Covidscience

Null hypothesis  Medicine has a very long tradition of jumping to conclusions and explaining all kinds of fancy mechanism.  Has anynody done the calculations of false positive covid tests ? It sounds great that a test is 99.9% accurate when it comes to specificity, but the results are terrible when very few have the so-called virus.  See the quote below from the  Norwegian authorities.  With a prevalence of 0.01 per cent (as in Norway today), the positive predictive value would be around 7 per cent with today's PCR test (sensitivity 80 per cent and specificity 99.9 per cent). That is, 14 out of 15 who test positive are not infected with SARS-CoV-2.   If we presume that the rate is as low as 20 positives in Australia, one in a million, then we would have 1000 false positives if we test  a million. If we are lucky we would find one of the   Infected ones.  We would then probably treat 1000 persons as if they had so-called Covid.  All the positives in Melbourne may in principle be fal

University of Texas found the following:  The sensitivity of the RT-PCR diagnostic test was estimated to be 0.777 (95% CI: 0.715, 0.849), while the specificity was 0.988 (95% CI: 0.933, 1.000). The confidence intervals include sampling error in addition to the error due to probabilistic knowledge of the data    https://www.medrxiv.org/content/10.1101/2020.04.24.20078949v1.full.pdf So the false positive error could be as high as 5% If there is no virus at all, we would get 50000  Pr million rested.  The us has tested 40 million  If the specificity is 92% all of the tests could be  false positives.  Approximately 8000 people die in the us pr day. Covid 19 has been going on for about 130 days. During this time   1 040 000 Americans have died.  Since many have been coded as covid deaths if doctors have suspected covid, 92% specificity could explain the whole epidemic with false positive.   With labs being under pressure to perform, it is probably that contaminants from previous tests lead