The current coronavirus disease, Covid-19, has been called a once-in-a-century pandemic. But it may also be a once-in-a-century evidence fiasco.

At a time when everyone needs better information, from disease modelers and governments to people quarantined or just social distancing, we lack reliable evidence on how many people have been infected with SARS-CoV-2 or who continue to become infected. Better information is needed to guide decisions and actions of monumental significance and to monitor their impact.

Draconian countermeasures have been adopted in many countries. If the pandemic dissipates — either on its own or because of these measures — short-term extreme social distancing and lockdowns may be bearable. 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?

advertisement

Vaccines or affordable treatments take many months (or even years) to develop and test properly. Given such timelines, the consequences of long-term lockdowns are entirely unknown.

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 don’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.

advertisement

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 — 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 — there were just seven deaths among the 700 infected passengers and crew — 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 frequencies of chronic diseases — a risk factor for worse outcomes with SARS-CoV-2 infection — 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%.

STAT Reports: STAT’s guide to interpreting clinical trial results

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. 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.

Could the Covid-19 case fatality rate be that low? No, some say, pointing to the high rate in elderly people. However, even some so-called mild or common-cold-type coronaviruses that have been known for decades can have case fatality rates as high as 8% when they infect elderly people in nursing homes. In fact, such “mild” coronaviruses infect tens of millions of people every year, and account for 3% to 11% of those hospitalized in the U.S. with lower respiratory infections each winter.

These “mild” coronaviruses may be implicated in several thousands of deaths every year worldwide, though the vast majority of them are not documented with precise testing. Instead, they are lost as noise among 60 million deaths from various causes every year.

Although successful surveillance systems have long existed for influenza, the disease is confirmed by a laboratory in a tiny minority of cases. In the U.S., for example, so far this season 1,073,976 specimens have been tested and 222,552 (20.7%) have tested positive for influenza. In the same period, the estimated number of influenza-like illnesses is between 36,000,000 and 51,000,000, with an estimated 22,000 to 55,000 flu deaths.

Note the uncertainty about influenza-like illness deaths: a 2.5-fold range, corresponding to tens of thousands of deaths. Every year, some of these deaths are due to influenza and some to other viruses, like common-cold coronaviruses.

In an autopsy series that tested for respiratory viruses in specimens from 57 elderly persons who died during the 2016 to 2017 influenza season, influenza viruses were detected in 18% of the specimens, while any kind of respiratory virus was found in 47%. In some people who die from viral respiratory pathogens, more than one virus is found upon autopsy and bacteria are often superimposed. A positive test for coronavirus does not mean necessarily that this virus is always primarily responsible for a patient’s demise.

If we assume that case fatality rate among individuals infected by SARS-CoV-2 is 0.3% in the general population — a mid-range guess from my Diamond Princess analysis — and that 1% of the U.S. population gets infected (about 3.3 million people), this would translate to about 10,000 deaths. This sounds like a huge number, but it is buried within the noise of the estimate of deaths from “influenza-like illness.” If 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. The media coverage would have been less than for an NBA game between the two most indifferent teams.

Some worry that the 68 deaths from Covid-19 in the U.S. as of March 16 will increase exponentially to 680, 6,800, 68,000, 680,000 … along with similar catastrophic patterns around the globe. Is that a realistic scenario, or bad science fiction? How can we tell at what point such a curve might stop?

The most valuable piece of information for answering those questions would be to know the current prevalence of the infection in a random sample of a population and to repeat this exercise at regular time intervals to estimate the incidence of new infections. Sadly, that’s information we don’t have.

In the absence of data, prepare-for-the-worst reasoning leads to extreme measures of social distancing and lockdowns. Unfortunately, we do not know if these measures work. School closures, for example, may reduce transmission rates. But they may also backfire if children socialize anyhow, if school closure leads children to spend more time with susceptible elderly family members, if children at home disrupt their parents ability to work, and more. School closures may also diminish the chances of developing herd immunity in an age group that is spared serious disease.

This has been the perspective behind the different stance of the United Kingdom keeping schools open, at least until as I write this. In the absence of data on the real course of the epidemic, we don’t know whether this perspective was brilliant or catastrophic.

Flattening the curve to avoid overwhelming the health system is conceptually sound — in theory. A visual that has become viral in media and social media shows how flattening the curve reduces the volume of the epidemic that is above the threshold of what the health system can handle at any moment.

Yet if the health system does become overwhelmed, the majority of the extra deaths may not be due to coronavirus but to other common diseases and conditions such as heart attacks, strokes, trauma, bleeding, and the like that are not adequately treated. If the level of the epidemic does overwhelm the health system and extreme measures have only modest effectiveness, then flattening the curve may make things worse: Instead of being overwhelmed during a short, acute phase, the health system will remain overwhelmed for a more protracted period. That’s another reason we need data about the exact level of the epidemic activity.

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. Unpredictable evolutions may ensue, including financial crisis, unrest, civil strife, war, and a meltdown of the social fabric. At a minimum, we need unbiased prevalence and incidence data for the evolving infectious load to guide decision-making.

