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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.

  • Outstanding article. It is stunning we don’t have a comprehensive population virus testing for an entire outbreak hotspot area.

    Instead we are collectively treating everyone with the same prescription for the same “disease” which may or may not be present and may or may not be high risk for mortality and may or may not be present for potential infection to others.

    Flattening the curve will not work out well in the end. It will ensure the risk lasts longer for those who really are at high risk and create additional problems for those who need treatment for any illness this virus or otherwise.

    To the point there is a human factor where we can can handle an overwhelming number of patients for a small window of time. But stretch that out over months and systems start to fail.

    Best plan is to encourage high risk individuals to take necessary measures for prevention and let the virus pass through the rest of the population as it may.

    The alternative path, which we seem to be traveling now, will have a host of unintended consequences.

    • I disagree with elements of your “best plan.” I think that with this current SARS-CoV-2 outbreak that we do run the potential to overwhelm the capacity of our healthcare infrastructure and that has the potential to drastically increase morbidity and mortality. A proactive public health approach is necessary to control the spread as much as possible so that when the high-risk are infected they have a healthcare system that is able to adequately treat them.

  • Thank you sir for a well reasoned and rational approach to this. I can’t help but think of the resilience that some prior generations had here in the West. Now when we need to dig new wells of hope and calm strength, where do we find these things? President Trump has been steadfast.

  • How do you explain what is happening in Italy right now? Today nearly 500 deaths, are they overreacting? This article is total BS

  • Nicely articulated skepticism. We must use logic, reason, and common sense if we can’t get channel-specific random samples. The Epi-models project an 81% total infection rate. Diamond Princess had 20%. I can’t reconcile this. Those insisting on extreme measures owe us a duty to reconcile this, IMO.

  • Thank God someone is rational about the irrational reaction to this virus! Everything I been thinking exactly. This irrational reaction of shutting down the world is going to cause many, many more deaths to come than this virus. How about a rational quarantine to bring down the curve? We have data to support what population is most at risk. If 6% is going to inundate the hospital system, why not quarantine that population instead of shutting down the world? It may not be perfect, but way better than what we are doing. Also evidence shows that Covid 19 may have existed since mid Nov. in China and mid Jan in the US has surfaced. What if it had already sweep through China which would explain the low recent transmission rate; and all the scary data gather is only from a major outbreak which drastically skew the fatality rate?

  • A very sober and reasoned analysis. It is my non-expert opinion the consequences of the fear of CV will far outweigh the damage the pathogen actually inflicts. I will want to know, when this blows over, why the excessive hysteria? The H1N1 killed around 13,000 Americans in 2009-10, and hardly a feather was ruffled.

  • my question is this, how are we going to have accurate numbers? Many here, in the US, are going to the hospitals, clinics, doctors, etc. but are not being tested even when there is high suspicions of being positive and just being sent home and told to self quarantine.

  • What do you think caused Italy’s apparent disaster then? Do you think they overreacted?

    • Been stated many times. One of the oldest population in the world, with a culture that is very warm with each other.

    • (Bloomberg) — More than 99% of Italy’s coronavirus fatalities were people who suffered from previous medical conditions, according to a study by the country’s national health authority. After deaths from the virus reached more than 2,500, with a 150% increase in the past week, health authorities have been combing through data to provide clues to help combat the spread of the disease.

      The new study could provide insight into why Italy’s death rate, at about 8% of total infected people, is higher than in other countries. The Rome-based institute has examined medical records of about 18% of the country’s coronavirus fatalities, finding that just three victims, or 0.8% of the total, had no previous pathology. Almost half of the victims suffered from at least three prior illnesses and about a fourth had either one or two previous conditions. More than 75% had high blood pressure, about 35% had diabetes and a third suffered from heart disease.

      The average age of those who’ve died from the virus in Italy is 79.5. As of March 17, 17 people under 50 had died from the disease. All of Italy’s victims under 40 have been males with serious existing medical conditions.

    • Here’s a guess about Italy. First, the Italian government signed an agreement with China for infrastructure improvement. The Chinese provide loans and a lot of labor, so there are many Chinese people coming and going to Italy. That could be a source of infection. Second, Italy has a very high rate of smoking, and as this attacks the respiratory system, the population may be more vulnerable. Finally, it seems like Wuhan Flu is hardest on older people. Demographically, Italy has the oldest population in Europe.
      I repeat, I have only created a hypothesis which is based on three indisputable facts.

    • As with the princess cruise, a large majority of Italy’s population is elderly which we already know have a higher mortality rate with the virus. Italy is very different from the US where they have tighter controls and regulations on immigration while the younger generation continues to leave the country. This results in a skewed median age within Italy and what is most likely contributing to the higher mortality rate we are seeing from COVID-19. As the author of the article was mentioning, our approach to testing may very well be imparting bias into the data.

  • my question is this, how are we going to have accurate numbers if people, esp. here in the US, are going to the hospital, are not being tested and just sent home with high suspicions of being positive and just told to self quarantine?

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