Posts by THHuxleynew

Quote from Shane D.

What changed it for me was the Delta variant. The vaccines do clearly minimize the severity. Looking back 5 years from now, will it be judged as the right decision to embrace the vaccine? Who knows, but I will deal with it if I made a mistake.

My 37 year old son got Bells Palsey from the vaccine (he thinks), which resolved itself before getting his appointment with a Neurologist. I thought he was crazy to have gotten the shot after he was first dignosed, but now, after Delta, I am glad he did.

Bells Palsey is an incredibly rare side effect of vaccination, and the background rate (just getting it spontaneously) is higher. Whereas, if he had heard of that as a possibility, imagining this would be more likely. I don't mean to be dismissive - we all are hypochondriac and if we hear of a possibility of a serious disease tend to self-diagnose that we have it. And given vaccines exercise the immune system there can be all sorts of transient symptoms.

It is still true that risks for children from COVID are low - but they are real, and risks from vaccine are lower.

Quote from Wyttenbach

Loosing a child is always hard. But the overall picture is not different from flu or vaccination that both may produce the same results. The paper also shows a social effect with far more hispanic & black people involved. This most likely leads to situations - small living rooms - where children get a high initial dose.

USA is a dangerous place for people with few money...

Not again...

The overall picture is very different from vaccination. This was 28 children permanently brain damaged or dead. Out of 1695 children hospitalised March - December 2020 (original + also some alpha).

The risk of hospitalisation for children 12-17 is:

As of May 10, 2021, there were 1,606,199 SARS-CoV-2 infections reported among adolescents 12-17 years of age in the United States.¹

During April 2021, children 12-17 years comprised 9% of total SARS-CoV-2 infections reported in the United States¹.

Hospitalization:

COVID-19 Associated Hospitalization Network (COVID-NET), a population-based surveillance system, reports a cumulative hospitalization rate among adolescents aged 12-17 years of 53.1 per 100,000 population as of May 1, 2021, indicating over 13,000 SARS-CoV-2 associated hospitalizations for this age group.²

13,000 / 1,606,199 ~ 8 : 1000. (1%).

Therefore the rate of brain damage or death to children from (original / alpha) COVID is 1% * 1.6% = 160 per 1,000,000 cases.

If we suppose 50% of infections in children will never be detected and not become cases we have a ball park risk of brain damage or death of 80 per million. (Death alone 30 per million).

Here is a more scholarly analysis from the UK (less politics) published as a preprint detailing 12-16 year old vaccine pros and cons (not yet allowed in UK).

Vaccinating adolescents in England: a risk-benefit analysis

The UK JCVI committee recently announced that vaccines would not be offered to all 12-17 year olds, as the potential risks were not outweighed by the benefits.…

osf.io

I like it because it explicitly makes a risk / benefit calculation - which many people do not. And it points out that COVID risks scale with COVID infection rate.

We should assume it to be near 100% with delta.

Here are the figures:

We have explicitly not factored vaccine uptake into our analysis because we only considered direct

risks and benefits to children either with or without vaccination. Thus the risk/benefit calculation

among those vaccinated is not changed by considering vaccine uptake. Were we considering

additional secondary impacts on transmission, vaccine uptake would be a crucial parameter.

We find that in England, if the late July 2021 rates of infection among 12 -17 year olds (1000 per

100,000 per week) continue over 16 weeks, this would lead to 5,100 hospitalisations, 330 admissions

to ICU (with 280 adolescents requiring ventilation), and 40 deaths. Vaccination is estimated to avert

4,590 COVID - 19 hospitalisations, 300 ICU admissions, 250 needing ventilation, and 36 deaths, with

the disbenefit of 160 cases of vaccine - associated myocarditis/pericarditis (see Figure 2A). Even if we

assume all cases of vaccine-associated myocarditis/pericarditis required hospitalisation, vaccination

would still avert 4,430 hospitalisations. For long COVID, vaccination would avert 31,000 (assuming 8%

incidence) or 16,000 (assuming 4% incidence) cases in 12-17 year olds.

The population these figures use is 4,000,000.

COVID:

For a 16% total infection rate over these 4 weeks we have 40 deaths from COVID, 5100 hospitalisations

For a 100% infection rate that scales to 225 deaths, 31875 hospitalisations

Vaccination averts 90% of these deaths

For comparison with the above we have 225 deaths per million infected. I prefer this figure to the much less accurate 30 per million US figure. But we should note that delta death rate may be a bit higher.

Vaccination:

For this same population, we expect 160 cases of vaccine-induced myocarditis/pericarditis. Even if all are hospitalised, that is 30 times smaller than COVID for 16% infection.

No deaths figures because we do not yet have any child deaths from myocarditis/pericarditis induced by vaccine.

The scales are weighted (given 16% chance your 12-17 child gets infected) 10X in favour of the vaccine - and that is for hospitalisation. Note it is only 10X because the vaccine does not prevent all COVID deaths. For vaccinated children the COVID risk is still larger than the vaccine risk.

W will produce some false figure for the deaths caused by COVID vaccines. If anyone here finds that even 10% convincing I will happily examine his figures and show why they are anti-vax lies.

(thus far, he has posted figures which conflate all deaths over a time interval close to vaccination with vaccine-caused deaths)

COVID: 10 dead (and from US ratio of damage to death - another 20 permanently brain damaged) children per 1,000,000

Vaccine: 0 dead - 40 cases of myocarditis/pericarditis per million. If you suppose 10% of these cases leaves permanent damage (high - almost all appear to recover completely and within a few days) that is 4 damage compared with 30 dead or damaged from COVID.

