America’s coronavirus response failed because we didn’t understand the complexity of the problem.

ZEYNEP TUFEKCI
MARCH 24, 2020

Many will be tempted to see the tragic coronavirus pandemic through a solely partisan lens: The Trump administration spectacularly failed in its response, by cutting funding from essential health services and research before the crisis, and later by denying its existence and its severity. Those are both true, but they don’t fully explain the current global crisis that has engulfed countries of varying political persuasions.

As it turns out, the reality-based, science-friendly communities and information sources many of us depend on also largely failed. We had time to prepare for this pandemic at the state, local, and household level, even if the government was terribly lagging, but we squandered it because of widespread asystemic thinking: the inability to think about complex systems and their dynamics. We faltered because of our failure to consider risk in its full context, especially when dealing with coupled risk—when multiple things can go wrong together. We were hampered by our inability to think about second- and third-order effects and by our susceptibility to scientism—the false comfort of assuming that numbers and percentages give us a solid empirical basis. We failed to understand that complex systems defy simplistic reductionism.

Widespread asystemic thinking may have cost America the entire month of February, and much of what we’d normally consider credible media were part of that failure.

On January 29, about a week after China’s government shifted from a deny-and-censor strategy to massive action and communication, Chinese scientists published a significant paper in The New England Journal of Medicine. The paper estimated the R0 (the basic reproduction number of an infectious disease) from the first known case of coronavirus in early December through January 4 to be little more than 2. That means that, left somewhat unchecked, each infected person infected two more people. Crucially, the paper pointed out evidence of mild and even asymptomatic cases, unlike SARS, which almost always came with a high fever. It also confirmed the reports that the disease was most dangerous for the elderly or people with underlying conditions. The paper came out just after China made the unprecedented move to shut down all of Wuhan, a metropolis of 10 million people, and also Hubei, a province of 50 million people.

For people stuck in asystemic thinking, all this may well have seemed like a small, faraway threat. If one merely looked at the R0, the virus wasn’t outrageously contagious. The number was similar to seasonal flu, but nothing explosive like measles, which has an R0 of 12 to 18—one ill person can infect another 12 to 18 people. For an asystemic thinker, it probably didn’t look that deadly, either. The mortal threat was disproportionately to the elderly, who already succumb to colds and influenza at much higher rates than younger, healthier people. The case-fatality rate (CFR), or the percent of infected people who die, for younger people seemed fairly low, perhaps comparable to seasonal influenza, which kills about 0.1 percent of its victims, exacting a toll in the tens of thousands in the United States alone. On January 29, the known global death total for COVID-19, the disease caused by the coronavirus, was still under 200, less than a weekend’s worth of traffic accidents in the United States, let alone the flu. And to an asystemic thinker, the threat seemed remote, unfolding as it was in Wuhan, a place that many people outside China may not have heard of.

Thus from the end of January through most of February, a soothing message got widespread traction, not just with Donald Trump and his audience, but among traditional media in the United States, which exhorted us to worry about the flu instead, and warned us against overreaction. It seemed sensible, grown-up, and responsible. “Get a Grippe, America,” read the headline of one piece that made fun of those worried about this pandemic with a play on grippe (French for “flu,” how clever.) The title said that the flu was a much bigger threat “for now.” There was a New York Times op-ed with a nice alliteration, telling us to “Beware of the Pandemic Panic,” again comparing the coronavirus to the flu, and warning us that overreaction would be worse than the pandemic. (The author wrote a follow-up admitting he was wrong, but claiming that this was a “black swan” event, something unpredictable, rather than what it was: predictable and predicted, a gray rhino). Another New York Times op-ed, on February 5, provocatively titled “Who Says It’s Not Safe to Travel to China?” and written by a tourism-industry reporter, claimed travel bans were unjust and ineffective, and were racist especially because they weren’t issued for flu—and, astonishingly, went on to reassure readers that most coronavirus victims recovered.

