Category Archives: Public Health/Coronavirus

Public health does not understand math

Proximity technology is controversial, particularly among some Americans who are unwilling to share personal data for privacy reasons and skeptical of the big tech companies offering the service. But it’s been embraced in some places, including Scotland, where a new app was reportedly downloaded 600,000 times.

Source: Your phone could determine if you’ve been exposed to coronavirus. Oregon’s ready to embrace the tech –

600,000 sounds impressive based on intuition (everything in public health seems to be based on intuition).

The population of Scotland is estimated as 5.5 million.

600k of 5.5 million is 10.9% of the population.

If we assume everyone has a compatible smart phone (which is a false assumption), then the probability that an individual has the app is 10.9%.

The probability that two people have the app is 10.9% x 10.9% or 1%. It takes two to have a detection.

Continue reading Public health does not understand math

Public health says it has no data on what works and what does not work

Oregon has released its periodic modeling update report.

Shockingly, they say they have no data on what measures work or do not work, or whether or not anyone is adhering to them. They have no information on whether some measures work better or worse than others. They have no data on any measures at all.

Continue reading Public health says it has no data on what works and what does not work

CDC director says you do not need a flu shot? Says face masks more effective than vaccine

This post was originally written on September 18, 2020 but not published at the time. It has gone live on January 26, 2021. This post highlights some insane comments from the CDC and the CDC Director.

This –> CDC director Robert Redfield said face masks may be more effective than a vaccine in preventing individual coronavirus infections

“I might even go so far as to say that this face mask is more guaranteed to protect me against COVID than when I take a COVID vaccine, because it may be 70%. And if I don’t get an immune response, the vaccine is not going to protect me,” Redfield said. “This face mask will.”

To emphasize that point, a CDC journal says we must maintain our national lock downs and wear face masks forever to prevent influenza.

CDC is telling us that flu vaccines do not work? What?

Earlier, the CDC Director issued scary warnings about a simultaneous epidemic of Covid-19 and influenza during the winter.

If the measure we are taking for Covid-19 actually work, then they should also work for the flu. Is Redfield saying our mitigations measures do not actually work?

They they tell us:

Still, the precipitous drop [in southern hemisphere flu] seems to suggest that prophylactic measures, such as social distancing and masks, that target one virus can also work against another.

Update January 2021

In January public health “experts” say influenza is almost non-existent (so far) and attribute that drop to Covid-19 mitigation measures.

Which leads to the obvious question: If mitigation measures worked for the flu, why did they not work for Covid-19?

Second, news reports call Robert Redfield a liar and say public health is politicized “voodoo” (refers to the pop culture reference not the original religion).

The CDC no longer has integrity or credibility. I do not expect public health to restore trust and credibility in my life time.

This is not going to work right: CDC would like to test “virtually everyone”

The false positives will overwhelm the true positives – how will they detect this?

Testing has so far been used in the United States mostly to diagnose people who are sick or have been exposed to someone with a confirmed Covid-19 case. Screening would test virtually everyone in a given community, looking for potentially infectious people.

Source: CDC is developing new coronavirus testing guidance for screening at schools, businesses

Here’s the problem.

  • We test everyone.
  • The actual prevalence of the disease in the community is 1 in 500 people (as an example).
  • Our screening test is 99% accurate (in reality, the full test process may have a much higher error rate).
  • We test 500 people and find 1 person who actually has Covid-19, plus we find 1% of the 500 or 5 people who are tagged as false positives.
  • We’ve now found six people testing positive but only 1 of the six actually has the disease; the other five are false positives.
  • Public health authorities tell us that six people tested positive and “new coronavirus cases” go up by six.
  • The community, however, has a population of  50,000 people.
  • Our testing “virtually everyone” finds 100 actual new cases (1/500 x 50,000) and 500 false positives (5 for every 500 or 10 for every 1,000, times 50 or 500).
  • Public health tells us there are 600 new cases (100 actual + 500 false positives).
  • 500 people are placed in two week quarantines unnecessarily.
  • Because we are testing everyone and because of this problem, we can never “flatten the epicurve” – we will always have a large number of false positives when we test everyone while the prevalence of the disease is low. Even a high accuracy test – or high specificity – still results in this problem. No test – including lab and handling – is 100% accurate.

