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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?

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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.

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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.

  • People are coming around to this way of thinking. The mainstream media has lied to us so many, many times before. Russia, Russia, Russia; the Mueller Probe, Kavanaugh, the Ukraine Phone Call, and now this. How did it come so neatly in sequence when they failed with the Phone Call? Don’t live in the lie.

  • After reading this article I still say it is not a pandemic here in the US. However, looking at it as a whole you could call it a pandemic. This Coronavirus is absolutely nothing new. It was studied in 1970, that was 50 years ago. To this date nothing has been developed to combat the virus, well go figure. This COVID-19 is a new strain of Coronavirus – SARS to which both are respiratory viruses. My concern here is why hasn’t the CDC and WHO (World Health Organization) been monitoring this? One would think something this serious they would have kept up with it. Now look at us and what happened.

  • Interesting argument (hopefully the author is correct!), but doesn’t explain why the situation in Italy is so dire, and their hospitals and ICUs are overwhelmed. That’s not an incorrect calculation or an overly pessimistic assumption somewhere on a computer model, but actual doctors and nurses having to decide who lives and who dies because there are not enough ventilators, or so reports say. Maybe the news from Italy are grossly exaggerated and they are just having a bad flu season getting blown out of proportion by the media? It would be great if the author could elaborate on this.

    • I thought about Italy too and a few questions I had we’re, the first one was how many people were really in hospitals there with covid 19? This is what I found
      35,713 total cases (.06% of the population)
      2,978 deaths (.00005% of population)
      2,257 currently hospitalized (.00004% of population)

      But how many people normally get hospitalized with the flu?
      I don’t know about Italy but in the US it’s
      9.3 million to 49 million illnesses each year and the flu results in 31.4 million outpatient visits and more than 200,000 hospitalizations each year.

      If Italy 1/5th the size of the US is equivalent that would be 40,000 a year in hospitalizations.

      3,000 is a lot at one time and above normal probably but their population is much older I believe. SO It’s not nothing and it is more dangerous to the elderly but it may not warrant this over reaction we’re giving it.

  • Yes. But if you have a sudden very high rate of sick/death people spreading in an specific pattern, no data, no experience. You have no choice but improvising with common sense. And most of the time they are not common people decisions, but professional experienced people improvising… Better than flying blind.
    I hope the world takes this lesson and gets better prepared for something like this in the future.

  • I also believe the cost of shutting down society, if it extends much beyond this first couple of weeks, will cause more devastation than this virus. Instead of locking everyone down, our efforts should be directed toward insulating those most at risk and those who live with a member of that group. I’m not saying the virus should be ignored, but I think the reaction to it is lacking in common sense and proportionality.

  • Assuming that a very high proportion of very healthy people will only experience mild symptoms and that these survivors will have developed antibodies and thus not pose any threat to the more vulnerable members of the community, once they recover, I suggest that very healthy volunteers be asked to deliberately expose themselves to the virus in a very controlled environment ie complete isolation with close monitoring and testing. Part of the problem seems to be that we can’t easily identify individuals who have become infected and survived. These survivors are a very valuable resource. This strategy would allow us to know who they are for sure.

    This group could become caregivers for the most vulnerable members of society and continue to provide essential services while everyone is in lockdown for one month. This would knock the whole problem on the head within a very short time.

    I would consider volunteering for this. I have had the flu only twice in the last 40 years and never get sick in Nepal where I work for five months every year … in spite of never taking any shots. I believe I have an exceptional natural immune system and would be happy to volunteer.

  • An extremely cogent arguement, expressed from the sidelines: but when a health service has been overwhelmed and people are dying from anything other than COVID-19 because they are in a long queue waiting for medical treatment, I am positive they will understand that it is all because of a lack of data.
    Decisions have to be made on the available evidence and data available at the time – however scanty.
    The disease was presented to the world in January 2020 with the Chinese in full Hazmat suits; this gives food for thought. Do I believe the Chinese are stupid people, if the answer is no then I must conclude that something very serious is scaring the pants off them. The BBC in a report from Hongkong stated and I quote “people in Wuhan are escaping from hospital” – now, why would anyone use the word escaping – escaping from what – usually a person goes to hospital to be cured. The people in Hongkong were obviously very disturbed also.
    It seems to me that when accurate data is absent we have to try and make the best judgement call.
    Everything the professor says is credible but someone sometime has to make a decision and if it proves to be correct believe it or not we will never know for sure.

  • The government should turn every cruise ship into floating quarantine ICU for CoVid-19 patients who are seriously ill. Anchor these ships near hot spots with ICU overflows and problems. Two little Navy hospital ships will not cut it. Do this NOW “Cruise Ships for CoVid-19 Treatment Hospitals.”

