would link Gates to yet another paedophile. A criminal court trial was opened after engineer, Rick Allen Jones, was found to be in possession of child pornography. It was discovered, after a tip-off from the National Center for Missing and Exploited Children, (NCMEC), that Jones had traded over 6000 graphic images of child abuse and rape with other perverts. One of these images had been traced back to the Gates household, where Jones worked and where he was arrested. Others were labelled by him to signal that they were images containing scenes of bestiality and urophilia. Intriguingly, a couple of them were even given the label ‘Melinda’. Several of them contained photographs of missing children who were known to the NCMEC.
The arrest of the engineer was later followed by an admission and a conviction. A 3000 page Seattle Police Department report gave further details that were absent from the press reports and indicated that the paedophilia extended to a much broader network. For starters, part of the report mentions a man by the name of ‘Brett’ who, according to police, referred to Rick Allen Jones in email correspondence as ‘Uncle RJ’. Brett’s email domain is from a company called Magic Hour, who claim to create ‘video magic at your fingertips’ and there is evidence in the police report that emails were exchanged between him and the engineer. Whether they were of a pornographic nature or not is uncertain, however we do know that part of the reference to Brett from Magic Hour is redacted and coded, which suggests that it is a description of child sexual abuse.
What is notable is that Magic Hour has a long list of big-name clients listed on their website. These include AT&T, McDonalds, Hewlett Packard, Holland America and, of course, Microsoft. I have reached out directly to them (so have others I researched this case with) and asked about Brett and their relationship with ‘Uncle RJ’. To date, I have not received a response. Nor has anybody else to the best of my knowledge.
Most frustrating of all is the fact that the warped engineer is a free man today and has not done any jail time for his crimes. Magic Hour continues to operate, still boasting of their celebrated customers, with their link to Rick Allen Jones and his child pornography stash remaining a mystery.
Bill Gates, of course, is never quizzed about these subjects. When you can afford to spend millions of dollars bribing the media, you ensure that you are exempt from such analysis. The subject matter discussed in this article will not be aired on CNN or BBC. We cannot expect, anytime soon, to hear Anderson Cooper or Don Lemon broach these facts. In the meantime, we are expected to ignore the elephant in the room and acquiesce to Bill Gates’ demands. We must stay tight-lipped and heed his advice. Depressingly, many do.
In the meantime we drift further into our Brave New World; one that sees innocent people locked up for not wearing a mask or for invoking their right to protest, as immoral, degenerate, child-abusing individuals like Rick Allen Jones walk with impunity on our streets. Bill Gates, the man who employed this reprobate and who wined and dined serial child abuser, Epstein, is held up as a saintly saviour, destined to protect us all from whatever danger he can conjure up for us – with no accountability and no attempt from the CIA agents masquerading as journalists in the mainstream media to get to the truth.
Undoubtedly, the plandemic has served as a great diversion for Gates, taking attention away from his affiliations with the most depraved in our society. But, it is high time now that he started to answer some very basic questions. Such as, why was a known distributor of child pornography, one he employed, sending emails to a man who was directly involved in a video production business which lists an organization that Gates founded as a client? Why were some of the pornographic images discovered in his employee’s trove of exploitation and abuse marked in the name of Bill Gates’ wife? Why did Gates continue to associate with Jeffrey Epstein, despite the knowledge of his abuse of underage girls? Why did he use the private plane of a child molester, when he has a plane of his own? Why didn’t Gates make his donation to MIT directly? Why did he pay it through Epstein? Why was a business associate named as an executor to Epstein’s will?
As verifiable, hard evidence exists in the form of police reports, court documents and bank statements, Gates cannot pull off his usual ploy – dismissing facts as wild conspiracy theories. He cannot gaslight the public any longer.
As a man who feels he has the right to know the ins and outs of everyone’s business and who inserts himself into the spotlight of every major world crisis, the least he can do is answer for the aforementioned connections. If it was you or I who was associated with an infamous child trafficker, or who had employed a trader of child pornography in our homes, you can be sure we would be asked to explain ourselves. There is no reason why Gates shouldn’t have to do the same.
In my book, The COVID-19 Illusion; A Cacophony of Lies, many of the characters at the forefront of the Coronavirus pandemic are placed under the microscope. They all have skeletons in the closet. This includes Dr. Tedros Adhanom Ghebreyesus (a man who has connections to terrorism and genocide), Dr. Anthony Fauci (a man who championed the poisonous and deadly AZT drug and approved its use on AIDS victims) and Professor Neil Ferguson (a man with a knack for getting it wrong via his erroneous computer-simulated projections). By far though, the person who is most shrouded in controversy is Bill Gates.
On top of the aforementioned connections with paedophilia, Gates has also been implicated in the suffering of children in Third World nations, as a direct result of his medical interventions – namely vaccination campaigns. His insertion into public health protocol arises even though he is not a doctor and does not have a medical degree. A report from the International Journal of Environmental Research and Public Health showed how his polio vaccines were responsible for 491,000 cases of paralysis in children in the state of Utter Pradesh, India. He has been accused of unethical practices and his experimental HPV vaccine drive was criticized for being deliberately misleading to vulnerable village girls. His team of vaccinators were also discovered to have bullied parents and forged documents. In Africa, his meningitis vaccine campaign alone caused the paralysis of 50 children and the Gates Foundation-funded GSK malaria vaccine is known to have killed 151 infants.
Again, if it was you or I practicing medicine without appropriate qualifications, how do you think we would be treated by authorities?
There can be no further evasion for Bill Gates. His silence on these matters must end. The public wants to know about the effects of his vaccines on children in Africa and India. They want to know about his connections to child abusers in the West. They have a right to ask and a right to know.
The farting cows and fake breast milk lectures can wait.
Bill Gates needs to start speaking about his crimes. It’s well past time he was held accountable for them.
thecovid19illusion@protonmail.com
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From the Archives
US Also Destroyed Iraqi Water Supply During First Gulf War
International Humanitarian Law prohibits the attack, destruction, or rendering useless of objects indispensable to civilian survival.
By Kurt Nimmo | Another Day in the Empire | June 10, 2026
… During the first Gulf War, the United States “deliberately bombed Iraq’s water system,” Thomas J. Nagy, a professor at George Washington University, wrote in September, 2001. The Bush administration, “with malice aforethought,” “destroyed, removed, or rendered useless” Iraq’s ‘drinking water installations and supplies’… They amount to a systematic effort to, in the DIA’s own words, ‘fully degrade’ Iraq’s water sources.”
Nagy found declassified documents at the website of the Office of the Special Assistant for Gulf War Illnesses that detailed the effort to degrade Iraq’s water supply and commit a serious violation of humanitarian law. The documents included “Iraq Water Treatment Vulnerability,” “Disease Information,” “Disease Outbreaks in Iraq,” “Medical Problems in Iraq,” “Status of Disease at Refugee Camps,” “Health Conditions in Iraq,” and “Iraq: Assessment of Current Health Threats and Capabilities.”
Then Representative Cynthia McKinney, a Democrat from Georgia (subsequently removed from Congress), referred to the Iraq Water Treatment Vulnerabilities document, and said, “Attacking the Iraqi public drinking water supply flagrantly targets civilians and is a violation of the Geneva Convention and of the fundamental laws of civilized nations.” … Read full article
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