A very interesting article was recently published in Lancet that sought to understand which factors correlate, on a country level, with covid related outcomes. The study was observational, so it can only show correlation, not causation, but it can still give pretty strong hints as to which factors protect people from covid, and which factors increase the risk of being harmed.
The most interesting thing about the study, from my perspective, was that it sought to understand what effect lockdowns, border closures, and widespread testing have in terms of decreasing the number of covid deaths. Although correlation does not automatically imply causation, if there is a lack of correlation, then that strongly suggests a lack of causation, or at least, that any causative relationship that does exist is extremely weak. And considering the amount of money, effort, and resources that have been poured in to lockdowns this year, and that continue to be poured in to them right now, it would be pretty disappointing if lockdowns had such a minimal effect that there was no noticeable impact on mortality whatsoever. Am I right?
But I get ahead of myself. The study chose to limit itself to looking at the 50 countries with the most recorded cases of covid-19 as of the 1st of April 2020. My interpretation is that they chose the top 50 most affected countries, rather than looking at all 195 countries, due to resource constraints. Data was gathered up to the 1st of May 2020. All information gathered was in the form of publicly available facts and figures. Data gathered included information about covid, income level, gross domestic product, income disparity, longevity, BMI (Body Mass Index), smoking, population density, and a bunch of other things that the researchers thought might be interesting to look at. The authors received no outside funding and reported no conflicts of interest.
There are a few problems here that become apparent straight away. First of all, as mentioned, all the data in this study is observational, so no conclusions can be drawn about cause and effect.
Second, May was relatively early in the pandemic, and it’s now November, so we’re missing about half a year’s worth of covid data. On the other hand, the pandemic had already peaked in much of the world by May 1st, and lockdown measures had at that point been in place for months in most countries, so it should be possible to get a pretty good idea about what effect lockdown has in terms of decreasing covid deaths, even using only the data available up to May 1st.
Third, the analysis builds on publicly available data, often provided by different governments themselves, with widely varying levels of trustworthiness, and with different ways of classifying things. As an example, data from Sweden is infinitely more reliable than data from China. And while certain countries have used quite inclusive criteria when deciding whether someone has died of covid or not, other countries have been much more strict. The countries with stricter definitions will tend to have lower covid death rates than the countries with more generous definitions. This lack of homogeneity in how things are defined can make it harder to see real patterns.
Fourth, the reseachers who put this study together gathered an enormous amount of data, pretty much everything they could think of under the sun that might in some way correlate with covid statistics. That means that this study amounts to “data trawling”, in other words, going through every relationship imaginable without any a priori hypothesis in order to see which relationships end up being statistically significant. When you do this, you’re supposed to set stricter limits than you normally would for what you consider to be statistically significant results. They didn’t do this. We’re going to discuss this problem in more detail later in the article.
Before we get in to the results, I’ll just mention one more thing. The results are presented as relative risks (not absolute risks), which tends to make results look more impressive than they really are, and the statistical significance level is presented in the form of confidence intervals, not p-values (not a problem in itself, just a different way of presenting data). If you haven’t already done so, I strongly recommend you read my guide to scientific method before reading further, in order to make sure you understand all the terms used and gain maximal value from the content. Anyway, let’s look at the results.
The factors that most strongly predicted the number of people who died of covid in a country were rate of obesity, average age, and level of income disparity. Each percentage point increase in the rate of obesity resulted in a 12% increase in covid deaths. Each additional average year of age in the population increased covid deaths by 10% . On the opposite end of the spectrum, each point in the direction of greater equality on the gini-coefficient (a scale used to determine how evenly resources are distributed across a population) resulted in a 12% decrease in covid deaths. All these results were statistically significant.
Another factor that had an effect that was significant, but more weakly so, was smoking. Each percentage point increase in the number of smokers in a population was correlated with a 3% decrease in covid deaths.
Ok, let’s get to the most important thing, which the authors seem to have tried to hide, because they make so little mention of it. Lockdown and covid deaths. The authors found no correlation whatsoever between severity of lockdown and number of covid deaths. And they didn’t find any correlation between border closures and covid deaths either. And there was no correlation between mass testing and covid deaths either, for that matter. Basically, nothing that various world governments have done to combat covid seems to have had any effect whatsoever on the number of deaths.
We’re going to come back to this incredible fact in a little bit, but first we’re going to go off on a little tangent. As mentioned, the researchers didn’t correct for the fact that they were looking at a ton of different relationships, rather than just one single relationship between two variables. As I have discussed previously in my article on scientific method, the more relationships you look at, the more strictly you have to set the cut-off for statistical significance, since you will otherwise just by chance get a lot of relationships that seem significant but aren’t.
If you set a p-value of 0,05 (5% probability that a significant relationship was seen in a study even though there isn’t one in the real world), then one in twenty relationships you look at will be statistically significant just by chance. The 5% cut-off is intended to be used when looking at a single relationship, not when looking at multiple relationships. Now, in this study, the authors used confidence intervals instead of p-values, but that doesn’t change anything. A 95% confidence interval is equivalent to a p-value of 0,05, and so the same rules apply.
When you look at multiple relationships at the same time, you are supposed to correct for it. One way to correct is by using a method called the Bonferoni correction formula. This formula is very simple to understand. Say you have a p-value of 0,05 when looking at one relationship (the standard p-value in medical science). If you instead look at two relationships, you divide your p-value by two, thus getting a new p-value for significance of 0,025. If you are looking at ten relationships, you divide by ten, thus getting a new p-value of 0,005.
The authors who performed this study used a 95% confidence interval, as though they were only looking at one relationship between two variables. But they were in fact looking at a ton of variables (they never even specify how many) and a huge number of relationships, so they should have set their confidence interval much more widely.
