Covid-19 Science

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

Do we understand what a hot spot is?

By Juergen Ude : 15th February 2021

A hot spot, at least a statistical hot spot is not finding one case in one area, it is an area that has a prevalence that is statistically more or less than the overall prevalence. For example there may be a tiny residual amount of infestation in all pallets of tobacco leaves, but some pallets may have much higher levels. These are hot spots.

When a hot spot is deemed because a region had a single case then this shows we do not understand what hot spots are.

Prevalence is needed to determine hotspots. Yet the science needed for prevalence estimates is non-existent. There has been no science applied in every country that we investigated, including Australia.

Current sampling is designed for contact tracing, not prevalence estimates and hence is biased and influenced, making prevalence estimates unreliable. The absence of prevalence estimates throughout a country means hotspot and cluster conclusions are meaningless. A sudden appearance of cases in a region does not necessarily mean a hotspot just ‘unlucky sampling’. It does not mean that if no more cases are found for 14 days the problem is gone. We were just lucky to find one.

If we find new cases in one area, not in another, we are living in a ‘fool’s paradise’ believing that the virus is located only in the area where a case was found. It all comes back to lucky sampling.

Whilst we believe we have hot spots without any statistically proven basis we will disproportionally allocate testing resources giving the virus a chance to multiply in areas not focused on. Until this aspect is dealt with we will continue to chase our tails and affect lives longer than necessary.

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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.