Lauren Meyers and Sam Scarpino at the University of Texas circa 2014.

As a child, Lauren Meyers devoured books about deadly plagues. She had nightmares about the big one that was going to wipe out Earth as we know it.

Earlier this year on Jan. 23, Meyers, a Santa Fe Institute external faculty member and a professor of integrative biology and statistics at the University of Texas, thought that day might have arrived.

She was working with researchers from the U.S. Centers for Disease Control and Prevention to model the spread of a novel coronavirus moving through the Chinese city of Wuhan. One of her team’s initial studies looked at the timing and location of the first cases of COVID-19 reported outside of China to determine how fast the virus was spreading. The data told a much different story than was being reported on the news. 

“There was this moment when we saw that there were so many more cases in Wuhan than anyone realized, and there were likely cases all over the world,” Meyers said. “We went from thinking ‘what is this little virus?’ to understanding that this had the making of a global pandemic – something we had never seen before.”

Since then, Meyers, a Harvard and Stanford-trained mathematical biologist, has been working with a team of experts around the clock to build tools for the CDC and other global health agencies to track and mitigate the emerging viral threat.

Her collaborator and former Omidyar Fellow at SFI, Sam Scarpino, who is now a Northeastern University professor of network sciences, is also playing a pivotal role in the global response to COVID-19. His main focus is designing interventions to predict and prevent superspreading events, such as the Mt. Vernon Washington choir practice on March 10 where one person infected 53 other choir members.

Meyers and Scarpino’s analyses have helped policy-makers on the local, national and international levels make critical decisions about purchasing medical equipment such as ventilators, implementing social distancing guidelines and determining when it is safe for people to return to their normal lives.

Much of their work relies on quantitative methods of network epidemiology that Meyers helped pioneer at SFI in the early 2000s. At that time, Meyers, an SFI postdoc, and then-SFI Professor Mark Newman departed from traditional epidemiological theory to develop models to help predict and control the spread of walking pneumonia. Unlike traditional epidemiological theory, which assumes every individual in a population has an equal chance of spreading a disease to everyone else, Meyers and Newman’s network approach incorporated the fact that some people are more likely to transmit or get a virus than others.

“Network modeling allows us to explicitly capture the complex and changing patterns of interaction between people that fuel the spread of a disease,” Meyers said. “For example, it takes into account that nurses and doctors have many potential disease-causing contacts on a daily basis while somebody who is retired at home will have fewer opportunities to spread the disease.”

Since then, Meyers has gone on to use network science techniques developed at SFI to track outbreaks, epidemics and pandemics like Ebola, SARS, and influenza. 

She and Scarpino worked closely with the state of Texas in 2009 in the aftermath of the H1NI influenza epidemic to use the lessons learned to develop a number of models that gave policy-makers the ability to determine how best to respond to an emerging pandemic.

Fast forward to 2020 and the methods they developed are now being used on a global scale to help direct the response to COVID-19.

“The network modeling techniques that were developed at SFI enable us to look at how human contact patterns shape the way diseases spread around the globe,” Scarpino said. “The way we move around cities, where we got to school, where we go to work – all of these interactions provide the fuel for epidemics.”