If you go up to 100% COVID infection the equation looks even more weighted in favour of vaccine, because we have 225 deaths and (presumably from US figures) another 300 or so permanently brain damaged.

Caveat - the UK figures, though published, might be a bit flaky. But the equation is so heavily weighted in favour of vaccination, if you want your children to live, that it is understandable that doctors in the UK are annoyed at over-cautious regulators who are waiting for more definite information before allowing vaccination. A lot of children will die while they wait.

THH

Quote from Shane D.

"If you don't see that, after reading the above posts, you could try to explain why. Otherwise I'll view you as posting dishonest rhetoric that you know is false when you back them"

He then replied with "Dishonest rhetoric? FU!!!!!". He was not accusing you of being dishonest. He was reacting to being accused by you.

Thanks Shane. FM1, my apologies for what you understood.

I was quite carefully making my accusation of dishonesty conditional (on you being undeniably dishonest in the future). Not saying you are dishonest now.

Cos I don't understand you endorsing those painfully false posts W keeps on making. I was restricting this to the one specific dishonesty (using UP as evidence that ivermectin works) which has been so clearly refuted. Obviously everyone comes to their own conclusions and I would not accuse anyone of dishonesty just because I disagreed with them.

THH

Quote from Fm1

We have been told by everyone from President Biden on down, to the CDC Director Walenski that “breakthrough infections are rare” – they are not – that this has become a “pandemic of the unvaccinated” – it is not – and most disturbingly and illogically (talking to you Mr. Krugman), that the reason we are not all having dinner parties right now is that the unvaccinated have let the rest of us responsible people down

Well just some comments. Breakthrough infections would be rare if the overall infection rate was small - but the idea that vaccinated people cannot be infected with delta is weird and I agree the US messaging has seemed very out of touch.

It is however a pandemic of the unvaccinated. What distinguishes COVID from Flu? The fact that it is much more dangerous. With vaccination, COVID becomes about as dangerous as a bad starin of Flu - admittedly with some added nasties like long COVID. The US has its problem with hospitals clogging up mostly because so many of the at-risk people in some States have believed political and anti-vax lies and end up in hospital.

Quote from Fm1

But when a person speaks, the screen doesn’t trap the exhaled particles — which just float around it. While the store clerk may avoid an immediate and direct hit, the particles are still in the room, posing a risk to the clerk and others who may inhale the contaminated air.”

We think about this stuff in the UK, running a high COVID infection rate though due to very high at risk vaccination rates not running a high death rate.

Mostly, schools and Unis are putting up CO2 meters everywhere to determine whether ventilation is good enough (CO2 level < 700ppm). Ventilation is the one thing that most helps COVID spread. The UK government says 1000-2000ppm is questionable and should be investigated. But outside CO2 is 400ppm, so 1000ppm, as a limit, is double the best practice limit.

As those who have been following know the UK government is sort of Sweden-lite. The worst of both worlds. Lock people down, get everyone scared, while simultaneously telling them it is freedom day and COVID is over, and being totally diorganised about the little things like ventilation everywhere that would make a big difference,

Masks would be a good idea too - both to stop people breathing directly into others faces - and to reduce a bit the spread in a room.

Quote from Wyttenbach

You seem shocked by the high efficiency of Ivermectin even in pampa states like Peru! And these damn trials use such large sets - millions of people or even hundred millions of people (India) that leaves no way for cheating FUD'ers like you.

Take a beer and calm down and forget that you soon will loose your vaccine protection

Just to remind those people here who have not been paying attention. There is no evidence for ivermectin having high efficiency from any of those ecological studies. the one particularly favoured by W (Uttar Pradesh) turns out totally bust as in my previous post.

And, again to remind people, it is dishonest for W to say that I sound shocked when I exactly said in my last post that I expected it to be possible to show any correlation with ivermectin by cherry-picking specific times and places, given all the random and geographic factors that affect COVID spread. The opposite of shocked. And I linked the relevant maths which W is pretending not to know - but which is really just common sense so I guess most people would not need to be reminded of it.

Now, I have calmed down - and as I promised I'll remind everyone when W is lying. Not always - that would exhaust even me! But enough to preserve the sanity of this thread.

Quote from Fm1

Dishonest rhetoric? FU!!!!!

FM1 - you are getting as bad as W. it don't suit you.

If you think I am in any way dishonest posting here you are an idiot.

A Litany of Problems With p-values | Statistical Thinking

With the many problems that p-values have, and the temptation to "bless" research when the p-value falls below an arbitrary threshold such as 0.05 or 0.005,…

www.fharrell.com

Here is someone who does not like using p-value arguments for anything (he is a Bayesian). But, even if you like p-value testing, which is valid if used correctly, you can check down the list here to see various ways it might go wrong and avoid them.

----------------------------------------------------------------------------------------

In my opinion, null hypothesis testing and p-values have done significant harm to science. The purpose of this note is to catalog the many problems caused by p-values. As readers post new problems in their comments, more will be incorporated into the list, so this is a work in progress.

The American Statistical Association has done a great service by issuing its Statement on Statistical Significance and P-values. Now it’s time to act. To create the needed motivation to change, we need to fully describe the depth of the problem.

It is important to note that no statistical paradigm is perfect. Statisticians should choose paradigms that solve the greatest number of real problems and have the fewest number of faults. This is why I believe that the Bayesian and likelihood paradigms should replace frequentist inference.

Consider an assertion such as “the coin is fair”, “treatment A yields the same blood pressure as treatment B”, “B yields lower blood pressure than A”, or “B lowers blood pressure at least 5mmHg before A.” Consider also a compound assertion such as “A lowers blood pressure by at least 3mmHg and does not raise the risk of stroke.”