Other media followed suit, in framing the initial response as an overreaction. BuzzFeed News ran a piece telling people to worry about the flu instead of the coronavirus. (It has since wisely changed its headline, though that doesn’t erase the damage.) Recode published an article titled “The Tech Industry Is Terrified of the Virus,” emphasizing that “public officials in the area say the virus is contained”—and even mentioning the eccentric doomsday scenarios of tech billionaires.

These pieces were neither exceptional nor exceptionally bad. In fact, they were routine examples of the common sentiment among mainstream media. There was coverage of the coronavirus, but we did not have what we desperately needed: the clear and loud warning that a tsunami was about to land on our shores, and that we needed to start getting ready, immediately. The appropriate message for a tsunami headed our way isn’t that it’s not a threat “for now” or that we should worry about falling in the tub instead. A massive reaction would not have been an overreaction at all; it would have been appropriate. If nothing else, that China’s efficient top-down regime, which highly values its own survival, was willing to take such drastic steps was a sign that the coronavirus was a profound threat.

This complacency went on until about early March, when the severity of the crisis finally sunk in, seemingly only after Italy started suffering the same kind of crisis that had hit Wuhan months earlier.

Many pieces with these flu comparisons usually included discussions of R0 and case-fatality rate, but numbers alone do not make science or sensible risk calculation in complex systems. We needed instead to think about these numbers and measurements in the context of the global system, including how epidemics and the health-care infrastructure work, and consider the trade-offs between resilience, efficiency, and redundancy within the system, and how the second- and third-order impacts can reverberate.

Let’s then go back to what we knew as of January 29 and think about it from a complex-systems perspective. A novel coronavirus (the same family as SARS and MERS) had been observed in Wuhan in early December 2019. The NEJM paper informed us that unlike SARS, this coronavirus included hard-to-detect cases. With SARS, the infectious phase reliably came with a high fever, allowing airports and health authorities to use temperature checks to quickly find and isolate affected individuals. And despite being easily detected, SARS still threatened to become a terrible pandemic in the much-less-interconnected world of 2003. In 2020, Wuhan may seem remote to Western audiences, but it’s a bustling, giant city with more than 500 direct international flights a day, situated in a country that has made great strides in domestic transportation since 2003, with more flights and high-speed train lines. Plus, in 2020, China is practically the manufacturing base of the world economy—something that had started in 2003, of course, but on a smaller scale.

Without using systemic thinking, in isolation, the case-fatality rate may not have seemed that alarming, especially because the virus seemed to disproportionately affect the elderly. But viewed through a systemic lens, even a small fatality rate foretold a disaster. It is true that the flu kills tens of thousands annually, but the choice here wasn’t between worrying about this coronavirus or seasonal influenza. It was about assessing what adding a COVID-19 pandemic on top of a flu season would mean—and how it would overwhelm health-care systems.

In complex systems, one can think about linear interactions and complex or nonlinear interactions. In linear interactions, we can add numbers to guess at combined impact. If the flu kills about 40,000 people annually in the U.S., and car accidents kill another 40,000 people annually, their combined impact is pretty much just that. They are both predictable events for which we have built infrastructure and expectations; our system anticipates both. But adding one more flu-like illness (as COVID-19 was presented) isn’t a linear event. Tipping points, phase transitions (water boiling or freezing), and cascades and avalanches (when a few small changes end up triggering massive shifts) are all examples of nonlinear dynamics in which the event doesn’t follow simple addition in its impacts—that’s why this coronavirus was never just about its R0 or CFR.

In many complex systems, efficiency, redundancy, and resiliency pull in different directions: More efficient systems, which are cheaper, eliminate redundancies, which provide resilience but cost more. For example, commercial airplanes always have two or more engines and have a co-pilot, even though one pilot and one engine is sufficient to fly the plane safely. The redundancy adds to expenses, but increases safety and resiliency in case something happens to one pilot or engine. In fact, commercial aviation is so safe because redundancy is mandated by regulation and built into every level, but our commercial-flying experience is so miserable because airlines have made it as efficient as possible to save money. (If one plane doesn’t arrive on time, there is no backup waiting to fly instead, for example.)

