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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%.

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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.

  • Brilliant article, thanx.

    Can the deaths of the seven ship passengers be attributed to Conora-19 for sure? Or can one or more of these seven passengers simply have died there for other reasons, e.g. from quarantine stress?

    In Germany, according to the news seven of the approximately 1,080 first and second division soccer players tested positive (0.65 percent). In South Korea, 20 to 29 year olds have Corona-19 three times more often than the general population because of their many social contacts. Perhaps therefore 0.2 percent of the German population has Corona-19, which would result in 166,000 infected. Officially, we have around 11,000 cases in Germany today. The undisclosed number of infected people is definitely high.

    From March 11th to March 14th five of the seven footballer cases were reported (days of test result), from March 15th until March 19th two of the seven cases. Coincidence or maybe a sign of a decrease in infections? The cases became known on March 11th, 12th, 13th, 13th, 14th, 17th, 17th.

    • also writing from Germany. I agree that this crisis is very serious, but throwing the world economy into depression is not the solution. General lockdowns cannot last much longer or consequences will be catastrophic. Increasing the testing capacity and taking measurements to get better data are paramount. Isolating and treating people who are infected makes complete sense, a prolongued general lock down does not.

  • Absolutely! Finally someone knowledgeable has the guts and platform to stand up against mob madness.

  • Demoralizing to read – if an epidemiologist can’t get that you act without firm data because it’s an epidemic how can uneducated people understand this?

    Doing more damage than good, giving those who would not take preventive measures a foothold to their argument.

    This article depresses me more than this pandemic. But then I remember the greek economic disaster, and this view comes from somewhere – the inability to connect dots.

    Still sad though.

    • Have you even read the article? He acknowledges that you act without complete information. You always have to. But that doesn’t mean you do anything you can think of. If you suspect termites, you don’t burn your house down, even if that would solve that one, particular problem.

      I’m sure he understand everything you claim he doesn’t understand, considering it is literally his expertise.

  • Now I would like to thank you for being one of the few voices of reason in the so-called Corona pandemic. I had tried to spread the fact that in China the death rate is 2.17 or million and that the death rate of the normal flu last year in Norway was 314pr million.

    This seemed to be blocked in moderation everywhere. Then I found your rapid response in BMJ and could finally tell my OCD and hypocondriac patients that an authority expert agreed with my sobering anti anxiety calculations.

    I would just like to give you some more ideas that you probably have thought of yourself, but just in case. …

    There are only 2 numbers that we can know for sure for each country: the population in the country and the number of deaths from the virus. How many who are registered sick or healthy are extremely dependent on everyone who is sick being tested etc.
    How many who die per million in the country is also the only number that has practical significance. Whether you get Corona hard or easy means little as long as you survive.

    In China, it has killed 2.17 per million, in South Korea 1.05, in Iran 3.5.
    Influenza killed 314 per million last year e.g in Norway. Thus, ordinary flu is about 100 times as dangerous.

    If the virus is spread in the population and all people who are dying from something an therefore brought to the hospital are tested, we will get a lot of people who die WITH corona rather than FROM corona. This means that e.g. 30% of all people who would have died anyway from other causes would be counted as Corona deaths if 30% of the population are contaminated by Corona.

    This may be what is happening in Italy. Yearly death rate is around 1%. With a population of 60 million this comes to 600 000 pr year and 1644 pr day. Last daily deaths were 175.

    This could indicate that around 10% Of the population is infected, many without symptoms and thy die WITH corona in their bodies. When people die, they are tested.

    Isn’t it time to select 100 people at random and test them to find out how many are contaminated in the population?
    Maybe you could argue for this to be done in Denmark?

    Jeg går ut fra at syke som legges inn på sykehus testes for Corona. Siden det et ganske tilfeldig hvem som f.eks blir innlagt for ulykker, kan antallet som er smittet av disse fortelle oss hvor stor prosent som er smittet i folket, også uten symptomer. Man kan også se trenden. Dette blir nesten like godt utvalg som et såkalt randomisert utvalg.

