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One was enough Scientific study inaccurate, that is, content with a mistake as trivial as it is serious, overestimating the death rate of Covid-19 ten times and causing the whole world to panic.
In August, a study by Professor Ronald Brown of the School of Public Health and Health Systems at the University of Waterloo, Canada, later featured in the Cambridge University Public Health Emergency Collection, showed how the report was transmitted in early March to the United States Congress by the Grandchildren (National Institute of Allergy and Infectious Diseases) contained a blue pencil oversight.
When in the spring, amid the first wave of coronavirus, the American National Institute of Allergy and Infectious Diseases sent the aforementioned document to American politicians, they took the content of that paper literally. On the other hand, why not do it since Niaid is one of the most prestigious and important institutions in the country?
Well, that report, inherent to Sars-CoV-2 and, more particularly, the danger of the virus, contained a sensational error. As pointed out Italy today, US experts on Niaid had overestimated ten times the estimated death rate of Covid among the national population.
Those numbers and those results scared not only the United States itself but also governments around the world. Those who, fearing the worst, began to devise models to stop the spread of the virus, between more or less extensive lockdowns and the imposition of restrictive measures.
The experts’ mistake
Let me be clear, the coronavirus remains a dangerous enemy and one that must be faced with the greatest commitment. But the fact is that the data initially handled by the United States Congress was distorted by a Niaid mess. The experts in question confused two categories of data different from each other but with very similar names: Mortality rate from cases and Mortality rate from infection.
Remember that the Case fatality rate measures mortality among all diagnosed and confirmed cases, whileDeath rate from infection Indicates mortality among all infected, including asymptomatic and undiagnosed.
The confusion in the figures stems from having applied the level of lethality among the certainly ill to the incidence in the extended population of infected but “apparently healthy”. The conclusion was particularly alarming given that, under these conditions, the COVID-19 it would have killed ten times as many people as a normal flu season does.
Beyond the embarrassing error mentioned, there are several questions to which, even today, it is impossible to give a determined answer. For example: when the number of deceased within a population is lower than projections, how can we show that the achieved result is derived solely from the precautions imposed by governments (ie mandatory masks, quarantines and various distances)? In fact, there is the possibility of having simply started from a calculation basis initial too high.
Of course, we must not forget the heavy economic effects of restrictive measures adopted by governments. In recent days, infections have risen again. And many countries wonder what to do. However, what happened in the United States could (and should) serve as a warning: be careful with the data. And to possible errors.
