My new article examines uncertainties in covid-19 data, from infection to death rates. While some complain that pandemic predictions have been exaggerated for social or political gain, that’s not necessarily true; journalism always exaggerates dangers, highlighting dire predictions. But models are only as good as the data that goes into them, and collecting valid data on disease is inherently difficult. People act as if they have solid data underlying their opinions, but fail to recognize that we don’t have enough information to reach valid conclusion…
There’s nothing quite like an international emergency—say, a global pandemic—to lay bare the gap between scientific models and the real world, between projections and speculations and what’s really going on in cities and hospitals around the world.
A previous article discussed varieties of information about COVID-19, including information that’s true; information that’s false; information that’s trivially true (true but unhelpful); and speculation, opinion, and conjecture. Here we take a closer look at the role of uncertainty in uncertain times.
Dueling Projections and Predictions
The record of wrong predictions about the coronavirus is long and grows by the hour. Around Valentine’s Day, the director of policy and emergency preparedness for the New Orleans health department, Sarah Babcock, said that Mardi Gras celebrations two weeks later should proceed, predicting that “The chance of us getting someone with coronavirus is low.” That projection was wrong, dead wrong: a month later the city would have one of the worst outbreaks of COVID-19 in the country, with correspondingly high death rates. Other projections have overestimated the scale of infections, hospitalizations, and/or deaths.
It’s certainly true that many, if not most, news headlines about the virus are scary and alarmist; and that many, if not most, projections and predictions about COVID-19 are wrong to a greater or lesser degree. There’s a plague of binary thinking, and it’s circulating in many forms. One was addressed in the previous article: that of whether people are underreacting or overreacting to the virus threat. A related claim involves a quasi-conspiracy that news media and public health officials are deliberately inflating COVID-19 statistics. Some say it’s being done to make President Trump look incompetent at handling the pandemic; others say it’s being done on Trump’s behalf to justify coming draconian measures including Big Brother tracking.
Many have suggested that media manipulation is to blame, claiming that numbers are being skewed by those with social or political agendas. There’s undoubtedly a grain of truth to that—after all, information has been weaponized for millennia—but there are more parsimonious (and less partisan) explanations for much of it, rooted in critical thinking and media literacy.
The Media Factors
In many cases, it’s not experts and researchers who skew information but instead news media who report on them. News and social media, by their nature, highlight the aberrant extremes. Propelled by human nature and algorithms, they selectively show the worst in society—the mass murders, the dangers, the cruelty, the outrages, and the disasters—and rarely profile the good. This is understandable, as bad things are inherently more newsworthy than good things.
To take one example, social media was recently flooded with photos of empty store shelves due to hoarding, and newscasts depict long lines at supermarkets. They’re real enough—but are they representative? Photos of fully stocked markets and calm shopping aren’t newsworthy or share-worthy, so they’re rarely seen (until recently when they in turn became unusual). The same happens when news media covers natural disasters; journalists (understandably) photograph and film the dozens of homes that were flooded or wrenched apart by a tornado, not the intact tens or hundreds of thousands of neighboring homes that were unscathed. This isn’t some conspiracy by the news media to emphasize the bad; it’s just the nature of journalism. But this often leads to a public who overestimates the terrible state of the world—and those in it—as well as fear and panic.
Another problem are news stories (whether about dire predictions or promising new drugs or trends) that are reported and shared without sufficient context. An article in Health News Review discussed the problem of journalists stripping out important caveats: “Steven Woloshin, MD, co-director of the Center for Medicine and Media at The Dartmouth Institute, said journalists should view preprints [rough drafts of journal studies that have not been published nor peer-reviewed] as ‘a big red flag’ about the quality of evidence, similar to an animal study that doesn’t apply to humans or a clinical trial that lacks a control group. ‘I’m not saying the public doesn’t have the right to know this stuff,’ Woloshin said. ‘But these things are by definition preliminary. The bar should be really high’ for reporting them. In some cases, preprints have shown to be completely bogus … . Readers might not heed caveats about ‘early’ or ‘preliminary’ evidence, Woloshin said. ‘The problem is, once it gets out into the public it’s dangerous because people will assume it’s true or reliable.’”
One notable example of an unvetted COVID-19 news story circulating widely “sprung from a study that ran in a journal. The malaria medicine hydroxychloroquine, touted by President Trump as a potential ‘cure,’ gained traction based in part on a shaky study of just 42 patients in France. The study’s authors concluded that the drug, when used in combination with an antibiotic, decreased patients’ levels of the virus. However, the findings were deemed unreliable due to numerous methodological flaws. Patients were not randomized, and six who received the treatment were inappropriately dropped from the study.” Recently, a Brazilian study of the drug was stopped when some patients developed heart problems.
Uncertainties in Models and Testing
In addition to media biases toward sensationalism and simplicity, experts and researchers often have limited information to work with, especially in predictions. There are many sources of error in the epidemiological data about COVID-19. Models are only as good as the information that goes into them; as they say: Garbage In, Garbage Out. This is not to suggest that all the data is garbage, of course, so it’s more a case of Incomplete Data In, Incomplete Data Out. As a recent article noted, “Models aren’t perfect. They can generate inaccurate predictions. They can generate highly uncertain predictions when the science is uncertain. And some models can be genuinely bad, producing useless and poorly supported predictions … .” But as to the complaint that the outbreak hasn’t been as bad as some earlier models predicted, “earlier projections showed what would happen if we didn’t adopt a strong response, while new projections show where our current path sends us. The downward revision doesn’t mean the models were bad; it means we did something.”