    I assume that all patients admitted to hospitals are tested for Corona. Since it is quite random who is admitted for e.g accidents, the number infected smong these patients can tell us what percentage is infected in the population, even without symptoms. One can also see the trend. This is almost as good a selection as a so-called randomized sample.

  • This works up until the part about concluding that “maybe” 1% of the US will get it and “probably” the mortality rate is well below 1%. The second is a decent guess, based on what I’ve ran across, unless swamped critical care facilities result in the kind of triage China and Italy experienced, and then maybe not, then maybe it’s back to 2-3%. Where did the 1% infection come from? This implies it’s all not a big deal, but really .5% of 70% of the US population could die this year (a high-side infection estimate; 50% is more conservative). Let’s hope treatment options turn up.

  • > Flattening the curve to avoid overwhelming the health system is conceptually sound — in theory.

    In practice, many countries have enacted extreme measures before even scratching the health-system’s capacity. This means that the population pays the price, without receiving the benefit.

    Instead of “flattening” the curve, it eradicates it, crushing society along the way.

    • I do hope you shared this article with the author(s) of the article from Harvard. Unless you are just arguing from consensus in posting another tired repetition of what everyone has already heard.

  • No

    The current COVID 19 crisis cannot be reduced to numbers. This is not going in to be a data driven response as much as it will be a response based on the clinical experience. The reports from Wuhan presented something that was entirely unexpected and horrific. The emerging data from Italy confirms what we feared in January. No, this cannot be deemed a fiasco and those who argue this are just clinically naive.

    The virus and its mechanisms of protection and self-preservation are impressive. The science to date would confirm that we are dealing with something with vicious potential.

    We need to buy ourselves time and those who naysay the value of the blunt instruments at hand threaten us all. Yes, we are winging it for now. We will know more in due time. The level of contagion may be overstated. But we must work together.

    Those who are skeptics should think hard about how their own speculation may be all that is needed for some to disregard recommendations made in good faith that are intended for the greater good.

    • The recommendations are no doubt made in good faith, but they are based on inaccurate, incomplete, quite random ‚bits’ of information.

      A birds’ eye view of all the current world health issues, diseases, and causes of 60 million deaths per year, should take place, rather than the intense and exclusive focus on just one of many currently active and deadly diseases.

      I continue to wonder why the fear of this particular disease (in the presence of much more frightening and significant diseases at the same time), has caused the world to ‘jump off the cliff’ – seemingly without careful and balanced consideration of the consequences of THIS reaction, to the human race.

      Anna

    • Consider that the whole of Lombardy has 800 intensive care units with an ageing population of 10 million [1]. The fiasco in Italy could equally well be the result of an incompetent health care system that is overwhelmed by a simple flu. Also, look at Wuhan, just google a random image of it. It is an heavily air polluted city, where you can expect that respiratory infections are more fatal (+ maybe an incompetent health care system).

      Also, what is the natural mortality during the same time period of similar populations to the ones tested? In a population over the age of 80 year, 10-15% will day anyway over the course of the year (maybe even more, dont have the data at hand currently).

      Whatever is the case with regards to the mortality of the virus, it is a fact, that the worlds governments are basing their decisions on bad science.

      [1] https://www.reuters.com/article/us-health-coronavirus-italy-ethics-speci/all-is-well-in-italy-triage-and-lies-for-virus-patients-idUSKBN2133KG

  • A very interesting and well argued read. However, it does not take Korea into account, no lockdown there, but massive testing and strong isolation measures for those in close contact with someone who tests positive. Society continues, business continues and their mortality rate is under 1%.

  • Everyone that is dealing with this situation – virologists, politicians etc are simply humans with a variety of mindsets – we seem to expect that the medical profession are holier than thou and never fail with regard to making the right choices for humanity – they are not – they can make appalling decisions based on an individual personal need and a drive for self – and politicians are the same – we all are – to understand the human condition makes perfect sense of it all. Dr Wodarg – a German Virologist on a GreenMedInfo article explains it perfectly.

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