They did have some results that they claimed were statistically significant, which I haven’t bothered to mention yet, because they’re certainly not significant after statistical correction.
For example, the authors claim a significant correlation between the Gross Domestic Product and covid deaths (relative risk 1,03, 95% confidence interval 1,00 to 1,06), and a significant correlation between the number of nurses per million population and covid deaths (relative risk 0,99, 95% confidence interval 0,99 to 1,00). But if you adjust, as they should have done, for looking at a large number of variables, then there is no way these results would still have been statistically significant. Sorry nurses.
So, what can we conclude from all this?
First of all, lockdowns do not seem to reduce the number of covid deaths in a country. Oops. Based on this data, if you want to decrease the number of covid deaths, you should encourage more people to start smoking, and possibly also start a communist revolution, to equalize wealth as far as possible.
Just kidding. As I’ve mentioned, the data is observational, so we can’t say anything about causality. What we can say from this is that lockdowns don’t seem to work – if they have any effect at all, it is too weak to be noticeable at a population level.
The other important finding from this study, from my perspective, is the strong link between obesity and risk of dying from covid. We can’t say that obesity in itself increases risk of dying – people who are obese have so many different biological systems malfunctioning at the same time that it’s impossible to say whether obesity is the cause of increased risk of death or just a marker of poor health in general.
Regardless, obesity is the strongest covid risk factor that we can do something about. And even if it isn’t the obesity itself that kills people, when we fix the obesity, we also fix the many derangements in metabolism and immune function that go along with it. So it is reasonable to think that efforts to decrease the rate of obesity in the population would decrease the number of people dying of covid. That is where we should be putting our efforts as a society right now – making people healthier so that their bodies are able to fight off covid (and cancer, and heart disease, and dementia, and all the other things that preferentially kill people with sub-optimal health).
A UK intelligence unit, known as the Government Communications Headquarters (GCHQ), has been authorised to conduct cyber operations to tackle the spread of anti-vaccine propaganda online, The Times reported citing an anonymous government source. According to the newspaper, the government increasingly views anti-vaxxers as a new priority because of the upcoming registration of domestically-developed vaccines against the coronavirus.
Apart from GCHQ, a secretive UK Army unit within the 77th Brigade specialising in information warfare will be taking part in the efforts “to quash rumours about misinformation” related to the COVID-19 vaccines, General Sir Nick Carter confirmed to The Times.
The newspaper’s source claims that GCHQ will be using the same toolkit it utilised to combat Daesh and its propaganda and recruitment efforts. The toolkit includes ways of taking down undesired content and conducting cyber attacks against the cyberactors behind it, for example by encrypting the perpetrators’ computer data, The Times added.
“GCHQ has been told to take out anti-vaxxers online and on social media. There are ways they have used to monitor and disrupt terrorist propaganda”, the anonymous source claimed.
However, GCHQ will not be able to use its tools against everyone online because its authority only extends to dealing with [alleged] state cyber actors and the content created by them, the newspaper reported citing another anonymous government source.
Russia as Main target for UK Intelligence Cyber Operations?
The British newspaper claims Russia will be the GCHQ’s prime target, citing its own investigation into the country’s alleged ties to the surge of internet memes questioning the safety of the vaccine developed by Oxford University in concert with AstraZeneca. The said investigation was based on a trove of documents and images provided by an anonymous source, who claimed to be part of an alleged propaganda effort purportedly seeking to hurt the image of the British vaccine. The Times, however, admitted in its article that it could not directly link the alleged social media campaign, targeting only the UK vaccine, with the Kremlin.
According to the newspaper, the alleged campaign against the AstraZeneca/Oxford vaccine started after the head of the Russian Direct Investment Fund (RDIF) that developed Sputnik V, Kirill Dmitriev, called the UK-developed medicine a “monkey vaccine” on several occasions. Dmitriev referred to the vaccine’s usage of a monkey virus as a vector to deliver the COVID-19 material needed to form immunity. He did not directly call the drug dangerous or ineffective, but noted that the use of human adenoviruses was more reliable, as their influence on the human body is better understood.
Dmitriev’s use of the term “monkey vaccine” prompted the emergence of numerous internet memes, baselessly alleging that the British drug would be turning recipients into monkey-like creatures or otherwise negatively affecting patients’ health. The head of RDIF later denounced the use of his words to besmirch the UK-developed vaccine, but defended his concerns over the possibility of its long-term side effects.
Paris Climate ‘Accord’ FOIA Case: State Dept. Releases, Withholds Parts of Memo to Sec. John Kerry Requesting Authority to Sign Paris Agreement
It appears possible that, come January, the United States will rejoin the 2015 Paris climate agreement, committing to adopt the “Green New Deal” agenda (now rebranded for political purposes as “Net Zero”). This will not be accomplished by Senate ratification, but by the ‘pen and a phone’ approach first used by President Obama to claim U.S. “ratification” of what is on its face and by its history a treaty, requiring approval instead by a two-thirds Senate vote.
A document released last week by the State Department, in Freedom of Information Act litigation by the transparency group Energy Policy Advocates, includes a reminder of one consequence of this for America, should it occur: claiming to “re-join” the Paris climate treaty will immediately subject U.S. energy policy — and thereby economic and to some extent trade policy — to a UN “climateconciliation commission”.
Already, as the United Kingdom has shown, developed nations’ courts can be expected to cite the Paris climate treaty in blocking infrastructure development. The UK’s Court of Appeal ruled earlier this year that Heathrow Airport cannot be expanded because that would violate the UK’s ‘net zero’ commitment under Paris.