And oh, the actual test they are proposing to use has a false positive rate of 3% – three times worse than the 1% I used above.

Continue reading This is not going to work right: CDC would like to test “virtually everyone”

It’s worse than we thought: “Second Analysis of Ferguson’s Model”

In the past I had some comments on Neil Ferguson’s disease model and have repeatedly noted its poor quality. This model was used, last spring, as the basis for setting government policies to respond to Covid-19. Like many disease models, its output was garbage, unfit for any purpose.

The following item noted that the revision history, since last spring, is available and shows that ICL has not been truthful about the changes made to the original model code.

Source: Second Analysis of Ferguson’s Model – Lockdown Sceptics

THIS! Many academic models including disease models and climate models, average the outputs from multiple runs, some how imaginatively thinking that this produces a reliable projection – uh, no, it does not work that way.

An average of wrong is wrong.  There appears to be a seriously concerning issue with how British universities are teaching programming to scientists. Some of them seem to think hardware-triggered variations don’t matter if you average the outputs (they apparently call this an “ensemble model”).

Averaging samples to eliminate random noise works only if the noise is actually random. The mishmash of iteratively accumulated floating point uncertainty, uninitialised reads, broken shuffles, broken random number generators and other issues in this model may yield unexpected output changes but they are not truly random deviations, so they can’t just be averaged out.

Software quality assurance is often missing in academic projects that are used for public policy:

For standards to improve academics must lose the mentality that the rules don’t apply to them. In a formal petition to ICL to retract papers based on the model you can see comments “explaining” that scientists don’t need to unit test their code, that criticising them will just cause them to avoid peer review in future, and other entirely unacceptable positions. Eventually a modeller from the private sector gives them a reality check. In particular academics shouldn’t have to be convinced to open their code to scrutiny; it should be a mandatory part of grant funding.

The deeper question here is whether Imperial College administrators have any institutional awareness of how out of control this department has become, and whether they care. If not, why not? Does the title “Professor at Imperial” mean anything at all, or is the respect it currently garners just groupthink?

When a software model – such as a disease model – is used to set public policies that impact people’s lives – literally life or death – these models should adhere to standards for life-safety critical software systems. There are standards for, say, medical equipment, or nuclear power plant monitoring systems, or avionics – because they may put people’s lives at risk. A disease model has similar effects – and hacked models that adhere to no standards have no business being used to establish life safety critical policies!

I and another software engineer had an interaction with Gavin Schmidt of NASA regarding software quality assurance of their climate model or paleoclimate histories[1]. He noted they only had funding for 1/4 of a full time equivalent person to work on SQA – in other words, they had no SQA. Instead, their position was that the model’s output should be compared to others. This would be like – instead of testing, Microsoft would judge its software quality by comparing the output of MS Word to the output of another word processor. In other words, sort of a quailty-via-proxy analogy. Needless to say, this is not how SQA works.

Similarly, the climate model community always averages multiple runs from multiple models to create projections. They do this even when some of the model projections are clearly off the rails. Averaging many wrongs does not make a right.

[1] Note that NASA does open source their software which enables more eyes to see the code, and I do not mean to pick on NASA or Schmidt here. They are doing what they can within their funding limitations. The point, however is that SQA is frequently given short shrift in academic-like settings.

Is the pandemic nearly over with?

Read this paper by apparently well qualified authors. It presents an argument that we are near the end of the pandemic, that this end has been reached naturally, and that most interventions have had little effect: How Likely is a Second wave?

(Note – published on a “skeptic” site by qualified authors.)

I have been tracking my state’s data since late March, and drawing about 40 charts plus about a dozen separate calculated numbers. I have no expertise in health topics – but I can analyze data. I have been watching similar trends play out and have had similar thoughts or questions as presented in the non-peer reviewed paper, above.

