Here is a screenshot of my Excel calculator, with explanatory notes:
false_positives_calc.jpg
It duplicates the BMJ calculator but allows percentages. The 'helper' at the side allows for total cases and tests. From this and the three inputs in the coloured boxes, it determines what the specificity needs to be to match.
Of course, the BMJ PCR calculator is right not to allow percentages - because these tests are unsuitable for such low figures. This is the main argument against these PissyArgh tests.
Ah OK thanks, so it's based on the ONS estimating a 0.79% Covid rate, do you know how the ONS estimates this figure?
Ah OK thanks, so it's based on the ONS estimating a 0.79% Covid rate, do you know how the ONS estimates this figure?
As I mentioned before, I have asked them a couple of times how they do this but I have never received a reply.
I used to find the ONS very helpful in answering questions. Now, there is not even an acknowledgement. Maybe they have been instructed to keep shtum as people start to scrutinise more and more.
The one thing that is missing from the diagram is to show that the very high false negative rate means that multiple rounds of testing doesnt lower the false positive rate by very much. Even after three rounds of testing the false positive rate is still over 30%. And the true infection detection rates is down to 12%..
Because the false negative rate for the RT/PCTR test is around 50% only half the samples with an active infection actual are positives. So using the numbers above and a 10K sample there would be 80 people with an active infection but only 40 would test positive. So that 650 false positives and 40 true positives. 95% false positive rate.
So a second round of testing to weed out the false positives also will fail negative for about 20 of the true positives. So after the second round of tests the false positive rate is still around 70%. 20 true positives + 50 false positive.
Now the third round of tests with the last 70 samples would produce 10 true positives and around 5 false positives. A false positive rate of around 30%.
A fourth round of tests would produce a false positive rate of only 5%. But with only 5 true positives.
So of the original 80 people in the 10K sample only 6% of the active infections can be detected with an error rate of 5%. Only 12% can be detected with an error rate of 30%. 24% with an error rate of 70%. And at best 50% with an error rate of 95%.
To put it another way. In the best case scenario for RT/PCR testing , the first round, where at least half the people with an active infection test positive only one in twenty of the positives actually has any kind of active infection. And increases the rounds of retesting to reduce the false positive rate just halves the positive detection rate every round.
Thats the best it can do. A detection rate of somewhere between 50% and 6%. With the 6% number being the only one that doe not have a very high margin of error.
If a private company offered those kind of levels of accuracy with an important diagnostic test they would be prosecuted for criminal fraud.