Health systems are prone to nonlinear dynamics exactly because hospitals are resource-limited entities that necessarily strive for efficiency. Hospitals in wealthy nations have some slack built in for surge capacity, but not that much. As a result, they can treat only so many people at once, and they have particular bottlenecks for their most expensive parts, such as ventilators and ICUs. The flu season may be tragic for its victims; however, an additional, unexpected viral illness in the same season isn’t merely twice as tragic as the flu, even if it has a similar R0 or CFR: It is potentially catastrophic.

Worse, COVID-19 wasn’t even just another flu-like illness. By January 29, it was clear that COVID-19 caused severe primary pneumonia in its victims, unlike the flu, which tends to leave patients susceptible to opportunistic, secondary pneumonia. That’s like the difference between a disease that drops you in the dangerous part of town late at night and one that does the mugging itself. COVID-19’s characteristics made it clear that the patients would need a lot of intensive, expensive resources, as severe pneumonia patients do: ICU beds, ventilators, negative-pressure rooms, critical-care nurses.

This is why the case-fatality rate for COVID-19 was never a sufficient indicator of its threat. If emergency rooms and ICUs are overloaded from COVID-19, we will see more deaths from everything else: from traffic accidents, heart attacks, infections, seasonal influenza, falls and traumas—basically anything that requires an emergency-room response to survive. If COVID-19 causes a shortage of masks for emergency-room workers, hospitals will stop everything that looks “elective” or nonurgent to fight that fire, but that means people will then suffer and die from things that those surgeries were intended to treat or improve. An angioplasty may not be urgent that week, but it is still a lifesaving intervention without which more people will die. This is true for even seemingly optional health interventions: If people can’t get knee-replacement surgeries, for example, they will be less active, which will increase their health risks.

Nonlinear dynamics and complex-system failures can also come about because of tight coupling between the components. Tight coupling means that every part of the system moves together, which in turn means that even small things can cause a crisis—for want of a nail. For example, COVID-19 testing requires skinny swabs that can reach the nasopharynx. Forget the tests themselves; we have a shortage of swabs because we didn’t ramp up their production when we had time, and now there aren’t enough. Reagents required for testing also are in short supply. Addressing these shortages may have other nonlinear effects: Medical-equipment producers are being told, understandably, to drop everything and produce more swabs. That means something else will not be produced, which we might notice as a crisis in a year.

The phrase flatten the curve is an example of systems thinking. It calls for isolation and distancing not because one is necessarily at great risk from COVID-19, but because we need to not overwhelm hospitals with infections in the aggregate. Also, R0 is not a fixed number: If we isolate ourselves, infectiousness decreases. If we keep traveling and congregating, it increases. Flattening the curve is a system’s response to try to avoid a cascading failure, by decreasing R0 as well as the case-fatality rate by understanding how systems work.

None of this erases the administration’s failures, which are grave, but the painful truth is that we could have tried to do a lot at the local level that would have helped. Not everything had to wait for the government. Hong Kong, too, had a largely unresponsive government, but great popular pressure and people’s own actions—immediate adoption of social distancing in January, universal mask wearing, calling for closures and cancellations even when the government dragged its feet—have meant that the city had a very low rate of infection until late March, despite its nearness to China and its status as one of the most densely populated places in the world. Hong Kong is now facing a second wave, but even that uptick, which has caused the anti-government and pro-democracy Telegram channels I follow in Hong Kong to burst with exhortations to the people not to let their guard down, still brings it to only about 360 cases total. That’s a minuscule number compared with, say, the more than 15,000 cases just in New York.

In the United States and Europe, the die is mostly cast for the immediate future. But understanding our failures leading up to this moment isn’t an abstract exercise. Maybe we will muddle through the next few months, at great cost. But we will still need all the systemic thinking we can muster to anticipate the second- and third-order effects that will follow this crisis. And if we hope to blunt the impact of others like it, let’s not forget, again, that all of our lives are, together, embedded in highly complex systems.

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ZEYNEP TUFEKCI is an associate professor at the University of North Carolina, and a faculty associate at the Harvard Berkman Klein Center for Internet and Society. She studies the interaction between digital technology, artificial intelligence, and society.