Then, Canada offered a reminder how progressive politicians will raise taxes in the name of complying with Paris: In Ottawa, “The parliamentary budget officer says the federal carbon tax would have to rise over the coming years if the country is to meet emission-reduction targets under the Paris climate accord.”
Now we are reminded that the U.S. can also expect a forum for antagonistic nations to bring their complaints about U.S. policy and claims of non-compliance with Paris’s required “Net Zero” agenda for resolution.
This might be one of the reasons that avoiding a Senate vote on Paris was a key objective of the Obama administration, which stated in August 2015 before there ever was even Paris text, that it would not be a “treaty”. This was the lesson learned from the U.S. Senate’s refusal to consider the 1997 Kyoto treaty: If the Senate votes on it, its details would be debated, and defeated.
That objective of an end-run around the U.S. Constitution’s process was shared by European nations: the French climate change ambassador to the U.N. and President of the Paris COP, Laurence Tubiana and Laurent Fabius, respectively, both openly admitted.
Yet, those same countries treated Paris as a treaty for their own ratification purposes. This cavalier approach to the Constitution in the Obama years makes it easy to forget the U.S. supposedly has the more stringent system for joining international entanglements.
Instead, the Obama team showed what one Senate Foreign Relations Committee lawyer decried as a “disturbing contempt for the Senate’s constitutional rights and responsibilities” by circumventing its constitutional treaty role on Paris. Unfortunately, the institution shrunk from a constitutional fight, and all parties spoke as if calling Paris an “accord” instead carried weight — though the the Kyoto Protocol was alternatelycalled the “KyotoAccord” and, yes, was still a treaty.
This brings us to the newly released (in part) memo — “Request for Authority to Sign and Join the Paris Agreement, Adopted under the 1992 UN Framework Convention on Climate Change” [UNFCCC] — reaffirming that Paris is the result of “a 2011 negotiating mandate (the “Durban Platform”)”. The Durban “mandate” was to “adopt…a protocol, another legal instrument or an agreed outcome with legal force at the twenty-first session of the Conference of the Parties and for it to come into effect and be implemented from 2020”.
That of course is Paris, the crushing provisions of which are found in Article 4, emission reduction promises. Art. 4.3 requires that the U.S. revisit and tighten its reduction promises every five years. That would cleverly make this the climate treaty…sorry, “accord”… to end all climate treaties. It commits the U.S. to ever greater “climate” policy restrictions, every five years, in perpetuity.
Pull this off and there will never be the threat again of facing the tyranny of the Constitution’s requirement of popular approval.
Political rhetoric aside, nothing in Paris’s terms says this provision is legally binding, but no that one over there isn’t. Instead, Paris was merely sold to and promoted by much of the press with the claim that Paris contains “a mix of legally binding and not legally binding provisions”.
As we have seen already in the UK/Heathrow Airport case, that did not last, as it was not intended to. Lawyers and courts have already begun to see to something of which Americans should be reminded, including that you can have promises of massive infrastructure spending, or you can have the Paris climate pact, but you can’t have them both.
And it won’t just be courts. Recall, first, that the Paris agreement as originally circulatedcontained a climate tribunal, or court. This was dropped after being noticed outside of polite circles. Nonetheless, the recently released if still heavily redacted memo reminds us that U.S. compliance with the legally binding here but maybe not over there Paris obligations is subject to the terms of that 1992 agreement, ratified by the U.S. Senate on the condition that it was and remained non-binding (again, stated nowhere in its terms).
UNFCCC declares, in Art. 14, “Settlement of Dispute”, that:
“5. … if after twelve months following notification by one Party to another that a dispute exists between them, the Parties concerned have not been able to settle their dispute through the means mentioned in paragraph 1 above, the dispute shall be submitted, at the request of any of the parties to the dispute, to conciliation.
6. A conciliation commission shall be created upon the request of one of the parties to the
dispute. The commission shall be composed of an equal number of members appointed by each party concerned and a chairman chosen jointly by the members appointed by each party. The commission shall render a recommendatory award, which the parties shall consider in good faith.”
This language governs U.S. compliance with the Paris climate “accord”. It is not open to dispute that any U.S. president who claims to “re-join” the Paris climate treaty will subject US energy policy — and thereby the U.S. economy — to a UN climate “conciliation commission”.
Paris requires, and mandates the U.S. revisit and tighten “Green New Deal”-style policies every five years. This is among the many reasons why the Paris climate agreement is a treaty, and also why it never would have been ratified. However, very soon, Americans may nonetheless be subject to its long-envisioned climate court.
In a reply to our piece “Welcome to Covidworld”, Ben Bramble engages in precisely the sort of thinking that we raised concerns about. He suggests that we are mistaken in comparing harms done by lockdowns and other measures to harms caused by the virus. Instead, we ought to have weighed up the costs of lockdowns against what would have happened without them.
Bramble’s case hinges on a counterfactual claim: in the absence of lockdowns, the virus would have inflicted much more harm than it has done. The cost of not locking down would, he says, have been “mind-bogglingly great”.
What could be wrong with Bramble’s claim? First of all, his use of the term “lockdown” is insufficiently discerning. Lockdown is not a simple, straightforward policy measure that took the same form in every country. There are, for instance, important differences between early and late lockdowns. Australia and New Zealand both locked down early and suppressed the virus.
Setting aside the issue of whether or not the actions taken by these countries are morally justifiable, it remains to be seen whether or not this is a success story. If a highly effective vaccine is not forthcoming, both countries will face the painful options of cutting themselves off from the rest of the world indefinitely, having strict lockdowns whenever the virus reappears, or eventually succumbing to the virus, none of which amount to success.
