BAYES' THEOREM: The basic reason we get so many false positives to COVID19. The disease is so rare that the number of false positives greatly outnumbers the people who truly have the disease: THE MATHS:
https://t.co/oLHyxYJW9H

"Suppose that you are worried that you might have a rare disease. You decide to get tested, and suppose that the testing methods for this disease are correct 99 percent of the time"
"Suppose this disease is actually quite rare, occurring randomly in the general population in only one of every 10,000 people. If your test results come back POSITIVE, what are your chances that you actually have the disease? LESS THAN 1% chance that you have the disease!"
"The basic reason we get such a surprising result is because the disease is so rare that the number of false positives greatly outnumbers the people who truly have the disease"
Say mass testing of the contagious virus was done to 1 million people. In that million, 100 will really have the disease, 99 will be correctly diagnosed as having it. 999,900 of the million will not have the disease, but of those about 9,999 will be false positives! #BayesTheorem
1 in 2,000 INCLUDING FALSE POSITIVES classifies Covid19 as a rare disease in winter 20-21
https://t.co/29FNwq0Qw2
Stefano Scoglio, Nobel prize candidate 2018, has calculated a real false positive rate of 95% from official Italian Health Service numbers. This is in line with #BayersTheorem. Calculation in links in thread:
https://t.co/rthjPRJWeB
If we apply the 95% false positive (Scoglio) back to England positive test % incl. false positive: 0.05% (ONS), we get a real % of positives in England of 0.0025% of the population, or 1 in 40,000 people. This would confirm Covid19 as a rare disease as per #BayersTheorem.
Try this calculator for up to 100 tests. https://t.co/5TKEYpjd80
Severe Covid19 is a rare disease in England, if tests are 100% accurate, acc. to hospitalization numbers, it's 0.02% or 1 in 5,000 people.
https://t.co/kFnQVoCspb
#BayesTheorem applied to LF tests: https://t.co/3OrdS7ZFUJ
A simple example of #BayesTheorem with a prevalence of 0.1% (much higher than Covid19) an error range of 1% (RT-PCR Charité range est. 0.8-4%) and only 1,000 people tested: 91% false positives.
#BayesTheorem in simple terms: when medical mass testing includes asymptomatics & the disease affects a minority of the population, a very small margin of error in the testing process will mathematically result in the false positives being many times more than the real positives.
"Covid19"[😞] mass testing graph from The Economist. Y axis being % of test results either true or false. As share of population with active infection (X axis) is well under 1%, most positive tests are false, & most negative results are true. This is called #BAYESTHEOREM.
Latest England estimates:
https://t.co/8hsZ1hNjD7
How to increase the prevalence of a rare disease from 0.01% to 1%? Test the asymptomatics. What prevalence do we estimate Covid19 at including asymptomatic tested? Less than 1%. What could the true prevalence be if we exclude asymptomatic testing? 0.01%. It's called #BayesTheorem
Proof that Matt Hancock is aware of #BayesTheorem, he mentions Bayesian mathematics: https://t.co/hpZYDzD5Pe
As Matt Hancock is clearly aware of #BayesTheorem, if he wanted to avoid the false positives being many more than the true positives, he would not say ONS is applying rigorous Bayesian mathematics, he would instead not implement testing of any asymptomatics not linked to a case.
Matt Hancock is pre-empting the #BayesTheorem false positive trap by mentioning Bayesian mathematics himself in reply. A Freudian slip, a lapsus which reveals what he is really thinking: how do I increase false positives to make Covid19 prevalence appear worse than it really is?
So how does he do it? He implements mass testing of asymptomatics in Universities, then schools. He uses the hierarchical power structures in these institutions to convince healthy students they need to be tested. The schools get closed on false positives, false fear is created.
#BAYESTHEOREM @ Cambridge University. 0.4% of 262 students came back as positive after the first "test". All came back as negative after the second. Government only tests once. ONS would say there is 0.4% prevalence instead it's 0%.
#BAYESTHEOREM @ Cambridge University. 0.5% of 1,937 students came back as positive after the first "test". All came back as negative after the second. Government only tests once. ONS would say there is 0.5% prevalence instead it's 0%.
Sorry this should say 0.4% of 263 students https://t.co/eSNnhyOI4n
See what happens in #BayesTheorem? Number of asymptomatic testing increases & the estimated prevalence of the disease increases!! This can be addressed by requiring confirmatory tests of those who test positive when numbers are small, otherwise DON'T test asymptomatics.
Cambridge Pooled Testing Report #BayesTheorem
https://t.co/BYIzoTl64c
To have the same number of false negatives as false positives you need a disease that is present in 30% of the population. Covid19 affects less than 1%. This means the false positives VASTLY outnumber both the real positives & the false negatives. It's called #BayesTheorem.
Matt Hancock claim: "the ONS report..address directly the question how the ONS adjusts for potential false positives, due to the high but not perfect specificity of the PCR test. I am very happy for one of my academics to take him through the rigorous Bayesian mathematics"
"I am very happy for one of my academics to take him through the rigorous Bayesian mathematics, which I am sure will help to elucidate the debate on this matter still further." @MattHancock to @DesmondSwayne
https://t.co/pZcFlMBKEZ
I am waiting for one of @MattHancock's academics to take us through this as I have seen no evidence of @ONS adjusting for false positives according to #BayesTheorem https://t.co/ykB67TJORe
Professor Emeritus in Public Health, University of Arizona:
https://t.co/aidVGWOVqH
WHO wakes up to #BayesTheorem https://t.co/nDKklwMhQe

