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.
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.
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.
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:
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. 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.
 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.
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?
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.
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.
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.
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.
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.
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.
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.
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.
The national lockdown may have indirectly caused 16,000 excess deaths in two months, according to government analysts.
The new report says a reluctance to attend A&E and difficulties accessing medical assistance likely meant that for every three deaths from coronavirus itself, a further two occurred because of the wider impact of the lockdown.
We are seeing in many states and countries that were the “poster child” for how to do everything right, that everything worked great until it no longer worked.
Just two months ago, the island state had the fewest cases per capita in the country at less than two dozen per day. Democratic Gov. David Ige was praised for acting early to close Hawaii’s borders and impose strict quarantines, a painful economic sacrifice for a state heavily dependent on tourism. [Also had inter island travel quarantine requirements and had a face mask mandate since April!]
But a ten-fold surge in coronavirus infections and hospitalizations over the last month has triggered new shutdown orders and a scramble to bolster the public health measures state officials neglected before reopening. ….
Now the state, once hailed as a Covid-19 success story, has become a cautionary tale for other parts of the country that are preparing to open schools and loosen economic restrictions as infection rates come down. For public health experts and Hawaii officials, the state’s worsening outbreak is a stark reminder that this virus will easily exploit gaps in defenses.
Public health mitigation measures only work to slow the progression of the disease spread. Many measures are not sustainable over long periods of time or over large regions (such as strict lock downs). Other measures do not work well, or do not work at all, due to the realities of real life. Others, which seem like intuitive solutions, have no evidence to support their use. That’s not my opinion – that’s the view of four epidemiologists, one of whom is responsible for eradicating smallpox from the planet in a paper they published in 2006.
Consequently, the reasons that some places do well and some places do not, likely has to do with other issues – such as demographics, population density, and the time dimension. Places that do well for months seem to eventually have bad experiences too (Hawaii now, New Zealand, re-surging cases in EU countries that “did everything right”, S. Korea, Australia).
We make the mistake of comparing region X to region Y, in say, the spring and conclude that X is doing great while Y is doing poorly – and then draw conclusions that Y is doing poorly because of reasons A, B and C. But then three months later, region X, which did everything right finds itself doing badly. Thus, reasons A, B and C had little or nothing to do with region X doing well early on.
The experts have said most of these public health mitigation steps only delay and do not prevent the eventual spread of the disease. Thus, all of these measures may accomplish little but to prolong the pain.
All pandemics eventually end – either via herd effects, vaccinations or because the virus eventually changes to a less virulent strain.
It seems that everyone has mostly done “everything right” and while it did not stop the pandemic, at least it destroyed their economies, so there’s that success.
Note – I am an idiot who has no expertise in any of this and this post is for Entertainment Purposes Only. All persons not in health care are required to post a disclaimer like this, but those in health care never issue a disclaimer when they issue policies concerning business and economics over which they have no expertise.
Some of the nation’s leading public health experts are raising a new concern in the endless debate over coronavirus testing in the United States: The standard tests are diagnosing huge numbers of people who may be carrying relatively insignificant amounts of the virus.
Most of these people are not likely to be contagious, and identifying them may contribute to bottlenecks that prevent those who are contagious from being found in time.
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