A. Problems With Conditioning

1. p-values condition on what is unknown (the assertion of interest; H~0~) and do not condition on what is known (the data).
2. This conditioning does not respect the flow of time and information; p-values are backward probabilities.

B. Indirectness

1. Because of A above, p-values provide only indirect evidence and are problematic as evidence metrics. They are sometimes monotonically related to the evidence (e.g., when the prior distribution is flat) we need but are not properly calibrated for decision making.
2. p-values are used to bring indirect evidence against an assertion but cannot bring evidence in favor of the assertion.
3. As detailed here, the idea of proof by contradiction is a stretch when working with probabilities, so trying to quantify evidence for an assertion by bringing evidence against its complement is on shaky ground.
4. Because of A, p-values are difficult to interpret and very few non-statisticians get it right. The best article on misinterpretations I’ve found is here.

C. Problem Defining the Event Whose Probability is Computed

1. In the continuous data case, the probability of getting a result as extreme as that observed with our sample is zero, so the p-value is the probability of getting a result more extreme than that observed. Is this the correct point of reference?
2. How does more extreme get defined if there are sequential analyses and multiple endpoints or subgroups? For sequential analyses do we consider planned analyses are analyses intended to be run even if they were not?

D. Problems Actually Computing p-values

1. In some discrete data cases, e.g., comparing two proportions, there is tremendous disagreement among statisticians about how p-values should be calculated. In a famous 2x2 table from an ECMO adaptive clinical trial, 13 p-values have been computed from the same data, ranging from 0.001 to 1.0. And many statisticians do not realize that Fisher’s so-called “exact” test is not very accurate in many cases.
2. Outside of binomial, exponential, and normal (with equal variance) and a few other cases, p-values are actually very difficult to compute exactly, and many p-values computed by statisticians are of unknown accuracy (e.g., in logistic regression and mixed effects models). The more non-quadratic the log likelihood function the more problematic this becomes in many cases.
3. One can compute (sometimes requiring simulation) the type-I error of many multi-stage procedures, but actually computing a p-value that can be taken out of context can be quite difficult and sometimes impossible. One example: one can control the false discovery probability (incorrectly usually referred to as a rate), and ad hoc modifications of nominal p-values have been proposed, but these are not necessarily in line with the real definition of a p-value.

E. The Multiplicity Mess

1. Frequentist statistics does not have a recipe or blueprint leading to a unique solution for multiplicity problems, so when many p-values are computed, the way they are penalized for multiple comparisons results in endless arguments. A Bonferroni multiplicity adjustment is consistent with a Bayesian prior distribution specifying that the probability that all null hypotheses are true is a constant no matter how many hypotheses are tested. By contrast, Bayesian inference reflects the facts that P(A ∪ B) ≥ max(P(A), P(B)) and P(A ∩ B) ≤ min(P(A), P(B)) when A and B are assertions about a true effect.
2. There remains controversy over the choice of 1-tailed vs. 2-tailed tests. The 2-tailed test can be thought of as a multiplicity penalty for being potentially excited about either a positive effect or a negative effect of a treatment. But few researchers want to bring evidence that a treatment harms patients; a pharmaceutical company would not seek a licensing claim of harm. So when one computes the probability of obtaining an effect larger than that observed if there is no true effect, why do we too often ignore the sign of the effect and compute the (2-tailed) p-value?
3. Because it is a very difficult problem to compute p-values when the assertion is compound, researchers using frequentist methods do not attempt to provide simultaneous evidence regarding such assertions and instead rely on ad hoc multiplicity adjustments.
4. Because of A1, statistical testing with multiple looks at the data, e.g., in sequential data monitoring, is ad hoc and complex. Scientific flexibility is discouraged. The p-value for an early data look must be adjusted for future looks. The p-value at the final data look must be adjusted for the earlier inconsequential looks. Unblinded sample size re-estimation is another case in point. If the sample size is expanded to gain more information, there is a multiplicity problem and some of the methods commonly used to analyze the final data effectively discount the first wave of subjects. How can that make any scientific sense?
5. Most practitioners of frequentist inference do not understand that multiplicity comes from chances you give data to be extreme, not from chances you give true effects to be present.

F. Problems With Non-Trivial Hypotheses

1. It is difficult to test non-point hypotheses such as “drug A is similar to drug B”.
2. There is no straightforward way to test compound hypotheses coming from logical unions and intersections.

G. Inability to Incorporate Context and Other Information

1. Because extraordinary claims require extraordinary evidence, there is a serious problem with the p-value’s inability to incorporate context or prior evidence. A Bayesian analysis of the existence of ESP would no doubt start with a very skeptical prior that would require extraordinary data to overcome, but the bar for getting a “significant” p-value is fairly low. Frequentist inference has a greater risk for getting the direction of an effect wrong (see here for more).
2. p-values are unable to incorporate outside evidence. As a converse to 1, strong prior beliefs are unable to be handled by p-values, and in some cases the results in a lack of progress. Nate Silver in The Signal and the Noise beautifully details how the conclusion that cigarette smoking causes lung cancer was greatly delayed (with a large negative effect on public health) because scientists (especially Fisher) were caught up in the frequentist way of thinking, dictating that only randomized trial data would yield a valid p-value for testing cause and effect. A Bayesian prior that was very strongly against the belief that smoking was causal is obliterated by the incredibly strong observational data. Only by incorporating prior skepticism could one make a strong conclusion with non-randomized data in the smoking-lung cancer debate.
3. p-values require subjective input from the producer of the data rather than from the consumer of the data.