In the most pessimistic scenario, which I do not espouse, if the new coronavirus infects 60% of the global population and 1% of the infected people die, that will translate into more than 40 million deaths globally, matching the 1918 influenza pandemic.

The vast majority of this hecatomb would be people with limited life expectancies. That’s in contrast to 1918, when many young people died.

One can only hope that, much like in 1918, life will continue. Conversely, with lockdowns of months, if not years, life largely stops, short-term and long-term consequences are entirely unknown, and billions, not just millions, of lives may be eventually at stake.

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.

John P.A. Ioannidis is professor of medicine and professor of epidemiology and population health, as well as professor by courtesy of biomedical data science at Stanford University School of Medicine, professor by courtesy of statistics at Stanford University School of Humanities and Sciences, and co-director of the Meta-Research Innovation Center at Stanford (METRICS) at Stanford University.

  • An immoral comment by a Stanford expert, seeking PR, and hiding behind “we need to know if we do more harm than good”.
    This contagion is not a scientific experiment upon which a logical conclusion can be reached.
    This is a time full of irreducible uncertainty when willed DECISIONS are made in absence of facts or reliable models.

    Consider the trope of “flattening the curve” by lockdown and social distancing – the quarantine-light: the experts forget to tell us that flattening the curve is a secondary effect – the primary effect is the REDUCTION OF TOTAL number of diseased due to lesser chances for virus transmission!!! How can these experts, hiding the obvious, be our guides during this time!!!
    Groupthink, doublespeak, corporate solidarity, vanity – all amply displayed in the professor’ article.

    We live in troubled times: every one with an opinion, and access to the Internet is compelled to publish…. So there are thousands of truths.
    And there is none.

    • Aside from your rampant unnecessary capitalization – which makes your vitriolic, frenzied post nearly unable to be read… the absence of the real data from which to expound upon is a fact, not an opinion.

  • How refreshing to read a balanced, rational exposition of the problem. There have been far too many knee jerk reactions and far too few, if any, cases of thinking through measures with regard to possible consequences.

  • Ok, another opinion, but you can’t calculate the death rate by simply using # of known infections as a denominator. You have to calculate # of deaths per # of people who were infected at the same time those who died were infected. That would be an actual death rate. The Lancet (peer reviewed), basing their calculations on Wuhan numbers and a 14 day infection rate, places that at about 15% and potentially as high as 20%. Meanwhile, any inclusion of asymptomatic infections to a calculation of death rates is immaterial because we already know that in countries where the response is lax, #s of deaths per #s of critically ill patients entering hospitals is more than even the best health care systems can endure. Hence, intervention is now widely accepted as the only reasonable way forward. Reduce exposure. Flatten the curve.

  • Finally some logic in a world where even common sense has escaped us.

    Data is a very tricky thing! even determining whether or not we have enough data can also be extremely difficult to conclude.

    But that does not justify an overreaction. On the contrary, that is exactly when you would want “cooler heads prevail”.

    In addition to Dr. Ioannidis’ elephant metaphor, I want to add another metaphor … what we are currently doing is trying to kill a fly with a sledge-hammer and we keep missing the fly!!!!

  • Take a look at the timeline. Trump, in Feb. 2018, cuts CDC funding by 80% for tracking worldwide epidemics in an order to save money. CDC goes on the record by saying that it could cost thousands of American lives. Trump claims “Fake News” about anything and everything while showing proof of his “genius” because of a booming stock market. Everything main stream media (MSM) has done hasn’t worked to shine a light on the disaster that is Trump. (I’m not a fan of either side of the same corrupt political coin) It doesn’t look like Biden or Sanders can beat Trump in November. Biden is no more than an old, white, more senile, version of Hillary and Sanders is too controversial. A virus comes along that is not all that dangerous but MSM now has their ultimate weapon. It is just deadly enough to scare everyone with an IQ under 100 and it will destroy the value of the stock market. So the MSM has proof that Trump’s policies have “caused” thousands to die whilst providing undeniable evidence that he is no stock market financial wizard. They finally have something that will stick, no matter what. The MSM will beat this to death until the November election. It won’t matter what science has to say about it since it is now a political issue. Just watch…this will go away after Trump is defeated and not until.

    • Nailed that one on the head! That’s my analysis as well. The politicization of a natural disaster / event all while the orange idiotic autocrat is at the helm. The word pandemic is being thrown around as if it has to do with the severity of illness. It has to do with the spread of the disease and people are being easily scared over a virus that so far has globally caused 5% of the deaths from the 2009-2010 H1N1pdm09 virus.

  • To me it seems that we have over reacted big time .
    One hates to compare flu and similar viruses to coronavirus but you have to.
    Every year globally flu kills 60 million to 100 million. Th media headlines “Don’t Panic” cause people to panic!

    • That’s the problem,you’re not thinking.You’re wanting to blame the media for reporting facts

  • Excellent and well-reasoned analysis!

    I’d also be eager for them to study how well climate models have tracked over the years.

Comments are closed.

A roundup of STAT’s top stories of the day in science and medicine

Privacy Policy