However, the current UK situation is very different. Given where we are now, nobody is claiming that this second lockdown or any future UK lockdowns will be able to suppress the virus here. It is too well established for that. Rather, the stated aims have been to buy us some time until a vaccine arrives and, most recently, to ensure that the NHS is not overwhelmed. In evaluating the effectiveness and appropriateness of such policy measures, it will not do to make sweeping claims about the effectiveness of lockdowns in general. When considering interventions so extreme and destructive, we need to proceed more carefully.
Bramble simply accepts that lockdowns in general work. He does not specify exactly what it would be for a late lockdown to work, when the goal is no longer complete suppression. Presumably, the relevant criteria will include reducing hospitalizations and deaths due to Covid-19, during the lockdown and in the longer term as well. But where is the evidence that lockdowns generally have this effect? Bramble doesn’t provide any. Maybe he thinks it’s just obvious that they achieve this, but it really isn’t.
A strict lockdown in Peru is associated with one of the highest Covid-19 death tolls in the world (currently recorded as 1,047 people per 1 million of the population). Other countries that have resorted to exceptionally long and strict lockdowns, such as Argentina, have also fared badly. One could, of course, run Bramble’s counterfactual here: it would have been even worse for these countries had they not locked down. But where is the evidence for that? Indeed, what would even count as evidence?
It would be intellectually and morally unacceptable to make the pro-lockdown position unfalsifiable by always insisting on the following: (1) where cases drop after a lockdown was introduced, it must be the lockdown that achieved this; (2) where cases rise after a lockdown was introduced, it would certainly have been even worse without the lockdown; (3) if other countries, such as Sweden, adopt less extreme approaches than us and fare better or at least no worse, this must be due to other differences between the two countries – the Swedish strategy would never have worked here.
So, how do we go about evaluating the effectiveness of lockdowns? Where is the evidence that the virus ultimately causes far more deaths in the absence of extreme social restrictions? Where are those countries that followed a different course from countries like the UK (which locked down, but did not suppress the virus) and now have higher death tolls than us? By simply assuming that his counterfactual claim is true, Bramble illustrates our worry that lockdowns risk becoming an unfalsifiable article of faith. In fact, he even asserts that “the science on this is beyond question”. Is it really? If so, all the disease modelers who have made dire predictions concerning the current UK situation will be delighted to hear that their work will be forever immune from critique, even if it turns out that their models have little bearing on reality. And, in any case, none of them would endorse Bramble’s exaggerated claim that, without a lockdown, there would have been “many millions of deaths” in countries such as the UK.
In fact, much about the behaviour of this virus remains unclear, including how the infection rate is influenced by growing immunity within a population. There is no single, homogeneous entity called “the science”. Rather, there are many different and often conflicting perspectives, theories, and claims. Furthermore, this is a complicated, fast-changing situation that impacts on all aspects of human society. Relevant expertise thus encompasses a wide range of academic disciplines and areas of practice. Philosophers should not simply defer to “the experts”; they also have plenty of relevant expertise themselves.
What we do know is that lockdowns are immensely damaging in so many ways. This second UK lockdown will further disrupt the social and emotional development of our children, cause a substantial rise in severe mental health problems, force many elderly people to live out the final weeks and perhaps months of their lives in loneliness and misery, exacerbate and prolong the pain of bereavement by depriving people of interpersonal and social interactions that shape and regulate grief, destroy livelihoods and risk mass unemployment, increase regional social and economic inequalities, reduce the life-opportunities of young people while saddling them with an ever-growing mountain of debt to pay off, suspend much of what gives our lives meaning, deprive people of countless precious, irreplaceable life-moments, and cause deaths due to the numerous resulting impacts on people’s health.
However, the true extent of certain harms, such as the long-term effects of sustained lockdown measures on children’s development, may not become fully clear for some time.
Others have similarly warned that policy makers are paying insufficient attention to these growing costs. For instance, an open letter by psychologists, which appeared on 1 November, spells out the widespread and damaging psychological effects of continuing restrictions, including the harms done to children. Similarly, an article published in the British Medical Journalon 2 November raises the concern that the “collateral damage” caused by public health interventions has “yet to be considered systematically”. Others have drawn attention to the global costs of national lockdowns. For instance, the charity Oxfam has stated that, by the end of this year, over 12,000 people could be starving to death every day due the global impact of national-level responses to Covid-19.
Bramble observes that the orthodox view has in fact been subjected to critical scrutiny. But the problem is that – in the UK, at least – alternative perspectives have had little influence on the processes of recommending, making, and implementing policy decisions. And we worry that this may be partly because of blinkered and inflexible attitudes that are widely held. People are often very quick to dismiss or express moral disapproval of dissenting voices. However, those who confidently endorse lockdowns with an air of moral authority also need to acknowledge the full extent of the harms these measures have caused, are causing, and are likely to cause. Furthermore, explicit and sufficiently specific criteria should be supplied for determining the effectiveness of any proposed lockdown, accompanied by convincing evidence to show that it is very likely to achieve its intended effects.
Instead of pursuing such a path, Bramble speculates that our own concerns originate in cognitive impairments caused by our distressing experiences of lockdown. This is the kind of response that motivated our earlier account of “Covidworld”, a simplified, virus-centric reality where various norms of reason, scientific enquiry, and moral conduct have ceased to apply.
All the media have banners and counters and big red numbers of the front page. They proclaim the “new daily cases”.
For example, today, it was reported that Spain has 22,000 new cases, Italy has 37,000, the UK 24,000 and the US 116,000. They are ubiquitously called “new daily cases”.
It wasn’t until I was working on my rebuttal of Moon of Alabama that I realised so many people take this statement literally. They shouldn’t, it is incorrect in almost every respect. To be clear here – “new daily cases” are probably not new, they certainly didn’t all happen in one day and they definitely aren’t “cases”.