H. Problems Interpreting and Acting on “Positive” Findings

1. With a large enough sample, a trivial effect can cause an impressively small p-value (statistical significance ≠ clinical significance).
2. Statisticians and subject matter researchers (especially the latter) sought a “seal of approval” for their research by naming a cutoff on what should be considered “statistically significant”, and a cutoff of p=0.05 is most commonly used. Any time there is a threshold there is a motive to game the system, and gaming (p-hacking) is rampant. Hypotheses are exchanged if the original H~0~ is not rejected, subjects are excluded, and because statistical analysis plans are not pre-specified as required in clinical trials and regulatory activities, researchers and their all-too-accommodating statisticians play with the analysis until something “significant” emerges.
3. When the p-value is small, researchers act as though the point estimate of the effect is a population value.
4. When the p-value is small, researchers believe that their conceptual framework has been validated.

I. Problems Interpreting and Acting on “Negative” Findings

1. Because of B2, large p-values are uninformative and do not assist the researcher in decision making (Fisher said that a large p-value means “get more data”).

J. Distortion of Scientific Conclusions

1. Greenwald, Gonzalez, Harris, and Guthrie’s paper Effect sizes and p values: What should be reported and what should be replicated? nicely describes subtle distortions in the scientific research process caused by the usage of null hypotheses:

One of the more important varieties of prejudince against the null hypothesis ... comes about as a consequence of researchers much more identifying their own theoretical predictions with rejections (rather than with acceptances) of the null hypothesis. The consequence is an ego involvement with rejection of the null hypothesis that often leads researchers to interpret null hypothesis rejections as valid confirmations of their theoretical beliefs while interpreting nonrejections as uninformative and possibly the result of flawed mehods.

It is ironic that the bad statistics traditionally used by dishonest big pharma to persuade people there is some merit in dubious drugs are being used in these ecological studies to promote anti-big-pharma ivermectin.

The p-value fallacy.

Simplified: if you take random numbers and test 100 different hypotheses you will typically find 5 or so of them show, on the random data, a statistically significant effect (less than 5% by chance).

Drug companies generate positive tests by running a randomised double-blind trial with a small number of patients, testing 20 different independent and fairly random things, and highlighting the ones that have statistically significant correlations with the drug. Except that p-values cannot be used that way - you have to find the result statistically significant over the null hypothesis, which is that none of the 20 different things show an individual 5% or less likelihood given chance. Used like this, correctly, the fake evidence is not sen as evidence. But it is a mistake very often made to ignore the need for a null hypothesis and take p-value 0.05 or less as proof of causation.

That is obviously wrong. It is often misunderstood and leads to false attribution of causation to correlations. In the case of ecological comparisons such as the ones in Peru there are 100s of different comparisons over different cities, nations, time periods that could be made. Choosing one where ivermectin distribution or not correlates with better or worse COVID infection is child's play.

That is why Gorski is so dismissive of the argument. You could look at his detailed refutation linked above) I have not done that and wonder is it the same as mine - or different. Anyway mine is correct as common sense will tell you. You can find lots and lots of neat example of you look for popular explanations of p-value fallacy.

THH

On ecological studies (I'm tired of always saying the same thing myself - so will let David Gorski do it this time):

Ivermectin is the new hydroxychloroquine, take 5: The Nobel Prize gambit

As regular readers know, I was on vacation last week. In my absence, guest blogger Dr. William Paolo provided an excellent fourth installment for our…

sciencebasedmedicine.org

Lastly, the authors cite data from Peru:

The clinical experience of IVM treatments of COVID-19 in 25 countries extends far beyond the RCT results summarized, yet incomplete tracking and lack of control data exclude most of this for evaluation. The record of nationally authorized such treatments in Peru provides a notable exception [42]. In ten states of Peru, mass IVM treatments of COVID-19 were conducted through a broadside, army-led effort, Mega-Operación Tayta (MOT), that began on different dates in each state. In these MOT states, excess deaths dropped sharply over 30 days from peak deaths by a mean of 74%, in close time conjunction with MOT start date (Figure 1B). In 14 states of Peru having locally administered IVM distributions, the mean reduction in excess deaths over 30 days from peak deaths was 53%, while in Lima, which had minimal IVM distributions during the first wave of the pandemic due to restrictive government policies there, the corresponding 30-day decrease in excess deaths was 25%.
Reductions in excess deaths by state (absolute values) correlated with extent of IVM distribution (maximal-MOT states, moderate-local distributions, and minimal-Lima) with Kendall τb = 0.524, p<0.002, as shown in Figure 1C. Nationwide, excess deaths decreased 14-fold over four months through December 1, 2020. After a restrictive IVM treatment policy was enacted under a new Peruvian president who took office on November 17, however, deaths increased 13-fold over the two months following December 1, through February 1, 2021 (Figure 1A).

Does this sound familiar? It’s the same sort of ecological “analysis” that an astroturf group was doing for hydroxychloroquine last year that I discussed. The methods of this study are equally awful, which is probably why this article, too, is only on a preprint server thus far, and guess what? It’s a study whose first author is David Scheim, a man with no discernable expertise in the sort of complex epidemiology that would be required to make sense of the Peruvian data, while the other author is Juan Chamie of—you guessed it!—the FLCCC. I’ll say about this study the same thing that I said about the HCQ astroturf site: This is all utter rubbish, methods, conclusion, and all, as you will see. It’s so bad that it reminds me of a study by two antivaxxers without any qualifications in epidemiology, Neil Z. Miller and Gary S. Goldman, that tried—and failed—to correlate the number of vaccines in the recommended vaccine schedules of various countries with those countries’ infant mortality rates.

