When Lauren Ancel Meyers was an undergraduate at Harvard University, she twice briefly left school to work for the National Security Agency. Intelligence deemed critical to the country’s safety lay hidden by cryptic ciphers, so her cohort of mathematical code breakers used intricate equations to try to unlock the information. “You learn math in school, and then you learn more complicated math in college,” she says. “But that was my first time using very advanced math to solve really practical problems in the real world.”
That her favorite subject held the key to not only solving abstract equations but also addressing imminent threats to the United States was a thrilling revelation for the young math whiz. Still, Meyers—who describes herself as an “academic brat,” the daughter of professors who moved nine times before she was in high school—wasn’t passionate about cryptography.
Instead, from a young age she had been captivated by pathogens. She delighted in reading books and watching movies about plagues and deadly diseases. “They horrified me; they fascinated me,” Meyers remembers. “Over the course of college and grad school, I realized that there actually was an emerging field of mathematical epidemiology, where people were using math to build models that could help us understand these diseases and fight them.”
That’s what Meyers has been doing since she moved to Austin, in 2003, to work as a professor in the University of Texas’s department of integrative biology. After completing her PhD in evolutionary biology at Stanford University in 2000, she had worked as a postdoctoral fellow at Emory University, in Atlanta, where she built a mathematical model to help the U.S. Centers for Disease Control and Prevention determine its strategies for fighting an outbreak of walking pneumonia.
Shortly after her arrival in Texas, she got a call from a representative of the Ministry of Health in British Columbia, who had heard about her work for the CDC. The Canadian province had detected the spread of a deadly disease—which turned out to be severe acute respiratory syndrome, known as SARS—in Vancouver, its most populous city. Officials there wanted Meyers’s help in trying to contain it. It was the first time she’d worked with local leaders in such a way, and it charted a course for the rest of her career.
In the following decades, she and her team of postdocs and graduate students modeled the likely spreading of disease outbreaks, including of the H1N1 influenza, known as swine flu, and Ebola, but it was her work during the COVID-19 pandemic that finally drew the professor into the limelight, advising leaders in Austin and Travis County on pandemic policies and bringing together dozens of experts from around the world to contribute to her UT COVID-19 Modeling Consortium.
Now the CDC has tapped Meyers to help lead a new nationwide outbreak-response network. It’s just one of the ways she’s already planning for how we might reduce the impact of the next pandemic.
Before Wuhan and other cities in China’s Hubei Province had been locked down, and well before nearly anyone understood the virulence of the novel coronavirus causing the disease that became known as COVID-19, Meyers’s lab had shared results of an analysis that correctly estimated how quickly the pathogen would spread. She had learned about this new threat “maybe half a day before it broke in the news,” in January 2020, and had immediately gone to work on the problem.
A second study by Meyers’s team soon showed that, unlike with the similar SARS virus, some infected by COVID-19 were spreading the disease silently, even before they had demonstrated symptoms. When she showed these findings with the CDC, she says, the agency’s first response was, “Oh, no, you must have made a mistake. Check your math.” But her math was solid, as the world soon found out.
In March 2020, organizers of Austin’s annual South by Southwest conference called Meyers seeking guidance. She told them that it was likely the event could fuel transmission of the virus. Ultimately, at the City of Austin’s direction, the conference was cancelled, among the first in a long line of events called off as the pandemic officially became a national health emergency. Before long, Austin’s city leaders were regularly consulting Meyers, the Texas Department of State Health Services asked her team to forecast statewide hospital bed shortages, and even the White House Coronavirus Task Force sought her analyses of COVID data—though there was no evidence that her work influenced President Trump’s decision-making, she says.
One of Meyers’s proudest accomplishments during the pandemic’s early days was helping the City of Austin design a staged alert system, a color-coded tool that helped guide local policymaking and inform the public about changing infection-risk levels. Driven by models built around daily COVID hospital admission data, the system was designed to ensure that intensive care units never exceeded capacity. “The goal was: we’re going to sustain some burden, we’re going to sustain some people getting infected, some people getting admitted to the hospital, some people even dying, because we can’t shut down forever, right?” Meyers says. “But the one thing we absolutely are not going to tolerate is our hospital system being overwhelmed.”
Experiencing less than half of the state’s mortality rate, Travis County had far fewer COVID deaths per capita than any of the other most populous counties in Texas. In January, former Austin mayor Steve Adler presented Meyers with a key to the city in recognition of her contribution to the region’s pandemic response. (She is quick to note that Austin also benefited from a relatively young and healthy population, both key factors in a low mortality rate.)
