Truth Behind The Science

The science behind the global response to the Covid-19 pandemic

Case reporting defies logic

By Juergen Ude : 17th February 2021

Case reporting is unscientific and highly flawed because case numbers are influenced by testing. Testing numbers have rapidly increased driven by fear in the hope of controlling the virus.

Imagine that everyone is infected in a population. If on day one, we test one person the cases are 1. If, on day 2 we test 2 people, then there will be 2 cases. If on day 100 we test 100, then there will be 100 cases. We will conclude that cases grew by a factor of 100. The reality is no growth because the whole population is infected.

When cases are dependent on test numbers, then these must be factored in. This is achieved with case positivity, or proportion of cases relative to tests performed. For the above example, case positivity would be 100% all the time, which is correct. No growth, just 100%.

Figure 1 show cases for the United Kingdom as at 10th February 2021 which does not factor in testing numbers.

BIS.Net Analyst Change Analysis used in Covid-19 analysis
Figure 1: Cases for the United Kingdom as at 10th February 2021

Figure 2 shows case positivity (proportion) for the UK as at 10th February 2021 which factors in testing numbers.

BIS.Net Analyst Change Analysis used in Covid-19 analysis
Figure 2: Case positivity (proportion) for the UK as at 9th February 2021

Please note that due to unavailability of test-data early in the pandemic the first wave is truncated.

Referring to Figure 2, by taking testing numbers into account, the second and third waves are a fraction of the first wave. The third wave is slightly higher, but we should not conclude due to higher contagiousness. This is a period where infections are usually higher.

Referring back to Figure 1, the world panicked due to the rising case numbers which did not factor in the increase in testing numbers.

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Dr Juergen Ude has a certificate in applied chemistry, a degree in applied science majoring in statistics and operations research as top student, a masters in economics with high distinctions in every subject, and a PhD in computer modelling and algorithms. He has lectured at Monash University on subjects of data analysis, computer modelling, and quality & reliability.

Prior to founding his own company (Qtech International Pty Ltd), Dr Ude worked as a statistician and operations researcher for 18 years in management roles having saved employers millions of dollars through his AI and ML algorithms. Through Qtech International, Dr Ude has developed data analysis solutions in over 40 countries for leading corporations such as Alcoa, Black and Decker, Coca-Cola Amatil, US Vision and many more. Additionally he has developed campaign analysis software for politicians.