Seriously, this is some really bad stuff. Let’s just put it this way. Ecological studies of the use of a drug are pretty much worthless because it’s impossible to control adequately for other potentially confounding factors or, in the case of a pandemic, the unexpected resurgence of a virus due to new variants, such as what we are seeing in the US due to the delta variant. But COVID-19 cranks do love their “real world evidence,” don’t they?

Quote from Fm1

I told you in March that by September we would see wide vaccine failure, I told you of the rise in cases first in the UK and the fall of cases same in the US. But again you tell me I'm wrong. I don't follow you, W or anyone else. I designed a model that has predicted every aspect of this pandemic from progression to vaccine failure and you all laugh at me. I haven't been wrong yet with the progression. I don't follow Thomas, I lead!!!

I am not telling you that you are wrong over what you have said there (though if you explained precisely your reasoning I might view it is wrong - I don't know. Certainly you have not supported it with evidence here). All i know is that you view COVID R value as influenced by weather - where we agree. But I suspect you rate that influence higher than I would. Still, that is a reasonable debate.

I am saying that W's and RBs ecological comparisons about India and UP as proving ivermectin works are rubbish. Provably so. I've just done that. I've corrected them, in detail, many times. They go on posting the same false numbers, saying this proves ivermectin is an effective treatment (or sometimes prophylactic - the above posts are about low IFR and hence a claim it is a good treatment) and ignoring the refutations which are as clear as your nose.

If you don't see that, after reading the above posts, you could try to explain why. Otherwise I'll view you as posting dishonest rhetoric that you know is false when you back them. You may have a whole load of other ideas I disagree with - I don't see that as dishonest and would never normally accuse anyone of that. But, over this one issue, because it is so clear, RB and W have continued to argue it, and it has been comprehensively refuted. Yes, persisting with it without addressing the refutation is dishonest.

Advancing a Covid-19 vaccine means countering science denial - STAT

Science denial underlies opposition to a Covid-19 vaccine. Overcoming it requires addressing its emotional underpinnings.

www.statnews.com

Written last year but you might notice some of these elements posted here (substitute ivermectin for hydroxychloroquine).

The continued spread of SARS-CoV-2 is a practical demonstration of what happens when science denial supplants evidence-based decision-making at multiple levels of government, from mask mandates to reopening schools mid-pandemic. Denial has led to needless deaths and suffering.

How can denial be identified? In 2009, Pascal Diethelm and Martin McKee defined science denial as employing some or all of five characteristic elements. All five of these have been deployed in the last few months, sometimes by the government. Public health advocates should be ready when they are deployed again.

The first characteristic is the use of conspiracy theories to frame a scientific consensus as the product of a conspiracy of bad actors. A bevy of conspiracy theories concerning SARS-CoV-2 have already been spread on social media. The president has used coronavirus briefings as a platform to share conspiracy theories, such as physicians lying about Covid-19 to hurt his reelection chances. A widely circulated conspiracy video bizarrely claims that SARS-CoV-2 is human-made, and that Bill Gates was involved in distributing it to profit from a future vaccine. Online, anti-vaxxers have begun framing a future coronavirus vaccine as a part of a conspiracy to enforce compulsory vaccination.

Karl Popper, a philosopher of science, described these kinds of conspiracy theories as being like Homer’s conception of the gods’ behavior on Olympus as determining events in day-to-day human life. Conspiracists believe that the actions of secret puppeteers control the impersonal and otherwise unpredictable events in our lives.

Conspiracy theorists are rarely effective in identifying real conspiracies or enacting counter-conspiracies for a simple reason: Things rarely go to plan. Conspiracy theories often hinge on unlikely events and large groups of people successfully keeping secrets for long periods. When real conspiracies are uncovered, it is often because secrets are hard to keep, especially when they require the coordinated actions of hundreds or thousands of people.

The second characteristic of denial is the use of fake experts. These are often credentialed individuals who hold views outside the broader scientific consensus. For example, the president has shared videos of Dr. Stella Immanuel (and several other lab-coat-attired individuals) to promote false claims that hydroxychloroquine is an effective treatment for Covid-19, and that masks do not slow transmission of SARS-CoV-2. Similarly, science denial marginalizes legitimate experts, such as Dr. Anthony Fauci, the head of the National Institute of Allergy and Infectious Diseases, who has become a trusted voice about the pandemic for many Americans.

Fake experts exploit the dual nature of expertise. An expert is someone who is highly skilled and knowledgeable, and is identified as an expert by the perception of skill and knowledge. That perception, created by language, dress, and other accoutrements, is not always accompanied by actual skill. Given the number of people carrying credentials like M.D., R.N., or Ph.D.,” it will always be possible to find “experts” who speak outside of their domains of knowledge, or who create the perception of knowledge without truly possessing it.

A third characteristic of science denial is selectivity. As more and larger studies are published, it has become clearer that hydroxychloroquine is not an effective treatment for Covid-19. But it will always be possible to select weaker elements of research to attempt to discredit a larger body of research, such as those who cherry-pick papers to discredit the use of masks.

Anti-vaxxers have now spent more than two decades selectively reading the scientific literature to cast doubt on vaccines, centering their arguments around ever-smaller minutiae. A prominent 2016 anti-vaccine documentary implied a “cover-up” over a minor disagreement about the interpretation of a statistical study of measles vaccination, premised on a now-retracted reinterpretation of the statistics, which had made a number of errors.

We should expect to see no less than a fully dishonest misinterpretation and mischaracterization of whatever clinical trials are conducted in the lead-up to the approval of a vaccine to prevent Covid-19.

Quote from Wyttenbach

FUD alert: A provaxx fake news site:Advisory Board

SUNIL KHILNANI

Avantha Professor and Director

King's India Institute

Kings College, London

It seems like there are a lot of scientists pro-vax. I wonder why that would be?