Yet even Austin couldn’t avoid one of the most troubling pandemic-related statistics—how COVID-19 hospitalizations and deaths occurred disproportionately among historically marginalized groups. Early in the pandemic, after Governor Abbott designated construction as “essential” labor, Meyers’s team, at the request of City of Austin officials, estimated that construction workers, many of them recent immigrants living on low incomes, were between four and eight times more likely to be hospitalized for COVID than residents who weren’t required at a jobsite in person. When they analyzed hospitalization data several months later, they were disheartened to see that their projections were spot-on: during the first six months of the pandemic, construction workers were five times more likely to wind up in the hospital with COVID. “There’s just such tragic disparities in the impact of this pandemic,” Meyers says. “We need to be improving the situation for vulnerable groups in so many different ways. We need to be tracking those risks and making sure resources are being channeled to mitigate those risks.”
Meyers acknowledges that, in retrospect, some lockdown measures were too draconian. But, particularly early in a pandemic, when there are so many unknown variables, she says it remains smart to “press pause for a few weeks to very rapidly do effective analysis, evaluate different strategies, and come up with a clear game plan.”
Though she was fortunate to get through the worst of the pandemic with only a mild case of COVID herself, and without losing a loved one, she nevertheless describes that period as “the most horrible two years of my life.” It wasn’t just the exhausting work, which required her to keep her eyes fixed on death on an unprecedented scale. She also grew frustrated seeing leaders make decisions that she knew—and the data showed—would needlessly cost lives.
Trump’s “Opening Up America Again” plan, for instance, eschewed a safer, strategic reopening in favor of simply returning to the status quo as soon as possible. Meyers understood and supported the impulse to keep schools and businesses open, but “there just wasn’t that really data-driven action plan, and we could have had one,” she says. “It was so painful and sad for me to see that happening.”
“It’s commonly said in our academic community that everybody wishes they had a Lauren Meyers in their city,” says Nicholas Reich, a professor of biostatistics at the University of Massachusetts Amherst and director of UMass’s COVID-19 Forecast Hub. It’s rare, he adds, that an expert “can both engage with the modeling at the level that she can and understand and prioritize the needs of governmental stakeholders.”
Reich is Meyers’s partner in the new CDC initiative. Under the auspices of the CDC’s recently established Center for Forecasting and Outbreak Analytics, their teams at UT and UMass will coordinate to bring successful COVID-era modeling practices to bear in planning for further infectious disease threats. “We made more progress, and were more innovative, in the first ten months of the pandemic than in the decade leading up to it,” Meyers says.
In September, the CDC selected thirteen groups to comprise the Outbreak Analytics and Disease Modeling Network and receive grants totaling $262.5 million over five years. Meyers’s and Reich’s teams will together have $27.5 million to work with more than two dozen partners, including several regional public health departments in Texas, to advance the analytic tools and forecasting models that proved useful during the pandemic and train public health departments to use those resources when the next threat arises.
The outbreak analytics network will also feed information into a centralized data hub at the CDC, which aims to create something akin to a weather forecast that leaders can use to guide policy and members of the public can use to gauge risk and adjust their behavior accordingly. “By the end of this initial five-year project period, I would expect that if we were to have a pandemic, the [CDC’s website] would become a really valuable, trusted one-stop shop for real-time comprehensive information,” Meyers says.
Just as a weather forecast might lead someone to pack an umbrella, a public health forecast might convince someone to wear a mask at the grocery store. “No one model is that great yet,” Meyers says. “Each of these different models and different groups bring different expertise, different data, different approaches to the task, and we can get a much more accurate forecast if instead of looking at any one of these models, we sort of look across them.”
Even before the CDC grant was awarded this fall, Meyers had launched her own attempt at long-term pandemic planning. In navigating the chaotic and unrelenting maelstrom of COVID, she was struck by how blindsided we were when experts knew beforehand that some infectious disease threat was likely to arise. “We’ve got to figure out how to help the world to overcome its failure of imagination in planning for this pandemic,” she decided.
At the onset of the pandemic, Meyers had multiple conversations with other public health experts who expressed hope that this would be their Manhattan Project moment. In the face of a grave tragedy, the country’s brightest minds would come together to defeat a brutal and deadly force. “It didn’t happen,” she says. Instead of a powerful, coordinated response, the U.S. saw scattered success stories and overwhelming loss of life.
We can’t expect to do a perfect job planning for a pathogen that doesn’t yet exist, she admits, but she’s devoted her career to ensuring we’re as prepared as we possibly can be. She believes that data modeling and analytics remain central to that goal. “Those are some of our most powerful weapons early in a pandemic, when we don’t have vaccines, and we don’t have drugs, and maybe we don’t even have tests yet,” she says. “Those tools can really help us understand and manage the situation.”
This conviction led her last year to cofound, with two Texas-based investors, a nonprofit based in Austin called the Center for Advanced Preparedness and Threat Response Simulation, or CAPTRS. Its focus is using best practices from cognitive science to develop simulations that national and local public health agencies can use to “pressure test their playbooks to build better data collection systems and better analytic systems,” Meyers says.
With funding from private donors and public grants, CAPTRS recently hired its first full-time employee, a “chief scientist for gaming” who had previously spent twelve years developing war games and other simulations for NATO. In something of a return to her days at the NSA, Meyers and her team are treating public health as a matter of national security.