Let’s start with the word “new” – They are almost certainly not “new cases”. Today isn’t the day they got infected, today is the day their test results came back. The may have been infected a week ago, or a month, or 6 months.(Or, indeed, never).
Just because Person A got tested on Monday, and Person B on a Tuesday does not mean B is a newer case than A.
We don’t have any idea if more people are getting infected, we only know we are testing more. Charting them as “new” means you can make the scary red line go up, but that is mathematically incorrect, and intellectually dishonest.
Secondly, the vast majority of “cases” reported are not actually “cases”.
Classically speaking, a “case” of a disease is someone who displays symptoms. There is a huge difference between being infected, and being a “case”. That’s why infection fatality rate (IFR) and case fatality rate (CFR) are two different numbers.
The “new daily cases” are none of the above. The “second wave” is likely the result of increased testing. The more people you test, the more “infections” you will find, (especially when your test has a known risk of false positives).
If you increase the number of tests you run, you will increase the number of infections you find. That is not a disease spreading.
In the spring the UK was testing 10,000s of people per day. As of last week, they claim to be testing half a million. From estimated false positive rates alone, that’s between 4000 and 20,000 “new cases” per day.
An analogy:
If you move a piece of furniture in your living room and find a spider underneath it on Saturday, and then the next day you move twenty items of furniture, and find 10 spiders it would be absolutely crazy to say there was 900% daily increase in spiders or that spiders are “increasing exponentially”.
Those spiders were probably there yesterday. You just weren’t looking for them.
…and if you hadn’t gone out of your way to find them, would you ever have known they were even there?
Lost in this whole pandemic hysteria are some key considerations that when carefully analyzed place the whole COVID-19 narrative in a highly questionable light. The gatekeepers of information dissemination are manufacturing consent at an alarming rate, but their fatigue is setting in, and their masks are falling off. What better, albeit unlikely, source to go for some much needed illumination than the New York Times ?
During a considerably quieter time, back in 2007, the New York Times featured a very interesting exposé on molecular diagnostic testing — specifically, the inadequacy of the polymerase chain reaction (PCR) test in achieving reliable results. The most significant concern highlighted in the Times report is how molecular tests, most notably the PCR, are highly sensitive and prone to false positives. At the center of the controversy was a potential outbreak in a hospital in New Hampshire that proved to be nothing more than “ordinary respiratory diseases like the common cold.” Unfortunately, the results wrought by the PCR told a different story.
Thankfully, a faux epidemic was avoided but not before thousands of workers were furloughed and given antibiotics and ultimately a vaccine, and hospital beds (including some in intensive care) were taken out of commission. Eight months later, what was thought to be an epidemic was deemed a non-malicious hoax. The culprit? According to “epidemiologists and infectious disease specialists … too much faith in a quick and highly sensitive molecular test … led them astray.” At the time, such tests were “coming into increasing use” as maybe “the only way to get a quick answer in diagnosing diseases like … SARS, and deciding whether an epidemic is under way.”
Nevertheless, today, the PCR test is considered the gold standard of molecular diagnostics, most notably in the diagnosis of COVID-19. However, a closer analysis reveals that the PCR has actually been pretty spotty and that false positives abound. Thankfully, the New York Times is once again on the case.
“Your Coronavirus Test Is Positive; Maybe It Shouldn’t Be,” according to NYT reporter Apoorva Mandavilli. Essentially, positive results are getting tossed around way too frequently. Rather, they should probably be reserved for individuals with “greater viral load.” So how have they’ve been doing it all this time you ask?
“The PCR test amplifies genetic matter from the virus in cycles; the fewer cycles required, the greater the amount of virus, or viral load, in the sample … the more likely the patient is to be contagious.”
Unfortunately, the “cycle threshold” has been ramped up. What happens when it’s ramped up? Basically, “huge numbers of people who may be carrying relatively insignificant amounts of the virus” are deemed infected. However, the severity of the infection is never quantified, which essentially amounts to a false positive. Their level of contagion is essentially nil.
How are they determining the cycle threshold? If I didn’t suspect that it was based on maximizing the amount of “cases,” I would find the determination pretty arbitrary. More than a few of the professionals on record for Times report appear pretty perplexed on this vital detail which is essentially driving “clinical diagnostics, for public health and policy decision-making.” Considering all that’s at stake and everything that hinges on positive vs negative case tallies, it’s outrageous that these tests would be tweaked in a way that would inflate the positive rate totals and percentages. According to one virologist, “any test with a cycle threshold above 35 is too sensitive.” She went on to to say, “I’m shocked that people would think that 40 could represent a positive.”
Personally, I think the science is just about settled on COVID-19. The conclusion? We’ve been duped!
Ivor Cummins BE(Chem) CEng MIEI PMP completed a Biochemical Engineering degree in 1990. He has since spent over 25 years in corporate technical leadership and management positions. His career specialty has been leading large worldwide teams in complex problem-solving activity.
Since 2012 Ivor has been intensively researching the root causes of modern chronic disease. A particular focus has been on cardiovascular disease, diabetes and obesity. He shares his research insights at public speaking engagements around the world, revealing the key nutritional and lifestyle interventions which will deliver excellent health and personal productivity. He has presented on heart disease primary root causes at the British Association of Cardiovascular Prevention and Rehabilitation (BACPR). He has also debated Irish professors of medicine on stage, at the annual conference of the Irish National Institute of Preventative Cardiology (NIPC). Since March 2020, Ivor has dedicated his analytical and biochemical expertise to deep and revealing analysis of the Covid19 pandemic situation.
Yesterday NASA’s Jet Propulsion Laboratory published “The Anatomy of Glacial Ice Loss.” For the most part it’s an interesting, though not particularly revolutionary, discussion of the various forces that add to and subtract from glacial ice. Nothing wrong with that.