Update: Here is an item from August that comes at the issue in a different way but has similar findings.  What should we think?

Interesting comparison finds ICL disease model was worthless

Disease models have been a fiasco from day one:

We can refer to a natural experiment in Sweden for some clarity. Sweden’s government did not lock down the country’s economy, though it recommended that citizens practice social distancing and it banned gatherings of more than 50 people. Swedish epidemiologists took the Imperial College of London (ICL) model – the same model that predicted 2.2 million Covid-19 deaths for the United States – and applied it to Sweden. The model predicted that by July 1 Sweden would have suffered 96,000 deaths if it had done nothing, and 81,600 deaths with the policies that it did employ. In fact, by July 1, Sweden had suffered only 5,500 deaths. The ICL model overestimated Sweden’s Covid-19 deaths by a factor of nearly fifteen.

Source: The Covid-19 Catastrophe – AIER

ICL’s model, was developed by Neil Ferguson, the architect of the UK’s lock down. He resigned after he was found to have had a tryst with his married lover while socializing was prohibited by his own lock down orders – and he had tested positive for Covid-19, himself shortly before.

Models have been a mess. Experts deeply critical of the ICL model.

Most people likely won’t get a coronavirus vaccine until the middle of 2021

I have been expecting a general roll out in Q2 of 2021 and am still sticking with that. We will hear much positive news on vaccines from now well into October and the first non-test phase recipients will receive vaccinations at some point during the last two months of the year (but very limited distribution).

Most Americans likely won’t get immunized with a coronavirus vaccine until the middle of next year, U.S. officials and public health experts say, even as the federal government asks states to prepare to distribute a vaccine as soon as November.

Source: Most people likely won’t get a coronavirus vaccine until the middle of 2021

I think we will see “herd effects” occurring from this point on ward, and especially by late this year. But I am an idiot with no health expertise so my comments are for Entertainment Purposes Only.

Of interest, the annual winter time influenza in the southern hemisphere has been so mild as to be almost non-existent; that is great news. No one knows why but some suggest may be because of social isolation, hand washing – and their favorite super hero, face masks. Regarding the latter, the University Washington published new  disease model projections (theirs have been mostly worthless) that surprising implies face masks do not work (but they did not seem to notice they had said that!)

The Institute for Health Metrics and Evaluation at the University of Washington’s School of Medicine is  predicting more than 410,000 deaths by January if mask usage stays at current rates. If governments  continue relaxing social distancing requirements, that number could increase.

Source:  Coronavirus live updates: Model predicts 410K US deaths by January; Labor Day weekend brings risk; South Dakota stages state fair

That is a more than doubling of deaths in the U.S. in the next 3 1/2 months – versus the past 5 months. And this occurs while face masks are now mandatory throughout most of the country and in all of the highly populated areas. National surveys indicate  face mask wearing is common and widespread in populated areas.; Newsweek reports that 95% of Americans are wearing face masks as required.

But the UW IHME is saying we will see a  more than doubling of deaths in the next 3 1/2 months – versus the past 5 months. They are saying – without realizing it – that face masks are  not working at all. We have high compliance but the death rate, per their estimate, will more than double in just over half the time as previous deaths occur.

This is a shocking finding – there are about 110 days between now and January. The CDC reports 186,153 deaths as of today. To reach 410,000 means an average of over 2,000 new deaths per day between now and January. As of today, the U.S. is averaging about 900 deaths per day.

Official CDC Chart as of 9/4/2020. To meet the UW IHME projection, the dropping death must not only reverse, but needs to double almost immediately to leaves not seen since last spring. Does this make any sense? 

To rise from 900 to 2,000 deaths per day means public health mitigation steps are not working. It means the U.S. would revert back to the peak deaths period that occurred in the spring.

That is a stunning conclusion from the UW’s IHME and apparently they did not notice what they just said.

A LOT of experts have said the IHME’s random number generation program is worthless and this new projection seems to reinforce what other experts are saying. Disease modeling is 21st century astrology and just as reliable.