But I posted two links. With direct independent journalism in one. It is also not surprising that deaths should be undercounted everywhere in india, and more in UP which is less developed, and also has a hardline government that wants to control information.

Your unsupported statements are not evidence.

Quote from Zeus46

Tables 5 & 6 in any of the monthly xls's:

https://www.ons.gov.uk/peoplep…ingcovid19englandandwales

Thanks Zeuss - yes that is quite helpful because it gives a lower estimate of 20% deaths healthy. We cannot so simply turn this into relative probability without knowing the prevalence of those conditions. Also, those are death certificate mentioned things and obesity is a recognised comorbidity and not included there (and thus invalidates that lower estimate). I remember giving up on that data when trying to disentangle it, but you can see why W's Aryan super-healthy people are probably still quite high risk.

EDIT - I've just noticed - the most common column - influenza and pneumonia - would include most COVID cases since COVID-induced pneumonia is a common cause of death. So all of that row (82 vs 101 no other condition) could also have no comorbidities. There are also missing cases not covered by any of those rows but included in the overall results.

I'd say a good estimate of healthy deaths would therefore be 180/540 = 33%. But, we do not know the fraction of people healthy in the population. And, the question was < 60, not overall. It remains tangled, but an objective person would say from this that yes, COVID hits all of us, healthy and less healthy alike.

THH

FM1 - my comment about knowingly misleading others should perhaps include you, since I believe you take and interest in this stuff, and you liked W's mendacious post above. You know enough not to endorse false statements. Should you not understand whta I have just posted, or disagree with it, please give your reasons - otherwise i can only conclude that you are deliberately endorsing things that you know are misleading. Perhaps, like Shane, you think this is balance? Because i argue better those who stand up to me should be encouraged?

That is a false argument. I argue better in this case because those I am arguing against here are wrong on these oft repeated points. They must at some level know it. And so must you.

Just a comment on when a comment on COVID death rates is probably knowingly misleading you.

Ecological comparisons (death rates between different countries) are incredibly difficult to make. Serious people will not use them as evidence without an enormous amount of extra context, trying to find all of the factors that confound the results, and even then will be cautious.

Case counts nearly always underestimate infections by at least 30% - because asymptomatics are not picked up. In many countries, during epidemic peaks, the underestimate is much higher. In developing countries like rural parts of India it will be very high. Death counts are much more reliable generally, in developed countries, but again some countries with poor or nonexistent health systems will undercount them because they are never tested nor diagnosed in a hospital. India is a known bad example of that, and UP a known bad outlier even in India.

OK, you think. The stats are unreliable but at least undercounted cases might cancel out undercounted deaths. Fair enough - although in that case we have total unreliability where a large error could be in either direction.

It might. But not when a poster uses estimates of infection from seropositivity (rather than cases + 40% asymptomatics) and compares them with these undercounted deaths to get an artificially low result without noting that caveat.

Now, W, RB are both clever enough to understand all this stuff. It has been pointed out before. I'd appreciate it if they both conceded that the naked ecological comparisons they continue to make are worthless. Otherwise I have to suppose they are deliberately trying to mislead others here.

Quote from Wyttenbach

100% THH FUD. No study ever did show this. The real risk protection from vaccines is If 100 die from CoV-19 it will be 99 with vaccines. Exception for age over 75! And sick people.

Vaccines do not really protect people age < 60. It's their health - immune system that already knows how to fight CoV-19. Most people Age < 60 that die from CoV-19 have >= 2 comorbidity! There is "0" protection for the healthy age < 60 if you include the vaccine death risk >= 25/mio or the permanent damage risk being > 250/mio then vaccines are a no go.

You mentioned a study (without linking it.). I was rephrasing what you said the way anyone else would read it. And when challenged about IFR - I answered it precisely.

Re your "most people who die <60 have comorbidities" that may well be true. So what? Most people over 60 have comorbidities. Are you saying we should dismiss the 42% of US adults who are obese as somehow deserving to die? And since the risks for age 60 men are significant, 1% IFR, the fact that they are known less with no comorbidities does not make them zero. I have not found it easy to get the "no comorbidity" data on death rate. Most people do not seem to be only concerned about a select super-healthy few. But, given all we know, it would be very surprising if the risk reduction was greater than a factor of 2. Please link evidence. That gives us 0.5% IFR, which is my fair estimate of your personal risk of death if you are infected with COVID - unless you know you are antibody-positive.

Your comment here beggars belief, and I would not continue to comment except that seemingly a majority of those actively posting here agree with you. I am not one to back off just because what I say is unpopular when I believe I have the right of it. Throughout this thread you have insulted pretty well everyone, made wild unsupported accusations, not admitted error when your statements are shown false, and what for me is worse, you have this view somehow that we should not count people who are not in perfect physical health. That way of thinking is dangerous, as anyone looking at 20th Century Germany can see. All people deserve respect (even you - though based on posting here i'm not finding it easy to give it). It is also personally dangerous to assume that good health saves you from any disease, let alone COVID.

This site should be ashamed of itself to be backing repeated false statements and morally reprehensible attitude. It is not in me to do other than reply with attention and to my best ability post what I think to be true, but you should reflect on your behaviour and preferably say nothing more. Repeated bad behaviour does not become good - nor is it validation that many here are will let let it go as the harmless rantings of an eccentric.

Quote from Wyttenbach

Now look at vaccines! At Least 1% will have had a symptomatic infection... May be you understand it that way....

