Did we really face a crisis? Was the fear overdone?
Governments were “spurred” to act by the rising hysteria and fear, and especially by the projections made by the modellers at the Imperial College in London. Apparently however, no government bothered to review the accuracy of past predictions made by these same modellers. A study by John Ioannidis (Stanford Prevention Research Center, Department of Medicine, and Departments of Epidemiology and Population Health, of Biomedical Data Science, and of Statistics, Stanford University, and Meta-Research Innovation Center at Stanford), Sally Cripps (School of Mathematics and Statistics, The University of Sydney and Data Analytics for Resources and Environments, Australian Research Council), and Martin Tanner (Department of Statistics, Northwestern University) pointed out the gross errors made in the past:
“Failure in epidemic forecasting is an old problem. In fact, it is surprising that epidemic forecasting has retained much credibility among decision-makers, given its dubious track record. Modeling for swine flu predicted 3,100-65,000 deaths in the UK. Eventually only 457 deaths occurred. The prediction for foot-and-mouth disease expected up to 150,000 deaths in the UK and led to slaughtering millions of animals. However, the lower bound of the prediction was as low as only 50 deaths, a figure close to the eventual fatalities. Despite these obvious failures, epidemic forecasting continued to thrive, perhaps because vastly erroneous predictions typically lacked serious consequences. Actually, erroneous predictions may have been even useful. A wrong, doomsday prediction may incentivize people towards better personal hygiene. Problems starts when public leaders take (wrong) predictions too seriously, considering them crystal balls without understanding their uncertainty and the assumptions made. Slaughtering millions of animals in 2001 aggravated a few animal business stakeholders, most citizens were not directly affected. However, with COVID-19, espoused wrong predictions can devastate billions of people in terms of the economy, health, and societal turmoil at-large.”
The Canadian Government could have used the resources and expertise of Statistics Canada to conduct monthly random sampling of the population to estimate the numbers infected, and the severity. Random sampling works for producing data on the labour market. Random sampling could have worked just as well with the virus. But for unknown reasons, Canada, and many other countries, chose not to do the obvious, probably for the same reasons they did not want to question the original dire predictions about the possible number of deaths.
In Table 1, the infection case mortality rate for Canada is just under 2%. Is this a reasonable estimate of the mortality risk? This would be a valuable piece of information to consider in trying to make a decision regarding how to deal with Covid-19.
A recent study by Arnold Barnett (“Covid-19 Risk Among Airline Passengers: Should the Middle Seat Stay Empty?”), a professor at the Sloan School of Business at MIT, used a mortality rate of 1%.
In mid-September, as the number of reported cases in the UK appeared to be rising again, Sir Patrick Vallance (the Chief Scientific Advisor to the UK Government) said:
“At the moment we think the epidemic is doubling roughly every seven days. If, and that's quite a big if, but if that continues unabated, and this grows, doubling every seven days... if that continued you would end up with something like 50,000 cases in the middle of October per day. Fifty-thousand cases per day would be expected to lead a month later, so the middle of November say, to 200-plus deaths per day.”
In other words, he was predicting a mortality rate of 0.4%.
Let’s apply the range of mortality rates between 0.4% and 1% to the Canadian estimates for the number of people who have died from Covid-19. We get a range for the possible actual number of Canadians infected to date between 2.4 and 6.1 million — well above the reported number of 1.2 million. For the US, we get a range between 59 and 148 million — compared to the reported 33 million.
There have been several studies indicating that the actual number of people infected with the Covid-19 virus might be six to 20 times the actual numbers reported.
What is the real number? No one yet knows, and definitely not governments.
The mortality rates might even be lower than 1% or even 0.4%, especially when we exclude the very old who die in nursing homes. For example, research by Oxford University found that around 29% of the people included in the coronavirus death tolls by the Office of National Statistics in the United Kingdom during the summer months had died primarily from other conditions. Most of these might have died even if the people had not become infected with the coronavirus. The research found that throughout the entire pandemic, about 8% of the people classed as Covid-19 victims did not have the disease as an underlying cause of death.
Dr. Jason Oke, part of the team from the Centre of Evidence-Based Medicine at Oxford that uncovered the data, said many people had been dying “with” coronavirus but not “from” it.
“The true death rate is an important thing to know because it gives us an idea of impact…The impact now seems to be lessening, and if that is true – which it certainly looks like at the moment, because there doesn’t seem to be the same fatality rate – then that will guide decisions in managing risk, so it's important to get this number right. In the lockdown, there may have been even more deaths that were not caused by Covid, but were caused by the actions of lockdown – and that is important to know.”
The CDC in the US reported last year that only 6% of all deaths attributed to Covid-19 were caused by the virus alone. In the other 94% of the cases, the virus was a contributing factor, and not always a critical factor. Thus, not only do we not know the number of total and daily new infections, we probably do not even know the number of deaths attributable primarily to the virus.
Remember that one of the objectives for flattening the curve was to prevent the healthcare system from being overwhelmed. What is the severity of the illness caused by the virus? That is, what proportion of all people infected by Covid-19 require hospitalization and intensive care?
As more data have become available, there are indications that 90% or more of people infected with the virus either are asymptomatic or suffer very mild symptoms. This would be consistent with the apparent low mortality rates. This virus might not be a more serious threat than the flu