Did initial studies overestimate cases?

Infection and fatality trends from the coronavirus disease (Covid-19) pandemic appear to be lower than those projected by several models at the beginning of the

Infection and fatality trends from the coronavirus disease (Covid-19) pandemic appear to be lower than those projected by several models at the beginning of the crisis, according to Canadian-American physician and popular podcast host Peter Attia, who says the difference may be key to understanding when and if cities should be reopened.

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Models created by infectious disease experts predicted, for instance, that 200,000-2 million Americans could eventually die due to Covid-19.

These, Attia says, are wrong. “Today I suspect American fatalities from Covid-19 will be more in line with a very bad, perhaps the worst, season of influenza (The last decade saw flu deaths in the US range from 12,000 to 61,000, so you can imagine how much variability exists). This suggests Covid-19 will kill tens of thousands in the US this year, but likely not hundreds of thousands, and definitely not millions, as previously predicted,” he wrote in a blog post on his website.

The divergence could be due to two reasons, Attia explains. “Either the models were wrong because they incorrectly assigned properties about the biology of the virus pertaining to its lethality and/or ability to spread, or the models were correct, but as a society we changed our behavior enough in response to the threat of the virus to alter the outcome.”

At the heart of the modelling diversion, according to the physician, lies estimations of the virus’s basic reproduction number (R0).

“If the predictions were wrong because we misunderstood the biology of the virus (overstating its risk significantly) then we may start the process of thoughtful reintegration. (But) if the predictions were wrong because we understood the biology, modelled it correctly, and appropriately put into place extreme social distancing measures then we need to continue strict social distancing until we have effective treatments,” he writes.

Determining which case is true will give more insight into how the virus behaves, letting countries make “better decisions with at least some modicum of confidence, versus blind certainty in models that don’t have the humility to incorporate a margin of error or degree of uncertainty”.

Thus, Attia concludes, it is crucial to find out how many people may have been asymptomatic to accurately determine the virus’s lethality. “Either way, we need to broadly test people with no symptoms, which is something we have not done so far in an area hit as hard as New York city,” he writes.