LOL. Statistics are not your strong point. Let me make it easy for you. That is 0% (of what?) and 1% (of what?). That is right - the two sets of people are not the same - you have not factored in the local infection rate (low). You did not fully read my links - the more recent one noted reinfection rates similar amongst unvaccinated and vaccinated. In addition to the statistical error there is another problem with the comparison - both vaccination and natural immunity will provide less protection against reinfection with delta. The difference for vaccination is well known. That for reinfection has not been so well studied, but must be considered. I don't know how reinfection now compared with breakthrough infection but both are non-zero and they will not be so far apart.

As the saying goes, there are lies, damn lies, and Wyttenbach statistics.

Quote from Wyttenbach

FUD-Alert! The conclusion were made by Israeli scientists - state officials. But our spin doctor knows it better.

Also the conclusion from the MA event is very clear.

Also all papers shows that the immune response from a natural infection is 100x better than from a vaccination as it causes a sterile vaccination. Here vaccines fail for 100%.

As said I will not waste my time with crap studies done by juniors for the fat daddy.

No link. No evidence.

Quote from Wyttenbach

Vaxxine terrorist FUD Alert! India's IVR states have already 70% antibodies. So e.g. 150 mio. people from Uttar Pradesh have been infected so far then the IFR is < 0.015%.

Sigh. I will lay it out for you again. UP being a very young state (youngest in India, which it self is young) we'd expect around 0.1% - 0.2% IFR.

Death rates in UP (and all India) from COVID are vastly undercounted. We do not know by how much:

Death Count In 24 UP Districts 43 Times More Than Official Covid-19 Toll — Article 14

New Delhi: The number of people who died in 24 Uttar Pradesh (UP) districts over nine months to 31 March 2021 was, cumulatively, 43 times higher than the [...]

article-14.com

New Delhi: The number of people who died in 24 Uttar Pradesh (UP) districts over nine months to 31 March 2021 was, cumulatively, 43 times higher than the total official Covid-19 death toll reported from these districts over this period, according to mortality data for India’s most-populous state.

The 24 districts—of 75 in UP—we chose reported the highest number of Covid-19 cases over the months of June, July, August, and October 2020 when the first wave struck, as per various media reports (here, here, & here) and government data tracked by Covid19india.org.

The excess deaths in these districts were between 10 to 335 times higher than the reported Covid-19 death toll between 1 July 2020 and 31 March 2021. The Bharatiya Janata Party has said UP’s chief minister Ajay Singh Bisht, popularly known as Yogi Adityanath “effectively managed” his state’s Covid-19 situation.

All the excess deaths cannot be attributed to Covid-19, and a diversion of health resources could cause other deaths to rise. But the vast divergence in average general deaths and excess deaths over a part of the pandemic calls into question UP’s official Covid-19 death toll of 4,537 by the end of March 2021.

Documents this reporter obtained for Article 14 under the Right to Information Act, 2005, revealed that during the no-pandemic period between 1 July 2019 and 31 March 2020 these 24 districts registered around 178,000 deaths. Over the same period in 2020-2021, deaths increased by 110% to 375,000, an excess of 197,000.

And also:

‘We’re burning pyres all day’: India accused of undercounting deaths

Fears of cover-up as crematoriums record twice the number of Covid fatalities as official death toll

www.theguardian.com

As India battles through one of the world’s deadliest surges of the Covid-19 pandemic, this week India’s health minister Harsh Vardhan insisted that its fatality rate from the disease remained “the lowest in the world”.

It was a statement that jarred with the devastating images and accounts that have flowed out of India in the past fortnight, of hospitals and morgues filled to capacity, people dying on pavements from scarcity of oxygen, and crematoriums and graveyards visibly overflowing with bodies.

India’s official death toll has continued to rise relentlessly. On Saturday, it was another record-breaking day, with 401,993 new cases and 3,523 deaths. Yet health experts widely believe the official daily figures do not come close to reflecting the real number of deaths.

With Covid-19 patients unable to get into hospitals, many have been dying at home, often without ever getting tested. Meanwhile, state governments and local authorities stand accused of rampant miscounting, covering up and obfuscating the true death toll in their states. Over the past month, in the Karnataka city of Bangalore – where case numbers are among the fastest rising in the country – the figure for Covid-related deaths registered in crematoriums was twice the official death toll.

The allegations of a cover-up have been particularly prevalent in Uttar Pradesh, where the state government is controlled by the ruling Bharatiya Janata Party (BJP), and the hardline chief minister, Yogi Adityanath, has insisted that the state has no shortage of oxygen and threatened to prosecute those who “spread panic”. Authorities have denied any cover-ups.

In the city of Muzaffarnagar, in Uttar Pradesh, data collected by the Observer shows a vast discrepancy between the official death toll recorded by the local authority and the accounts given by those who run its crematoriums and graveyards.

According to official statistics, Muzaffarnagar had just 10 Covid deaths over four days in late April. However, Ajay Kumar Agarwal, president of Muzaffarnagar’s city crematorium, said this was not even close to the scale of bodies he was handling.

“In normal times, we were cremating three bodies a day, but in the past 10 days it has increased,” he said. “One day it was 18, another day it was 20, then 22, and one day 25. In the past 10 days, we haven’t had any less than 12 bodies a day– 90% of them corona deaths.”

With only seven pyres in Muzaffarnagar’s city crematorium, Agarwal said they were so overwhelmed they were having to cremate the bodies on open ground, and send some to another crematorium 20 miles away. “The situation here is pathetic,” he said,

Agarwal alleged that “incorrect” figures were being published, and dismissed suggestions that the city had experienced any days this week with no Covid deaths or just two deaths. “The administration does not make the correct death figures public,” he said. “I don’t understand why they’re hiding them. Maybe they don’t want people to panic.”

