Stanford’s Ioannidis Contrarian COVID-19 Voice

Apart from President Donald Trump and his backers who don’t much listen to scientists and medical experts on how to deal with the still-raging COVID-19 pandemic, there is widespread support for using lockdowns aimed at preventing a spread.

But one of the country’s most respected physicians – and also a scientist, and writer – Stanford University’s Dr. John Ioannidis, a professor at the school of medicine, argues that the virus is less deadly than predicted, despite more than 3 million cases worldwide and deaths surpassing 211,000 and virtually every country hit.

People are making “big statements about ‘lockdowns save the world.’ I think that they’re immature. They’re tremendously immature. They may have worked in some cases, they may have had no effect in others, and they may have been damaging still in others,” he told The Wall Street Journal.

Ioannidis, 54, rated by Google Scholar as one of the world’s 100 most-cited scientists, was born in New York City, raised in Athens and graduated at the top of his class at the University of Athens Medical School.

He then attended Harvard for his medical residency in internal medicine and did a fellowship at Tufts University for infectious disease and has highlighted flaws in research methods and been a kind of contrarian in the scientific society.

But his credentials are impeccable, adding to the weight of his voice disagreeing with many over COVID-19 with the fear spreading as fast as the virus, locking down whole countries and keeping people in their homes.

Ioannidis’ most famous paper, Why Most Published Research Findings Are False, has been cited in thousands of other studies since its publication in 2005. A 2010 profile in The Atlantic stated that “Ioannidis may be one of the most influential scientists alive,” noted the site Undark.

That’s why he’s being listened to and not dismissed as he differs with colleagues.

In a March article for Stat News, Dr. Ioannidis argued that COVID-19 is far less deadly than modelers were assuming. He considered the experience of the Diamond Princess cruise ship, which was quarantined Feb. 4 in Japan. Nine of 700 infected passengers and crew died.

Based on the demographics of the ship’s population, he estimated that the U.S. fatality rate could be as low as 0.025% to 0.625% and the most at 1% – comparable to that of seasonal flu.

“If that is the true rate,” he wrote, “locking down the world with potentially tremendous social and financial consequences may be totally irrational. It’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.”

He called the pandemic “the perfect storm of that quest for very urgent, spectacular, exciting, apocalyptic results. And as you see, apparently our early estimates seem to have been tremendously exaggerated in many fronts.”

That was in reference to an Imperial College London forecast that the U.S. could suffer as many as 2.2 million deaths, with 1.01 million cases and 56,634 deaths as of April 28, far lower than the apocalyptic predictions of many.

He said the Imperial projection was miles off the mark. “They used inputs that were completely off in some of their calculation,” he says. “If data are limited or flawed, their errors are being propagated through the model…So if you have a small error, and you exponentiate that error, the magnitude of the final error in the prediction or whatever can be astronomical,” like a bricklayer a millimeter off at the beginning and a foot at the end.

He told the WSJ that early in his career that he realized “the common denominator for everything that I was doing was that I was very interested in the methods – not necessarily the results but how exactly you do that, how exactly you try to avoid bias, how you avoid error.” When he began examining studies, he discovered that few headline-grabbing findings could be replicated, and many were later contradicted by new evidence.

Ioannidis said “our early estimates seem to have been tremendously exaggerated in many fronts.” That may unsettle politicians and state governors who acted quickly to bring lockdowns with plenty of proof they worked, such as in Greece where closing non-essential businesses before a single death held down the number of cases and fatalities.

But while Ioannidis likened the pandemic to severe illnesses, he suggests that the reaction may have been too strong, which won’t comfort the friends and families of those who died from the virus as he provided a cold analytical look. 

“What we need is data. We need real data. We need data on how many people are infected so far, how many people are actively infected, what is really the death rate, how many beds do we have to spare, how has this changed,” he said, information likely not forthcoming as even the top medical officials in the Trump Administration were accused of bowing to him. 

He came up with his study at Stanford, using blood tests from 3,300 volunteers that estimated in the first week of April that 2.49-4.16 percent of the population of Santa Clara County was infected, implying a low fatality rate of 0.2 percent or under.

Now it was his turn to be criticized by some statisticians who questioned his methods, further adding to the confusion of a public first told not to wear masks and now being told in some states they will be mandatory. 

Ioannidis, who was quick to rip holes in other studies, then admitted his own wasn’t “bulletproof,” and he welcomed scrutiny from peers even as he said he believes the data will hold up and that antibody studies around the world will provide more data.


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