    • In addition to Douglas’ comment, that seems like a wildly impractical way to add hospital capacity compared to alternatives. Better to have the Army and National Guard build field hospitals in hospital parking lots if that’s what we need.

      In addition, I don’t think that addresses the real bottleneck on treatment capacity, which is probably trained people. Some stats from a blog post at the Society for Critical Care Medicine:

      – U.S. acute care hospitals are estimated to own approximately 62,000 full-featured mechanical ventilators

      – The addition of older hospital ventilators, Strategic National Stockpile ventilators, and anesthesia machines increases the absolute number of ventilators to possibly above 200,000 units. Many of the additional and older ventilators, however, may not be capable of adequately supporting patients with severe acute respiratory failure.

      – An analysis of the literature suggests that U.S. hospitals could absorb between 26,000 and 56,000 additional ventilators at the peak of a national pandemic, as safe use of ventilators requires trained personnel.

      The third of those sounds like the bottleneck on capacity.

      The only use I can think of for cruise ships would be if we need to forcibly quarantine a bunch of people who are confirmed to have COVID-19 because too many of them refuse to adhere to strict self-quarantine guidelines. If we reach that point, we’d also have other options such as hotels – plenty of those are empty enough they could be available – or military bases. The advantage of a cruise ship for quarantine is that it’s very easy to monitor that nobody is getting off a cruise ship, even if it’s docked. (Don’t know if a docked cruise ship requires any crew, but I suspect it could be minimal.)

  • FINALLY! Someone other than a talking head or blow-hard Op-Ed author communicating some common sense. This virus is serious and should be taken seriously, just as the flu and other nasty viruses current and in the past. However, we are making history of the worst kind. The measures currently being taken are going to go down in the history books as one, if not the, worst economic disasters ever in the modern world. You do not have to be a statistician or PhD to look at the numbers and see they do not add up. The exponential projections and “we are all going to die” mantras that are repeated ad nauseam in article after article are not proving to be true. Think back, better go actually look back, at news stories over the last decade. We have had numerous instances of flu and similar sweep through nursing homes, bad flu seasons, etc. This time though, these same stories are being framed by a constant barrage of negativity at the same time business are being shuttered, schools closed, and quarantine directives. Draconian at the least. This is causing unprecedented economic disaster. It is going to cause, I fear, irreversible harm to our children, and God forbid, if, no when, we ever have a real pandemic sweep across the globe that actually kills the healthy, not just the very elderly or those with severe preexisting conditions, we will have been acclimated to not take it seriously and then we will see real destruction. The destruction this time around is going to be man-made and far worse than that caused by a mutating cold-virus in and of itself. You can reply with “what about Italy”, it “is different than the flu” and “#flattenthecurve” all you want, but take a step back and think clear and logically, we’ve run into worse viruses before, it was just another news story and yet here we are, this time we are under constant attack the sky is falling, our inept leaders and government has responded in kind and set us back who knows how long economically. If you can’t see this, God have mercy on you. This is a result of our reliance on social media and inept “journalism” over the last decade rather than using clear logic and data, as the author has perfectly laid out.

  • Fear mongering over economic damage is just as counter-productive. Where is the evidence to back this statement up? “…with lockdowns of months… consequences are entirely unknown, and billions, not just millions, of lives may be eventually at stake.” Let’s take a pessimistic scenario: based on statistics reported by Galbraith in The Great Crash 1929, the suicide rate in the United States increased from 17.0 per 100,000 people in 1929 to 21.3 in 1932. That is equivalent to an increase of 14,061 deaths over 1 year in the US now. Compare that to 408,750 from Covid-19 at 25% infection rate and 0.5% CFR. (25% being a low estimate from https://www.statnews.com/2020/03/16/lower-coronavirus-death-rate-estimates/)

    Staying in lockdown till everyone is vaccinated sounds extreme, but that’s NOT the plan for most countries. It is to flatten the curve for a month or two, so we can go back to containment for some areas, so medical equipment and staff can ramp up, so we finally get enough test kits and masks. See Hong Kong, Singapore, Japan; their early response kept infection rates and CFR low and now commercial activity is steadily returning to normal.

    Lastly, over-reacting now is far less costly over the long run than under-reacting. If better estimates in the next few weeks show a much lower infection rate or CFR, we can undo lockdowns quickly and go back to our lives. If worse estimates come in and we under-reacted, there is little we can then do to avoid a tragedy. The correct response to uncertainty then is to err on the side of caution.

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