A Contrarian Covid-19 View From Stanford
Daily FT|March 27, 2020
John Ionnidis, Professor of Medicine, Epidemiology and Population Health, of Biomedical Data Science and of statistics at Stanford University and Co-Director of Stanford’s Meta-Research Innovation Center has suggested that our “once-in-a-century pandemic” could also be a “once-in-a-century evidence fiasco.”
Omar Khan
A Contrarian Covid-19 View From Stanford

As our governments grapple with the best response, neither alarmism, nor unchecked paranoia nor denying the danger nor irresponsible negligence can be allowed to hold sway.

Professor Ionnidis points out “we lack reliable evidence on how many people have been infected with SARS-CoV-2 or who will continue to be infected.”

His challenge: if the pandemic actually begins to dissipate in weeks, draconian measures will have been all to the good (short term extreme distancing and lockdowns for example). But how will we judge if we are doing more harm than good?

Vaccines or affordable treatments he argues could be many months or even years away. On such timelines, the cost of long-term lockdowns are unknown, but almost certainly unsustainable.

We need rational forecasting

Current data he says is unreliable due to in part to limited testing (we don’t know if we are failing to capture infections by a factor of 3 or 300, consider India). No country currently has reliable data on the prevalence of the virus in a representative random sample of the general population – yet this is critically needed for any rational forecasting.

This “evidence fiasco” also creates uncertainty about the risk of dying from COVID-19. The WHO 3.4% claimed rate is meaningless he says as those who are treated are disproportionately those with severe symptoms and bad outcomes. And given limits on health systems, further such selection bias may occur.

The only situation we know of 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 even there, this was a largely elderly population, where death rates are higher.

A possible projection

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