Here’s How We Prepare for the Next Pandemic

If we keep developing the tech that has been supercharged for COVID-19, it never has to be this bad again

6 min read
Illustration of COVID-19 moving around the globe.
Illustration: StoryTK

When the Spanish flu pandemic swept across the globe in 1918, it ravaged a population with essentially no technological countermeasures. There were no diagnostic tests, no mechanical ventilators, and no antiviral or widely available anti-inflammatory medications other than aspirin. The first inactivated-virus vaccines would not become available until 1936. An estimated 50 million people died.

Today, a best-case scenario predicts 1.3 million fatalities from COVID-19 in 2020, according to projections by Imperial College London, and rapidly declining numbers after that. That in a world with 7.8 billion people—more than four times as many as in 1918. Many factors have lessened mortality this time, including better implementation of social-distancing measures. But technology is also a primary bulwark.

Since January of this year, roughly US $50 billion has been spent in the United States alone to ramp up testing, diagnosis, modeling, treatment, vaccine creation, and other tech-based responses, according to the Committee for a Responsible Federal Budget. The massive efforts have energized medical, technical, and scientific establishments in a way that hardly anything else has in the past half century. And they will leave a legacy of protection that will far outlast COVID-19.

In the current crisis, though, it hasn’t been technology that separated the winners and losers. Taking stock of the world’s responses so far, two elements set apart the nations that have successfully battled the coronavirus: foresight and a painstakingly systematic approach. Countries in East Asia that grappled with a dangerous outbreak of the SARS virus in the early 2000s knew the ravages of an unchecked virulent pathogen, and acted quickly to mobilize teams and launch containment plans. Then, having contained the first wave, some governments minimized further outbreaks by carefully tracing every subsequent cluster of infections and working hard to isolate them. Tens of thousands of people, maybe hundreds of thousands, are alive in Asia now because of those measures.

In other countries, most notably the United States, officials initially downplayed the impending disaster, losing precious time. The U.S. government did not act quickly to muster supplies, nor did it promulgate a coherent plan of action. Instead states, municipalities, and hospitals found themselves skirmishing and scrounging for functional tests, for personal protective equipment, and for guidance on when and how to go into lockdown.

The best that can be said about this dismal episode is that it was a hard lesson about how tragic the consequences of incompetence can be. We can only hope that the lesson was learned well, because there will be another pandemic. There will always be another pandemic. There will always be pathogens that mutate ever so slightly, making them infectious to human hosts or rendering existing drug treatments ineffective. Acknowledging that fact is the first step in getting ready—and saving lives.

Left, an IMP. Center, IMP log of the first ARPANET message. Right, An interface to link to UCLA\u2019s IMP.Trouble Brewing: When forests are cut down to make way for agriculture and livestock is crowded together on farms, animal and human populations come into contact. Then pathogens that typically sicken animals have opportunities to mutate and take hold in human hosts. This “zoonotic transfer” is the cause of most outbreaks of new infectious diseases.Photos, top: Leo Correa/AP; bottom: Damien Meyer/AFP/Getty Images

The cutting-edge technologies our societies have developed and deployed at lightning speed are not only helping to stem the horrendous waves of death. Some of these technologies will endure and—like a primed immune system—put us on a path toward an even more effective response to the next pandemic.

Consider modeling. In the early months of the crisis, the world became obsessed with the models that forecast the future spread of the disease. Officials relied on such models to make decisions that would have mortal consequences for people and multibillion­-dollar ones for economies. Knowing how much was riding on the curves they produced, the modelers who create projections of case numbers and fatalities pulled out all the stops. As Matt Hutson recounts in “The Mess Behind the Models,” they adapted their techniques on the fly, getting better at simulating both a virus that nobody yet understood and the maddening vagaries of human behavior.

In the development of both vaccines and antiviral drugs, researchers have committed to timelines that would have seemed like fantasies a year ago. In “AI Takes Its Best Shot,” Emily Waltz describes how artificial intelligence is reshaping vaccine makers’ efforts to find the viral fragments that trigger a protective immune response. The speed record for vaccine development and approval is four years, she writes, and that honor is held by the mumps vaccine; if a coronavirus vaccine is approved for the general public before the end of this year, it will blow that record away.

