The Slatest

The False Certainty of the Curve

The data doesn’t tell the whole story of what’s happening right now.

A sign reading "Save lives, flatten the curve" over a highway.
A digital sign is shown on the Wantagh State Parkway that reads “Save Lives #Flatten The Curve” on March 19 in Wantagh, New York.
Al Bello/Getty Images

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What will be the defining image of this catastrophe? Other historical disasters are inextricably linked to some visible marker: burning towers, a mushroom cloud, rows of emaciated prisoners behind a barbed wire fence. Perhaps it will be the face masks that are ubiquitous on streets around the world. Perhaps the shuttered storefronts, empty streets, and barren supermarket shelves. But these are the ancillary effects of the virus, not the crisis itself. In this disaster, the true horror is safely sequestered out of view—for most of us, anyway—in ICUs and nursing homes.

But we will all remember “the curve.” When the virus first spread globally in March, we quickly learned of the critical need to “flatten the curve”—that is, keep the growth of new infections at a manageable rate to avoid overwhelming hospitals. Since then, those of us not on the front lines have mostly had to follow its progress through numbers, statistics, and probabilities—of which the curve has been the most iconic representation. A plethora of sources allows us to break down daily totals of infections, hospitalizations, and deaths by country, state or region; debate the merits of absolute numbers versus per capita; and compare different universities’ projection models. Every day, we inspect the curve for signs of flattening, any indications that we’ve reached the peak or are at least starting to approach it. New York Gov. Andrew Cuomo’s popular daily briefings are master classes in data visualization, even as major flaws in New York’s data collection are emerging.

I’m as guilty of daily curve watching as anyone, but it’s probably not the best way to understand the reality of this crisis. For one thing, there are serious questions about what the numbers are really telling us. In the most blatant cases, some governments appear to be deliberately misreporting their data. Others are just unreliable. As John Burn-Murdoch, the journalist who produces the Financial Times’ elegant and widely shared daily coronavirus charts, recently noted: “Vast amounts of the data coming in from individual countries is essentially junk. Take Ecuador, where according to the data, deaths are trending downwards into single digits, yet literal vultures are circling overhead as coffins lie in the streets.”

Other governments are simply leaving out critical amounts of data. France’s coronavirus cases recently jumped 61 percent over two days because the health ministry had previously not been collecting data from nursing homes. New York’s death toll similarly jumped by 3,700 overnight this week when officials began counting people who had never tested for the virus but were presumed to have died from it. Comparing the trajectories of different countries and regions might be a meaningless endeavor given the discrepancies in data collection and testing across borders.

And while the frustrations of lockdown make us all desperate for signs of progress, there’s simply not that much to be learned from day-to-day changes. Longer-term trends are what will tell us if we’re making progress, even if the wait for that kind of data feels excruciatingly long.

And for all their supposed precision, the daily counts, with no context in a nearly unprecedented pandemic, are open to interpretation and can usually show a determined partisan exactly what they want to see. The seriousness of the coronavirus, and the measures needed to combat it, has, absurdly, become a partisan debate in the United States. But it’s not just politicians who twist the data. Throughout January and February, as the virus spread throughout China and then beyond, smart, data-informed columnists in major news outlets pointed to low fatality rates and assured us that the coronavirus was no more dangerous than the seasonal flu, that our growing concern was the result of cognitive biases, not informed decision-making.

Our failure to see this virus coming was not solely a failure of data processing. It was a failure of empathy. We saw China locking down a city larger than New York and building new hospitals in a week. We saw the fear in the eyes of Dr. Li Wenliang. Yet because it was far away, in a place with a different culture and an authoritarian political system, it was hard to imagine it could happen the same way here.

The pandemic should remind us of the importance of lived experience and effective storytelling. The East Asian countries that were most effective in containing the virus had a leg up not because they are richer or their medical systems were more advanced, but because they had already lived through one deadly coronavirus—the 2003 SARS outbreak—and didn’t need to be told of the importance of quarantines, lockdowns, testing, and mask-wearing. President George W. Bush reportedly began to take pandemic preparedness seriously, including funding stockpiles of medical equipment, not as a result of briefings from his science advisers but because he read author John Barry’s mass-market history of the 1918 flu pandemic while on vacation. Stories still matter, and hopefully, for the sake of future generations, this pandemic leaves more of a cultural impact than the one Bush read about did.

Faith in data—in the curve—is also going to be of only limited usefulness as we tentatively work on exiting our current state of isolation. Various experts and think tanks have now released plans detailing the benchmarks we will need to meet before reopening our economies, and, as Ezra Klein notes, they’re depressingly daunting. But these are recommendations based on projections in an unpredictable circumstance. This is the first time that the majority of the human race has ever been simultaneously locked down to this extent, and there’s no obviously correct way to do it. We don’t know exactly how the virus is going to progress in different areas going forward, and, yes, there are other factors to consider.

Trump was pilloried by critics for saying that “if it were up to the doctors,” shutdowns would last for years, and that he would rely on “a lot of facts and a lot of instinct” in deciding when to call for parts of the country to be reopened. These statements are alarming because of Trump’s track record and because of the criteria that he likely is using in place of “facts” and medical advice. But Trump’s not entirely wrong. Experts and advisers, be they doctors, generals, economists, or climate scientists, are there to give political leaders the information they need to make informed decisions. But they’re not the only factors to take into account.

Governments around the world are making very different decisions based on the available data. Several European countries are gradually lifting their lockdowns this week, despite the warnings of WHO and other public health experts. Sweden, which has a centrist government and no hostility to science, opted not to pursue a full-scale lockdown at all. Other countries at similar levels of risk are choosing to keep severe measures in place for much longer.

As Denmark’s prime minister, Mette Frederiksen, put it, “We cannot open a textbook—neither on healthcare nor economy—and find the right answer. The math is too simple. …The strategy we follow is a political choice.”

It’s become axiomatic that the vastly expanded testing will be a precondition for reopening the economy. Certainly we need more testing so those who get sick can better understand the care they need and so that we can respond more quickly to future flare-ups. But at a certain point, testing will only tell us the level of risk, not how to respond to it.

Data won’t make our decisions for us in the coming months, and thankfully, no matter what he thinks, Trump won’t be making most of them either. State and local governments, businesses, and individuals around the world are going to have to make hard decisions about the level of risk they’re comfortable with and what they’re willing to give up to avoid that risk—in economic stability, personal liberty, and privacy.

It’s probably time to spend a little less time watching the curve and a little more deciding on what kind of world we want to live in when we finally reach the bottom of it.