But its authors took the opportunity to insert a poison pill, a little bit of fearmongering, in a video caption:
Did you catch that little trick? “Combined, the two regions also contain enough ice, that if it were to melt all at once, would raise sea levels by nearly 215 feet ….”
Well, yes, but at what rate is the ice from the two regions melting, and at what rate can we, with any confidence, predict they’ll continue to melt, and over what period of time?
There is absolutely no chance of their melting “all at once”—barring, I suppose, Earth’s collision with some enormous asteroid that sends Earth careening into the Sun!
So, how fast is the ice melting?
For Greenland, about 0.1% of its ice mass per decade—1 percent per century.
For Antarctica, about 0.0045% per decade—1% in 2,200 years.
Combined, those contribute to sea-level rise of about 1 mm per year, i.e., 3.94 inches per century.
So, if the actual rate is about 3.94 inches (0.3283 foot) per century, how long would it take to raise sea level by 215 feet? The answer: 215 ft. / 0.3283 ft. per century = 654.889 centuries, or 65,488.9 years.
To be fair, glacial melt from Greenland and Antarctica isn’t the only contributor to sea-level rise. Thermal expansion and other factors also contribute, and some estimates put annual sea-level rise at around 11.81 inches per century, or about 3 times the rate I posited above.
So, let’s redo the calculations. How long, at that rate, would it take to raise sea level by 215 feet? A mere 21,829 years.
Now tell me, if JPL had made that clear, would anyone have taken seriously its saying that this makes “the study and understanding of [the melt in the two glacial regions] … crucial to our near-term adaptability,” or even to “our long term survival”?
No doubt the study is interesting. But it’s certainly not “crucial to our near-term adaptability” or “our long-term survival.”
Is there any reason to think humanity couldn’t survive a 215-foot rise in sea level spread over 21,,829 years, let alone 65,000? And if we define “near-term” as, say, 100 years, or 500, is there any reason to think our “adaptability” would be seriously threatened by 11.81 inches of sea-level rise in a century, let alone 3.94 inches? Or by 4.92 feet in five centuries, let alone 1.64 feet?
And for that matter, what reason have we to think this rate of glacial melt will continue that long into the future? We’re in the midst of a pleasant interglacial period now, but in terms of ice-age cycles, we’re due for the onset of another before long (perhaps in the next few centuries to a millennium?).
Joining me today is a Dr. Sterling Simpson MD, a double boarded pulmonary specialist here to discuss his dissenting views on numerous topics of paramount importance, each of which we have discussed at length here at The Last American Vagabond, and all surrounding the COVID-19 scandal. His professional opinions, despite being deemed “controversial,” are currently supported by countless experts and medical professionals around the world. My objective today as the host of this interview is to give you an opportunity to listen to these medical opinions that the entirety of MSM are actively hiding from you, and which you have every right to hear. As always, listen, think, and come to your own conclusions.
Want to send a check to support TLAV, or just words of encouragement?
5 NIH studies from 2004-2020 all finding verifiable health effects from wearing a face mask, including scientifically verified reduction is blood oxygen level:
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Google News today is promoting articles (see the Google-promoted PhysOrg article here, for example) about a speculative World Bank “study” claiming climate change is threatening crop production in the Middle East. The World Bank study is full of speculation but short on facts. Real-world data show crop yields per acre and total crop production are consistently and dramatically rising in each of the Middle East countries examined by the World Bank study.
In its study, titled “Water in the Balance,” the World Bank says, “[w]hile information about water scarcity at present and in the future is available there is little knowledge of what this increasing scarcity means for Middle Eastern … food security. Agriculture will suffer because of climate change and water scarcity….”
In particular the World Bank asserts water scarcity caused by climate change will reduce farm production in Iran, Iraq, Jordan, Lebanon, Syria, and Turkey. The available evidence strongly suggests that will not happen.
Had the study’s authors examined real-world data concerning crop production in the Middle Eastern countries, they would have found, even amidst substantial strife in the region, crop yields and overall production have increased dramatically. More food is being produced even as thousands of acres of agricultural lands have been abandoned during regional conflicts.
Data from the U.N. Food and Agriculture Organization show during the period of modest warming since 1989:
It is clearly good news – and not a climate crisis – that Middle Eastern countries have increased crop production despite the fact that many of them have been embroiled in internal political strife, outright civil warfare, and external conflicts. That good news is ignored in the World Bank’s doom-and-gloom report.
Global warming lengthens growing seasons, reduces frost events, and makes more land suitable for crop production. Also, carbon dioxide is an aerial fertilizer for plant life. In addition, crops also use water more efficiently under conditions of higher carbon dioxide, losing less water to transpiration. The latter fact should have allayed the World Bank’s concern about climate change induced water shortages leading to crop failure.
The benefits of more atmospheric carbon dioxide and a modestly warming world have resulted in 17 percent more food being available per person today than there was 30 years ago, even as the number of people has grown by billions. Indeed, the last 20 years have seen the largest decline in hunger, malnutrition, and starvation in human history.
Sorry, World Bank, Google, and PhysOrg, but that does not equate to a climate crisis.
H. Sterling Burnett, Ph.D. is managing editor of Environment & Climate News and a research fellow for environment and energy policy at The Heartland Institute. Burnett worked at the National Center for Policy Analysis for 18 years, most recently as a senior fellow in charge of NCPA’s environmental policy program. He has held various positions in professional and public policy organizations, including serving as a member of the Environment and Natural Resources Task Force in the Texas Comptroller’s e-Texas commission.
No doubt this will be wheeled out at the end of the year by the Met Office to bolster its “extreme weather” propaganda. But was it really the wettest day? How do we know?
