“You need to read the article I sent you,” a doctor and an old friend of mine advised. “Dr. Ioannides is very intelligent,” he continued.

“I’ll read it,” I answered.

It was a Saturday morning. The time I enjoy my coffee while reading the newspapers.

Not too long after that, I received another email from my friend asking: “Did you read it?”
It must be very important, I thought, and I started reading it.

The title immediately caught my attention: “A fiasco in the making? As the coronavirus pandemic takes hold, we are making decisions without reliable data?”

Many conspiracy theory stories about the coronavirus have popped up on irresponsible websites. The most improbable things are being written.

Each gives his or her own version of how the pandemic started, who started it and why.

However, the author of this article, Dr. John P.A. Ioannidis is no such a person. He’s a distinguished scientist, a professor of medicine, epidemiology, population health, biomedical data science, and statistics at Stanford University. He is also co-director of Stanford’s Meta-Research Innovation Center.

Yes. His article is not mere speculation at all.

I read the recommended article and then opened the New York Times. As I was reading the column of Bret Stephens, one of the commentators I almost always read, there was a reference to Ioannidis’ article:

“’We don’t know if we are failing to capture infections by a factor of three or 300,’ writes Stanford’s disease prevention expert, John P.A. Ioannidis, in a must-read piece in the authoritative science and medicine website Stat (itself a must-read).”

Stephens continued: “What we should not do, Ioannidis warns, is impose draconian measures that seem effective on paper but whose unintended consequences are poorly thought through…Policymaking should not be dictated, as it is now, by a combination of raw fear and lousy data.”

And I continue by quoting Dr. Ioannides’ article directly: “How long, though, should measures like these be continued if the pandemic churns across the globe unabated? How can policymakers tell if they are doing more good than harm?”

“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.”

If, Dr. Ioannides continues, “we had not known about a new virus out there, and had not checked individuals with PCR tests, the number of total deaths due to ‘influenza-like illness’ would not seem unusual this year. At most, we might have casually noted that flu this season seems to be a bit worse than average…One of the bottom lines is that we don’t know how long social distancing measures and lockdowns can be maintained without major consequences to the economy, society, and mental health.”

And he concludes:

“Unpredictable evolutions may ensue, including financial crisis, unrest, civil strife, war, and a meltdown of the social fabric…If we decide to jump off the cliff, we need some data to inform us about the rationale of such an action and the chances of landing somewhere safe…at a minimum, we need unbiased prevalence and incidence data for the evolving infectious load to guide decision-making.”