How Reliable Are Rising Case Numbers?

Today we’re publishing an original article on Lockdown Sceptics by four scientists at Queen Mary University – two professors, a post-doc and a reader – casting doubt on the “evidence” that we’re in the midst of a deadly second wave.

The massive increase in ‘new cases’ is almost completely explained by factors that have nothing to do with an increasing population health risk. New cases are simply the count of those who get a positive test result. But almost all of those – as can be seen from the university student ‘cases’ – are either asymptomatic or false positives., i.e. they do not – and will not – show any symptoms of a ‘COVID-19 illness’. Nor will they ‘spread the virus’ to others.

Also, contrary to widely believed assumptions, there is no ‘gold standard’ test for COVID-19. A diagnostic process, namely PCR, has been used, but since the outbreak there has been no attempt to determine its accuracy. It might be shocking to find that research on lab grown ‘live’ cultures of the virus, taken from patients, had not been published until early August – eight months after the virus outbreak. These have been used to assess the accuracy of PCR and the results are not good. It has been shown it is possible to return a positive PCR test where a sample taken from the same patient never grows a viral culture – meaning the patient does not have an active COVID-19 infection despite the positive PCR test. The implications of this for the false positive rate of PCR tests are obvious and significant.

The other obvious explanation for the increase in number of ‘cases’ is that far more people are being tested – 280,000 per day now compared to 10,000 at the peak in March. So, while there are twice the number of ‘new cases’ per day now compared to the March peak, the number of ‘new cases’ per 1000 people tested now is actually only ONE-TENTH of that in the March peak (45 compared to 450).

The authors of this piece are Professor Norman Fenton is the Professor of Risk and Information Management, School of Electronic and Electrical Engineering, Queen Mary University of London; Dr. Scott McLachlan is a Postdoctoral Research Assistant, School of Electronic and Electrical Engineering, Queen Mary University of London; Professor Martin Neil is the Professor of Computer Science and Statistics, School of Electronic and Electrical Engineering, Queen Mary University of London; and Dr. Magda Osman is a Reader in Experimental Cognitive Psychology, School of Biological and Chemical Sciences, Queen Mary University of London.

Very much worth reading in full.