Quite simply, we don’t, because the Met Office have never published a database of UK daily rainfall. Instead we are expected to take their word for it. Would you trust a company claiming that it had just made record profits, when it had never published any accounts? Of course not.
We also know that the Met Office has recently included several high altitude sites in its rainfall database, which have inevitably skewed upwards rainfall totals.
However, although they do not publish UK daily rainfall data, we do have daily data from the England & Wales Precipitation Series back to 1931. This series categorically shows that October 3rd was not a record, nor anywhere close.
Rainfall totalled 28.48mm on that day, well below the record of 43.23mm which fell in August 1986. Last month’s “record” was in fact only the tenth wettest day.
Even the England & Wales series is of limited value, as it still only has 90 years of data. There will undoubtedly have been many other extremely wet days earlier.
In fact, as the Met Office admits, the rainfall on 3rd October was not particularly intense anywhere, simply widespread across the whole country.
For instance, Oxfordshire was one of the wettest spots in England, and they had about 60mm that day:
To be fair, Scotland got a real drenching that day, which may have tilted the UK figures up from the England & Wales ones. However, as I have frequently pointed out, Scotland has become wetter in recent decades, but that does not mean that the rest of the UK has.
The Met Office’s Mark McCarthy gives us the usual weasel words:
“We can’t make any definitive statements specifically about the attribution of this particular event on October 3,” said Dr McCarthy.
“There’s a general expectation that under our warming climate, we would expect to see increases in some types of extreme rainfall and rainfall events and we’re expecting to have wetter winters overall, we could expect increases in these types of extremes.”
If what he says is true, we would expect to see a pattern of increasingly extreme wet weather in England & Wales, and not just Scotland. The fact is that there is no such pattern, either in these intense daily events, or for that matter monthly totals.
As Angus McFarlane shows in a 2018 well researched wattsupwiththat.com web post (McFarlane, 2018), some 65% of the peer-reviewed climate papers, that offered an opinion, published between 1965 and 1979 predicted that the global cooling seen at the time would continue. He references and is supported by a Notrickszone post by Kenneth Richard (Richard, 2016).
Attempts to erase the “global cooling scare” from the internet by the notorious William Connolley, who has rewritten 5,428 Wikipedia articles in a vain attempt to change history, failed. As James Delingpole explains in The Telegraph, Connolley systematically turned Wikipedia into a man-made global warming advocacy machine (Delingpole, 2009). He rewrote articles on global warming, the greenhouse effect, climate models and on global cooling. He tried to erase the Medieval Warm Period and the Little Ice Age. In the Wikipedia pages he trashed famous climate scientists who were skeptical of man-made global warming like Richard Lindzen, Fred Singer, Willie Soon and Sallie Baliunas. He also blocked people from correcting his lies.
William Connolley is friends with Michael Mann and his Hockey Team, which includes Phil Jones and Raymond Bradley. He is also a cofounder of the alarmist website Realclimate.org. Obviously, Connolley made sure that Mann and Bradley received glowing praise on Wikipedia until he was fired in 2009 and removed as a Wikipedia administrator (Delingpole, 2009).
We are not surprised that Connolley shows up as a co-author on the peer-reviewed paper, “The Myth of the 1970s Global Cooling Scientific Consensus” in BAMS, written by Thomas Peterson, William Connolley and John Fleck (Peterson, Connolley, & Fleck, 2008). The paper is nonsense and made no difference because facts are stubborn things. That the paper passed peer-review illustrates how corrupt climate science has become. The paper begins with this:
“There was no scientific consensus in the 1970s that the Earth was headed into an imminent ice age. Indeed, the possibility of anthropogenic warming dominated the peer-reviewed literature even then.” (Peterson, Connolley, & Fleck, 2008)
Figure 1. The U.K. Met Office Hadley Centre and the Climatic Research Unit (CRU) at the University of East Anglia global average temperature reconstruction since 1850. It shows, like other reconstructions, global cooling of about 0.09°C (0.16°F) from 1944 to 1977.
The global cooling scare of the 1960s and 1970s did exist, both climate scientists and the public were afraid that the global cooling trend, that began in the 1940s (see Figure 1), would continue and the world would turn very cold, maybe even return to a glacial period like the one that ended about 11,700 years ago at the beginning of the Holocene Epoch.
The Peterson, et al. paper carefully cherry picks 71 papers and claims that only seven papers between 1965 and 1979 disagreed with the “consensus” position that global warming would occur in the future. They found 20 that took a neutral position and 44 that agreed with the global warming consensus. But the world was cooling then and had been since 1944. Kenneth Richard researched this and expanded the time frame to 1960 to 1989. Richard found 285 papers that disagreed with the “consensus” position that global warming would occur in the future (Richard, 2016).
Of these 285 papers, 156 discussed the cooling since 1940 and predicted future cooling. Seven tried to show that CO2 might be causing the cooling. A complete list of papers can be downloaded from Kenneth Richard’s post. The alarmists fudged the numbers to show a 97% consensus that man caused global warming, then they fudged the global cooling consensus in the same way.
Angus McFarlane took the databases created by Kenneth Richard and Peterson, et al., merged them (there were 16 duplicates) and then did an independent search of his own. He found two additional relevant papers that were not already in one of the two databases. Then he eliminated the papers that were outside the original Peterson et al. period of 1965-1979.