Sanjay Mittal, at Muzaffarnagar’s only other crematorium, New Mandi, recounted similar scenes. He said he had “never seen such a situation in my life – we are burning pyres from morning till evening”.

According to Mittal, prior to the pandemic, the New Mandi crematorium usually saw five bodies arrive in a day. But on 27 April they received 21 bodies, on 28 April it was 15, and on 29 April it was 18. He could not confirm how many had been Covid-19 positive.

“It is midday and we’ve already had 12 bodies. Who knows how many it will be by the end of the day,” he said on Friday.

A similar recent surge in bodies was also reported by Abdul Quadir, who runs the Muslims cemetery in Muzaffarnagar. “Before corona, we buried two to three bodies a week, but now six to seven bodies arrive every day,” he said. “Only three of these bodies so far have come from hospital, the rest died at home and had not been tested.”

Official government data confirms very low Covid-19 testing rates in Muzaffarnagar; on Tuesday 27 April, no tests were done in the area, while on 29 April, only 561 tests were done, which all came back positive.

Quote from Shane D.

https://www.businessinsider.co…-higher-death-rate-2021-8

The western health care power brokers will never forgive Sweden for doing things their way. In this article they brutalize them by carefully cherry picking which nations to compare them to. Look at all of Europe though, or the world, and even certain states in the US, and they did fairly well.

Shane, I don't know that article is poking holes at Sweden. It is worth remarking that there is no easy way through COVID, and that no-one knows what is for the best (other than the weird anti-science US encouragement of people not to wear masks, not to have vaccination).

I've always seen Sweden's policy as being brave and interesting, with no clarity whether it would be good or bad. That is what they think themselves. And it remains true. When everyone is vaccinated then we can total up the deaths and other damage. Your response however is political - and politics never helps find the best solution. Maybe it is just I am not in US but western care power brokers is not a phrase i understand, nor do (if i interpret it right) I notice any suhc in our part of the west (UK). If the US is riven with politics, and has a broke health system (as Jed's link said - though I imagine there is another side) that is a matter for the US to sort out - and political wrangling, or leaping to conspiracy theories, will not help mending it.

The UK, with its alphabet vaccination mafia, is managing to control COVID infection.

We have a very high rate (mostly amongst younger and unvaccinated people). This week we (England, not UK) are at 1 in 80 people infected - compared with 1 in 75 last week.

This is slightly counterintuitive, because cases are going up - slowly. However the infection figures are community - a random sample of England homes. And cases are not a reliable measure of infections.

I wish I could say that high vaccination rates meant we could control COVID. The test will come when schools return in September.

Compared with the US we are not vaccinating children. We have just recently moved to vaccination for all 16-17 year olds - and rates of vaccination there are still quite low.

All children < 16 are unvaccinated and currently what is driving the infection together with < 24 (not that well vaccinated). Older age groups still get infected but at a lower rate. the very low rate for 70+ is probably because they are being more careful, but they do also have a higher vaccination rate.

Coronavirus (COVID-19) Infection Survey, UK - Office for National Statistics

Quote from Mark U

Given : deaths to date with Coronavirus is 4.42 million and the world population is 7.67 billion.

If the infection fatality rate over the entire population is one percent , and an 'infection' means encountering the virus on one's mucosa,

then (4.42E6/7.67E9)/.01 = just 5.8 percent of the population has encountered the coronavirus on their mucosa over the last year and a half.

Clearly the coronavirus is very shy and doesn't want to transmit and encounter people, let alone encountering a person more than once!

The crazy alternative is that the infection fatality rate is actually much lower than 1 percent.

Display More

The IFR varies depending on population demographics. Obviously. We could work it out for specific countries with good data (e.g. UK). It will probably be lower than 1% but that is from alpha statistics - delta a bit worse.

I actually did some UK calculations a while back based on the ONS infection and death data. Working out infections is difficult though.

Your world calculations don't help because in many countries (e.g. India) death rate is undercounted by a large amount, in addition a lot of the world is low income demographics.

This is as good as it gets in terms of estimates:

https://www.imperial.ac.uk/media/imperial-college/medicine/mrc-gida/2020-10-29-COVID19-Report-34.pdf

The infection fatality ratio (IFR) is a key statistic for estimating the burden of coronavirus disease 2019
(COVID-19) and has been continuously debated throughout the current pandemic. Previous estimates
have relied on data early in the epidemic, or have not fully accounted for uncertainty in serological
test characteristics and delays from onset of infection to seroconversion, death, and antibody waning.
After screening 175 studies, we identified 10 representative antibody surveys to obtain updated
estimates of the IFR using a modelling framework that addresses the limitations listed above. We
inferred serological test specificity from regional variation within serosurveys, which is critical for
correctly estimating the cumulative proportion infected when seroprevalence is still low. We find that
age-specific IFRs follow an approximately log-linear pattern, with the risk of death doubling
approximately every eight years of age. Using these age-specific estimates, we estimate the overall
IFR in a typical low-income country, with a population structure skewed towards younger individuals,
to be 0.23% (0.14-0.42 95% prediction interval range). In contrast, in a typical high income country,
with a greater concentration of elderly individuals, we estimate the overall IFR to be 1.15% (0.78-1.79
95% prediction interval range). We show that accounting for seroreversion, the waning of antibodies
leading to a negative serological result, can slightly reduce the IFR among serosurveys conducted
several months after the first wave of the outbreak, such as Italy. In contrast, uncertainty in test false
positive rates combined with low seroprevalence in some surveys can reconcile apparently low crude
fatality ratios with the IFR in other countries. Unbiased estimates of the IFR continue to be critical to
policymakers to inform key response decisions. It will be important to continue to monitor the IFR as
new treatments are introduced.