More from Robin Monotti FRSA MA BSc

The evidence based science shows that medical face masks for the healthy do not alter rates of community transmission of SARSCoV2 while they contribute to the plastic pollution of planet. Cloth & masks of other materials increase rates of infection through nebulization spread.

"Speaking through some masks dispersed largest droplets into a multitude of smaller droplets..smaller particles are airborne longer than large droplets (larger droplets sink faster), a mask might be counterproductive."
https://t.co/jBQlWRxcEL


Influenza like illness rates 3 times higher with cloth masks when compared to control group:
https://t.co/djT0mfutv9
Prof. Carl Heneghan, Oxford University: "The high quality trial evidence for cloth masks suggest they increase your rate of reinfection."


Please note, droplets smaller than 120 microns can't be measured. SARSCoV2 is 0.14 microns. This means that the nebulization effect of medical masks could not be measured, not that it does not happen. ⬇️


The really small aerosols <1 μm [the ones that pass through ALL surgical masks] can penetrate all the way to the alveoli - the basic units for gas exchange
I have now re-examined this document:


It clearly does indicate both the risks of bacterial infection & to prescribe broad spectrum antibiotics as part of treatment:
"Collect blood cultures for bacteria that cause pneumonia and sepsis, ideally before antimicrobial therapy. DO NOT
delay antimicrobial therapy"

"6. Management of severe COVID-19: treatment of co-infections
Give empiric antimicrobials [broad spectrum antibiotics] to treat all likely pathogens causing SARI and sepsis as soon as possible, within 1 hour
of initial assessment for patients with sepsis."

"Empiric antibiotic treatment should be based on the clinical diagnosis (community-acquired
pneumonia, health care-associated pneumonia [if infection was acquired in health care setting] or sepsis), local epidemiology &
susceptibility data, and national treatment guidelines"

"When there is ongoing local circulation of seasonal influenza, empiric therapy with a neuraminidase inhibitor [anti-viral influenza drugs] should
be considered for the treatment for patients with influenza or at risk for severe disease."

More from Category c19

1/: Avicenna was a Persian scientist, who lived 1000 years ago. He put two lambs in separate cages, which had the same health conditions. But only one lamb could see a wolf that was put in a third cage. The observations were astounding. (h/t @farmer_student) ⬇️a thread⬇️


2/: Both lambs were provided with the same feed. Also, the weight was exactly the same when the experiment started. Several months later, the lamb with sight on the wolf became cranky, restless, weak, and showed a significant weight loss and signs of poor development.

3/: The lamb that was under chronic stress as it was placed in a situation of constant apparent danger died eventually. 🐑🪦 In fact, the wolf did not pose a danger at all, but this was beyond the lamb's perception.

4/: This experiment showed that increased levels of the stress hormone cortisol have a bad impact on the metabolism of mammals. And 1000 years after this experiment, we are facing a similar situation again but with the difference that we are aware of the impact of stress.

5/: Currently, we are overwhelmed with medial and governmental propaganda with respect to a common cold virus (that might hypothetically be more lethal though) that doesn't do harm to the majority of the people. Extreme global measures are taken.
1/: The inventor of the corona PCR-Test @c_drosten is one of the #protagonists of the current crisis. He is known for involving himself in contradictions. In 2014, he gave a legendary #interview to @wiwo (https://t.co/jzTRh5Suhc) that I will address in this ⬇️short thread⬇️.


2/: The interview is significant because @c_drosten made totally sane statements back then that follow the principle of common sense. Considering his involvement in the "genesis of the current pandemic", his assertions appear in an entirely different


3/: In 2014, for instance, washing the hands was sufficient against being infected by coronaviruses. Several years he demands measures that destroy national economies and social life worldwide.


4/: Young @c_drosten also severely criticized the fact that Saudi Arabia used the PCR method to detect potential infections. From his point of view, that specific method could lead to many irrelevant cases. Nowadays, his view shifted his opinion towards 'collective punishment'.


5/: Whereas he demands "testing, testing, testing" nowadays and spreads panic and fear via (social) media, he heavily condemned that behaviour of Saudi media in 2014. On top of that, he expressed his concern that medial panic could increase the number of lab tests significantly.

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