McFarlane’s database is smaller than Richard’s and only has 190 relevant papers, but this is 119 more than Peterson, et al. found and it covers the same period. McFarlane’s review of the papers found that 86 predicted future cooling, 58 were neutral, and 46 predicted warming. Of the 86 cooling papers, 30 predicted a possible new “ice age.” Strictly speaking, we are in an ice age, what they mean is a new glacial period where ice advances to a major new maximum extent like 19,000 years ago in the last major glacial maximum. The 86 cooling papers are 45% of the total. If we ignore the neutral papers, like John Cook, et al. did (Cook, et al., 2013) in his 97% consensus study, then cooling papers are 65% of the papers that offered an opinion. Using Cook’s rules, we can comfortably claim there was a global cooling consensus in 1979.
However, once the mid-twentieth century cooling trend reversed and became a warming trend, it did not take long for the “consensus” to reverse as well. The global surface temperature trend changed to warming (about 0.017°C/year as shown in the graph) around 1977, and the peer-reviewed climate papers from 1977-1979 changed to a ratio of 52% warming to 48% cooling, a bare majority of warming papers, ignoring the neutral papers. During the 1980s the papers quickly changed to pro-warming.
The press in the mid-seventies reported that a consensus of climate scientists believed the world was cooling and the cooling would continue (Struck, 2014). Articles on the cooling consensus appeared in Newsweek, Time, the New York Times, and National Geographic. A landmark story by Peter Gwynne in Newsweek April 28, 1975 was typical (Gwynne, 1975). It was entitled “The Cooling World.” In the overheated style of Newsweek, the article begins, “There are ominous signs that the earth’s weather patterns have begun to change dramatically.” Later in the article Gwynne breathlessly explains “… the earth’s climate seems to be cooling down. … and the resulting famines could be catastrophic.” Gwynne’s cited sources include the National Academy of Sciences, Murray Mitchell (NOAA), George Kukla (Columbia University), James McQuigg (NOAA’s Center for Climatic and Environmental Assessment) (Gwynne, 1975).
George Kukla of Columbia University and the Lamont-Doherty Earth Observatory did not change his view of a long-term global cooling trend, like many of his colleagues did. When he sadly passed away May 31, 2014, he still believed that a new massive glacial period would begin in the future, perhaps 5,000 years from now. Javier Vinós, in his blog post on “The next glaciation,” (Vinós, 2018) predicts that the next major glaciation will begin in 1500 to 2500 years. It is fortunate that both predictions are far in the future.
When the next global cooling period begins, as it inevitably will, will climate scientists write more global cooling papers? Why should we believe climate scientists who say the world is warming dangerously now, when just 50 or 60 years ago they were saying it was dangerously cooling? A reasonable question. What direct evidence has arisen that convinced them to reverse course? We had a consensus for cooling when the world was cooling, now we have a consensus for warming and the world is warming. Is that all there is to it? Both are hypotheses, what makes them become facts or theories?
Hypotheses are speculative ideas. A real scientist asks, “Is that so? Tell me why you think that.” A rigorous scientific process must be used to demonstrate why observed events, such as global warming or global cooling, are occurring. To show they are potentially dangerous takes even more work.
Consensus is a political thing. The public forms a consensus opinion, then vote and make laws or rules that reflect the opinion. In science, we first form a hypothesis or idea that explains an observed natural phenomenon, such as warming or cooling. The next step is to attempt to disprove it. If we fail the idea survives. We publish what we did, and others attempt to disprove the idea, if they fail to disprove it, it survives. Once this has gone on long enough, the idea becomes a theory. A scientific theory simply survives, it is never proven, it must always be subject to testing.
We mentioned above that seven of the papers examined by Angus McFarlane and Kenneth Richard suggested that CO2 might be causing global cooling. A good example is Sherwood Idso’s, 1984 paper in the Journal of Climatology. The paper is entitled “What if Increases in Atmospheric CO2 Have an inverse Greenhouse Effect?” (Idso, 1984). Idso speculates that additional CO2 will encourage plants to move into more arid areas, because additional CO2 causes plants to use less water per pound of growth. Idso thinks that this might change Earth’s albedo (reflectivity) in such a way as to lower temperatures. In a similar way, Richard Lindzen and Yong-Sang Choi speculated that additional CO2 would increase humidity in the tropics and thus cloud cover (Lindzen & Choi, 2011). Extremely small changes in average cloud cover can have a large cooling effect during the daytime.
Peter Webster presents an interesting discussion of Sherwood Idso’s work in his Climatic Change paper, “The Carbon Dioxide/Climate Controversy: Some Personal Comments on Two Recent Publications” (Webster, 1984). Besides an interesting discussion of the emotions involved in the human-caused climate change debate, we can see from Webster’s discussion, and Idso’s paper, how little we really know about the impact of additional CO2 in the real world. Tiny changes in Earth’s albedo, whether due to cloud cover or the distribution of plants can make a huge difference.
Empirical estimates of ECS (the change in air temperature due to doubling the CO2 concentration) have never matched theoretical calculations from climate models. The empirical values (like Idso’s or Lindzen and Choi’s) are normally about half of model estimates, and can be negative, like Idso’s. This is likely because the models are missing something and possible future changes in albedo due to changing cloud and plant cover are likely candidates.
By Professor Roger Watson and Dr. Niall McCrae | The Daily Sceptic | November 15, 2021
As scholars at leading British universities over recent decades, we witnessed the replacement of critical thinking and debate by narrative: facts are discrimination and scientific method is imperialism; truth, instead, is derived from ‘progressive’ values. This educational trend may be a major contributory factor to the ease in which society has been inculcated to the Covid ‘new normal’ of masking, testing, and repeated doses of vaccines for a disease of similar risk to severe influenza.
One doesn’t need much critical reasoning to observe the flawed logic of some vaccination enthusiasts, such as people who respond to experiencing any side effects, however debilitating, by saying “at least I know it’s working”, or, after contracting the disease despite their promised inoculation (over 90% effective, according to initial drug company claims), “I’d have been worse off without the jab.” … continue
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