One example of the uncertainty of data is the number of COVID-19 deaths in New York City, one of the hardest-hit places. According to The New York Times, “the official death count numbers presented each day by the state are based on hospital data. Our most conservative understanding right now is that patients who have tested positive for the virus and die in hospitals are reflected in the state’s official death count.”
All well and good, but “The city has a different measure: Any patient who has had a positive coronavirus test and then later dies—whether at home or in a hospital—is being counted as a coronavirus death, said Dr. Oxiris Barbot, the commissioner of the city’s Department of Health. A staggering number of people are dying at home with presumed cases of coronavirus, and it does not appear that the state has a clear mechanism for factoring those victims into official death tallies. Paramedics are not performing coronavirus tests on those they pronounce dead. Recent Fire Department policy says that death determinations on emergency calls should be made on scene rather than having paramedics take patients to nearby hospitals, where, in theory, health care workers could conduct post-mortem testing. We also don’t really know how each of the city’s dozens of hospitals and medical facilities are counting their dead. For example, if a patient who is presumed to have coronavirus is admitted to the hospital, but dies there before they can be tested, it is unclear how they might factor into the formal death tally. There aren’t really any mechanisms in place for having an immediate, efficient method to calculate the death toll during a pandemic. Normal procedures are usually abandoned quickly in such a crisis.”
People who die at home without having been tested of course won’t show up in the official numbers: “Counting the dead after most disasters—a plane crash, a hurricane, a gas explosion, a terror attack or a mass shooting, for example—is not complex. A virus raises a whole host of more complicated issues, according to Michael A.L. Balboni, who about a decade ago served as the head of the state’s public safety office. ‘A virus presents a unique set of circumstances for a cause of death, especially if the target is the elderly, because of the presence of comorbidities,’ he said—multiple conditions. For example, a person with COVID-19 may end up dying of a heart attack. ‘As the number of decedents increase,’ Mr. Balboni said, ‘so does the inaccuracy of determining a cause of death.’”
So while it might seem inconceivably Dickensian (or suspicious) to some that in 2020 quantifying something as seemingly straightforward as death is complicated, this is not evidence of deception or anyone “fudging the numbers” but instead an ordinary and predictable lack of uniform criteria and reporting standards. The international situation is even more uncertain; different countries have different guidelines, making comparisons difficult. Not all countries have the same criterion for who should be tested, for example, or even have adequate numbers of tests available.
In fact, there’s evidence suggesting that if anything the official numbers are likely undercounting the true infections. Analysis of sewage in one metropolitan area in Massachusetts that officially has fewer than 500 confirmed cases revealed that there may be exponentially more undetected cases.
Some people have complained that everyone should be tested, suggesting that only rich are being tested for the virus. There’s a national shortage of tests, and in fact many in the public are being tested (about 1 percent of the public so far), but such complaints rather miss a larger point: Testing is of limited value to individuals.
Testing should be done in a coordinated way, starting not with the general public but instead with the most seriously ill. Those patients should be quarantined until the tests come back, and if the result is positive, further measures should be taken including tracking down people who that patient may have come in contact with; in Wuhan, for example, contacts were asked to check their temperature twice a day and stay at home for two weeks.
But testing people who may be perfectly healthy is a waste of very limited resources and testing kits; most of the world is asymptomatic for COVID-19. Screening the asymptomatic public is neither practical nor possible. Furthermore, though scientists are working on creating tests that yield faster and more accurate results, the ones so far have taken days. Because many people who carry the virus show no symptoms (or mild symptoms that mimic colds or even seasonal allergies), it’s entirely possible that a person could have been infected between the time they took the test and gotten a negative result back. So, it may have been true that a few days, or a week, earlier they hadn’t been infected, but they are now and don’t know it because they are asymptomatic or presymptomatic. The point is not that the tests are flawed or that people should be afraid, but instead that testing, by itself, is of little value to the patient because of these uncertainties. If anything, it could provide a false sense of security and put others at risk.
As Dr. Paul Offit noted in a recent interview, testing for the virus is mainly of use to epidemiologists. “From the individual level, it doesn’t matter that much. If I have a respiratory infection, stay home. I don’t need to find out whether I have COVID-19 or not. Stay home. If somebody gets their test and they find out they have influenza, they’ll be relieved, as compared to if they have COVID-19, where they’re going to assume they’re going to die matter how old they are.”
If you’re ill, on a practical level—unless you’re very sick or at increased risk, as mentioned above—it doesn’t really matter whether you have COVID-19 or not because a) there’s nothing you can do about it except wait it out, like any cold or flu; and b) you should take steps to protect others anyway. People should assume that they are infected and act as they would for any communicable disease: isolate, get rest, avoid unnecessary contact with others, wash hands, don’t touch your face, and so on.
A version of this article appeared on the CFI Coronavirus Response Page, here.
Part 2 will be posted in a few days.