Antiviral researchers have it even tougher in some ways. As Megan ­Scudellari writes, hepatitis C was discovered in 1989—and yet the first antiviral effective against it didn’t become available until 26 years later, in 2015. “Automating Antivirals” describes the high-tech methods researchers are creating that could cut the current ­drug-development timeline from five or more years to six months. That, too, will mean countless lives saved: Even with a good vaccine, some people inevitably become sick. For some of them, effective ­antivirals will be the difference between life and death.

Beyond Big Pharma, engineers are throwing their energies into a host of new technologies that could make a difference in the war we’re waging now and in those to come. For example, this pandemic is the first to be fought with robots alongside humans on the front lines. In hospitals, robots are checking on patients and delivering medical supplies; elsewhere, they’re carting groceries and other goods to people in places where a trip to the store can be fraught with risk. They’re even swabbing patients for COVID-19 tests, as Erico Guizzo and Randi Klett reveal in a photo essay of robots that became essential workers.

“This pandemic is the first to be fought with robots alongside humans on the front lines.”

Among the most successful of the COVID-fighting robots are those buzzing around hospital rooms and blasting floors, walls, and even the air with ­ultraviolet-C radiation. Transportation officials are also starting to deploy UV-C systems to sanitize the interiors of passenger aircraft and subway cars, and medical facilities are using them to sterilize personal protective equipment. The favored wavelength is around 254 nanometers, which destroys the virus by shredding its RNA. The problem is, such UV-C light can also damage human tissues and DNA. So, as Mark Anderson reports in “The Ultraviolet Offense,” researchers are readying a new generation of so-called far-UV sterilizers that use light at 222 nm, which is supposedly less harmful to human beings.

When compared with successful responses in Korea, Singapore, and other Asian countries, two notable failures in the United States become clear: testing and contact tracing. For too long, testing was too scarce and too inaccurate in the United States. That was especially true early on, when it was most needed. And getting results sometimes took two weeks—a devastating delay, as the ­SARS-CoV-2 virus is notorious for being spread by people who don’t even know they’re sick and infectious. Researchers quickly realized that what was really needed was something “like a pregnancy test,” as one told Wudan Yan: “Spit on a stick or into a collection tube and have a clear result 5 minutes later.” Soon, we’ll have such a test.

Digital contact tracing, too, could be an enormously powerful weapon, as Jeremy Hsu reports in “The Dilemma of Contact-Tracing Apps.” But it’s a tricky one to deploy. During the pandemic, many municipalities have used some form of tracing. But much of it was low-key and low-tech—sometimes little more than a harried worker contacting people on a list. Automated contact tracing, using cloud-based smartphone apps that track people’s movements, proved capable of rapidly suppressing the contagion in places like China and South Korea. But most Western countries balked at that level of intrusiveness. Technical solutions that trade off some surveillance stringency for privacy have been developed and tested. But they couldn’t solve the most fundamental problem: a pervasive lack of trust in government among Americans and Europeans.

It has been 102 years since the ­Spanish flu taught us just how bad a global pandemic can be. But almost nobody expects that long of an interval until the next big one. Nearly all major infectious outbreaks today are caused by “zoonotic transfer,” when a pathogen jumps from an animal to human beings. And a variety of unrelated factors, including the loss of natural habitats due to deforestation and the rapid growth of livestock farming to feed industrializing economies, is stressing animal populations and putting them into more frequent contact with people.

We’re unlikely to halt or even measurably slow such global trends. What we can do is make sure we have suitable technology, good governance, and informed communities. That’s how we’ll mount a tougher response to the next pandemic.

This article appears in the October 2020 print issue as “Prepping for the Next Big One.”

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The humanitarian program leverages technology for sustainable development

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Photo of a man in a blue "IEEE SIGHT" jacket in front of a flowering tree.

Amarnath Raja, an IEEE senior member, founded the IEEE Special Interest Group on Humanitarian Technology in 2011.

Jaya Krishnan

Amarnath Raja

Founder of IEEE Special Interest Group on Humanitarian Technology

Senior member, 65; died 5 September

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This optical tomography device that can be used to recognize and track breast cancer, without the negative effects of previous imaging technology. It uses near-infrared light to shine into breast tissue and measure light attenuation that is caused by the propagation through the affected tissue.

A.H. Hielscher, Clinical Biophotonics Laboratory

This is a sponsored article brought to you by NYU’s Tandon School of Engineering.

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