John P.A. Ioannidis, professor of medicine, of epidemiology and population health, of biomedical data science, and of statistics at Stanford University and co-director of Stanfordâ€™s Meta-Research Innovation Center, warns that, as is very much standard operating procedure in the modern world, we are making serious decisions treating totally unreliable statistics as facts.
The data collected so far on how many people are infected and how the epidemic is evolving are utterly unreliable. Given the limited testing to date, some deaths and probably the vast majority of infections due to SARS-CoV-2 are being missed. We donâ€™t know if we are failing to capture infections by a factor of three or 300. Three months after the outbreak emerged, most countries, including the U.S., lack the ability to test a large number of people and no countries have reliable data on the prevalence of the virus in a representative random sample of the general population.
This evidence fiasco creates tremendous uncertainty about the risk of dying from Covid-19. Reported case fatality rates, like the official 3.4% rate from the World Health Organization, cause horror â€” and are meaningless. Patients who have been tested for SARS-CoV-2 are disproportionately those with severe symptoms and bad outcomes. As most health systems have limited testing capacity, selection bias may even worsen in the near future.
The one situation where an entire, closed population was tested was the Diamond Princess cruise ship and its quarantine passengers. The case fatality rate there was 1.0%, but this was a largely elderly population, in which the death rate from Covid-19 is much higher.
Projecting the Diamond Princess mortality rate onto the age structure of the U.S. population, the death rate among people infected with Covid-19 would be 0.125%. But since this estimate is based on extremely thin data â€” there were just seven deaths among the 700 infected passengers and crew â€” the real death rate could stretch from five times lower (0.025%) to five times higher (0.625%). It is also possible that some of the passengers who were infected might die later, and that tourists may have different equencies of chronic diseases â€” a risk factor for worse outcomes with SARS-CoV-2 infection â€” than the general population. Adding these extra sources of uncertainty, reasonable estimates for the case fatality ratio in the general U.S. population vary from 0.05% to 1%.
That huge range markedly affects how severe the pandemic is and what should be done. A population-wide case fatality rate of 0.05% is lower than seasonal influenza. If that is the true rate, locking down the world with potentially tremendous social and financial consequences may be totally irrational. Itâ€™s like an elephant being attacked by a house cat. Frustrated and trying to avoid the cat, the elephant accidentally jumps off a cliff and dies.
We have a serious problem in the fact that most people, including the overwhelming majority of people in positions of power and responsibility do not understand math and think that statistical estimates and projections constitute scientific fact. How many times have you heard the network news talking head gravely report that “We know there are only [pick a number] of [pick an allegedly Endangered Species] left”?
Only old people like myself remember today the 1976 Swine Flu Epidemic Panic that Gerald Ford presided over which, when the whole thing petered out into a non-event, left the President and official Washington looking ridiculous.
HT: Frank Dobbs.