W.H.O.: Stop the locckdowns !
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Started by GunterK - Oct. 11, 2020, 6:06 p.m.
By joj - Oct. 11, 2020, 8:14 p.m.
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Stop the beyond stupid practice of not wearing masks in crowded spaces.

Never mind.  There is no way to fix stupid.

By metmike - Oct. 12, 2020, 1:38 a.m.
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I'm shocked that W.H.O finally acknowledged this.

The cure has been worse than the disease in many cases.

By joj - Oct. 12, 2020, 7:44 a.m.
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The cure is masks without lockdowns.

(See Asian countries)

By wglassfo - Oct. 12, 2020, 1:25 p.m.
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There seems to be a debate going on as to how effective masks might be

Most acknowledge the N95 is superior mask. But most do not wear a N95 mask. Now if you take your commonly used mask and put it under a microscope you will see all kinds of junk collected by the mask. I don't know if this is proof that masks do some good, but junk on a mask shows some thing

On the other hand, the debate about wearing masks or not, is way above my pay grade

I wear a mask if the biz requires me to wear a mask before entering, such as a hard ware store or grocery shopping. However, out in the open I do not wear a mask. I find them hot and hard to breathe. I thinner mask is not so hot etc but how effective is a thin mask???

By metmike - Oct. 12, 2020, 9:46 p.m.
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What the pandemic has taught us about science



"Seeing science as a game of guess-and-test clarifies what has been happening these past months. Science is not about pronouncing with certainty on the known facts of the world; it is about exploring the unknown by testing guesses, some of which prove wrong.


Bad practice can corrupt all stages of the process. Some scientists fall so in love with their guesses that they fail to test them against evidence. They just compute the consequences and stop there. Mathematical models are elaborate, formal guesses, and there has been a disturbing tendency in recent years to describe their output with words like data, result or outcome. They are nothing of the sort.


An epidemiological model developed last March at Imperial College London was treated by politicians as hard evidence that without lockdowns, the pandemic could kill 2.2 million Americans, 510,000 Britons and 96,000 Swedes. The Swedes tested the model against the real world and found it wanting: They decided to forgo a lockdown, and fewer than 6,000 have died there.


In general, science is much better at telling you about the past and the present than the future. As Philip Tetlock of the University of Pennsylvania and others have shown, forecasting economic, meteorological or epidemiological events more than a short time ahead continues to prove frustratingly hard, and experts are sometimes worse at it than amateurs, because they overemphasize their pet causal theories.


A second mistake is to gather flawed data. On May 22, the respected medical journals the Lancet and the New England Journal of Medicine published a study based on the medical records of 96,000 patients from 671 hospitals around the world that appeared to disprove the guess that the drug hydroxychloroquine could cure Covid-19. The study caused the World Health Organization to halt trials of the drug.


It then emerged, however, that the database came from Surgisphere, a small company with little track record, few employees and no independent scientific board. When challenged, Surgisphere failed to produce the raw data. The papers were retracted with abject apologies from the journals. Nor has hydroxychloroquine since been proven to work. Uncertainty about it persists.


A third problem is that data can be trustworthy but inadequate. Evidence-based medicine teaches doctors to fully trust only science based on the gold standard of randomized controlled trials. But there have been no randomized controlled trials on the wearing of masks to prevent the spread of respiratory diseases (though one is now under way in Denmark). In the West, unlike in Asia, there were months of disagreement this year about the value of masks, culminating in the somewhat desperate argument of mask foes that people might behave too complacently when wearing them. The scientific consensus is that the evidence is good enough and the inconvenience small enough that we need not wait for absolute certainty before advising people to wear masks.


This is an inverted form of the so-called precautionary principle, which holds that uncertainty about possible hazards is a strong reason to limit or ban new technologies. But the principle cuts both ways. If a course of action is known to be safe and cheap and might help to prevent or cure diseases—like wearing a face mask or taking vitamin D supplements, in the case of Covid-19—then uncertainty is no excuse for not trying it.


A fourth mistake is to gather data that are compatible with your guess but to ignore data that contest it. This is known as confirmation bias. You should test the proposition that all swans are white by looking for black ones, not by finding more white ones. Yet scientists “believe” in their guesses, so they often accumulate evidence compatible with them but discount as aberrations evidence that would falsify them—saying, for example, that black swans in Australia don’t count."