Update: When will we resume having public events again? I am planning to attend a comic con event in early March 2021 but I am convinced it will be canceled. My guess is for vaccines to start rolling out in Q1 2021 but might not be available to the general population until Q2 2021. Then, it will take months to get the vaccine administered to millions of people.

Public health authoritarians will not reduce restrictions until some as yet unspecified metrics are achieved. For example, perhaps a positive Covid-19 test rate of 0.25% or something. Who knows what they will require?

This might happen in Q2 – or may be, like some of them have been saying in the media, we will face restrictions for the next 1 to 3 years. I doubt the public will agree with that – as the authors of the 2006 paper on public health mitigation note, pandemics end when herd effects take over, vaccines are available, the virus mutates to a less infectious or virulent form – or the public just gives up and gets on with life.

The Failed Experiment of Covid Lockdowns – WSJ

There are other published papers that came to this conclusion long ago – lock downs are useful for a few weeks, and are not otherwise sustainable in most societies (and especially for entire regions or countries) and people will not follow them for long because they cannot.

Six months into the Covid-19 pandemic, the U.S. has now carried out two large-scale experiments in public health—first, in March and April, the lockdown of the economy to arrest the spread of the virus, and second, since mid-April, the reopening of the economy. The results are in. Counterintuitive though it may be, statistical analysis shows that locking down the economy didn’t contain the disease’s spread and reopening it didn’t unleash a second wave of infections.

Source: The Failed Experiment of Covid Lockdowns – WSJ

Saw someone on Facebook proclaiming that if “we had a HARD LOCKDOWN like Wuhan” this would have all be over with. This person did not seem to realize that China’s lock down was for a limited region of their country, not the entire country. A country wide lock down is not feasible and not sustainable.

The concept of a lock down goes back hundreds of years when coastal communities would develop outbreaks of diseases upon arrival of ships. Their solution was to isolate the sailors and quarantine the community. This could work in a small, isolated communities hundreds of years ago. But the U.S. federal government concluded in 2004 that lock downs were infeasible. A 2006 paper by 4 epidemiologists drew the same conclusions; one of the authors is credited with having eradicated smallpox from the planet.

Public health pandemic responses have not advanced in hundreds of years, and are unworkable. This explains why so many regions and countries that were doing everything right and looked great eventually end up no longer looking great. The viral outbreak mostly does what the viral outbreak does. Random correlations, especially those ignoring the time dimension, are easy to make and to incorrectly conclude that measure X had some great impact.

All pandemics eventually end – due to herd effects, vaccines, the virus mutates, or people eventually get on with life and ignore the restrictions – or a combination of all of them.

Remember, since I do not work in health care I am de facto required to note that I am an idiot with no expertise in any of this and my comments are for Entertainment Purposes Only. The CDC, meanwhile, is now issuing economic Orders even though it has no expertise in that area and does not provide any disclaimer.

CDC bans rental evictions through Dec 31, 2020

What expertise does the CDC have in this matter?

To be eligible for protection, renters will need to provide proof of their inability to pay rent because of the pandemic.

Source: Renters in U.S. cannot be evicted through the end of the year due to coronavirus, CDC order states – MarketWatch

The CDC has no expertise what so ever in making real estate, financial and economic orders – but is now asserting that the powers of public health are unlimited. Lawyers, writing on social media, say the legal basis for the CDC regulating housing is quite a stretch.

In an election year, this action seems to be based on politics – and not much else.

Public health has asserted itself as a politicized totalitarian regime.  I no longer  believe a word from any one in public health. Remember, protesting is now more important than fighting a virus, they said. That virus that was the reason we shut down everything, put 40 million people out of work and close our schools.

Stay Home, Save Lives, Don’t Kill Grandma gave way to “Protest! Kill Grandma!”

The above was not supportable by any evidence – that, like the CDC asserting evictions bans, is based on politics and not science. Public health appears to be a fake science at this point.

Update: My state extended its “state of emergency” through November 3rd (the national election date). As of November 4th, the pandemic emergency is apparently over with. Talk about politicizing.