Nicolet grad is an expert in coronavirus modeling
As public health officials try to control the spread of the coronavirus, one of the experts they turn to is Nicolet High School graduate Lauren Ancel Meyers, a leading epidemiologist who has been modeling the spread of infectious diseases for more than 20 years.
After graduating from Nicolet in 1991, Meyers trained as a mathematical biologist at Harvard and Stanford universities. She is now a professor of integrative biology and statistics at the University of Texas at Austin.
When news broke about the coronavirus in January, Meyers’ team was one of five research groups working to create a pandemic planning toolkit for the Centers for Disease Control and Prevention. The tool not only shows how a virus spreads, but also suggests what resources could be managed to control the virus.
They were about 80% finished when they began to receive reports about a highly infectious new strain of the coronavirus in China.
“Little did any of us know, the pandemic would happen in the middle of this project,” she said.
Meyers and her team quickly shifted their focus, adapting their models to
understand how fast the virus was spreading, what proportion of those with the virus died and what proportion were hospitalized.
Meyers’ team was able to acquire data from a Chinese social networking website, which it used to determine how many people traveled to, from and within China before the government imposed stay-at-home restrictions.
In developing a model, researchers looked at case reports to determine the time between an infected person developing symptoms and the onset of symptoms for the person they infected. This time period, called the serial interval, gives researchers a sense of the pace of transmission.
With this new coronavirus, Meyers found the serial interval is about four to five days, which means it was spreading at twice the speed of SARS, another contagious viral respiratory illness that spread worldwide in 2003. When China reported 400 cases of COVID-19, Meyers’ team determined the number of cases was likely closer to 12,000.
To make matters worse, the virus was spreading before infected people developed symptoms.
“This is the earliest sign that we were dealing with something much more harmful than SARS,” she said.
Meyers’ team tapped demographic and health data to create a map of the U.S., showing the risk the virus posed in 217 cities. The statistical models also predicted the outcome of preventive actions such as social distancing and school closures.
Meyers’ team shared its model with the CDC and the White House Coronavirus Task Force, a group established in late January to lead the country’s response to COVID-19.
The model was added to other researchers’ models to create an ensemble of forecasts that aim to predict how the virus will spread — and how it can be stopped.
In March, Meyers launched the University of Texas COVID-19 modeling consortium, which started with a small number of students and postdoctoral students. Now, a group of about 30 researchers from a variety of disciplines meet virtually every morning to collaborate in the development of better statistical models.
During the early phases of the virus, the consortium caught the attention of the national press because it created national forecasts that were publicly available. The consortium also created a county-by-county map of the U.S. that showed where the virus had likely spread, even if there were no
confirmed cases in those counties.
Making models
Similar to her work in China, Meyers’ team uses anonymous cellphone data to analyze how people move and how likely they are to spread the virus.
Predicting the future can be difficult, though, because the most significant factor — people’s behavior — is also an unknown. She said they can’t predict how many people will practice social distancing or wear masks.
“Epidemiologists cannot predict human behaviors,” she said. “We know how people are moving around, but we can’t predict how people’s behavior will change.”
Adding to the complexity, Meyers said the testing data is not stable enough at this point, so it is more reliable to look at hospital and death data. While that data may be more reliable, it presents an outdated representation of the virus’ spread.
Someone who is admitted to the hospital for coronavirus was typically exposed to the virus 10 days earlier. Patients typically die three weeks or more from the time they were exposed.
As states start to relax their stay-athome restrictions, Meyers said, it may take three weeks or more to know the effect of changed behaviors.
“The combination of lag in death and hospital data, uncertainty in testing data and the exponential nature of epidemic curves means if transmission starts picking up, we may not know it for several more weeks,” she said.
Although testing is more available than it was during the early stages of the pandemic, Meyers said there also needs to be rapid contract tracing that determines who an infected person might have passed the virus to in the days leading up to their first symptoms.
Early adopter
Meyers was studying pandemics long before the coronavirus caught the world’s attention.
When she started as an epidemiologist, most disease modeling was done by applied mathematicians. Epidemic modeling was still in its infancy, she said, and it was difficult to get a National Institutes of Health grant for that work.
That changed about 15 years ago, when the NIH launched a program to fund epidemic modeling research. What started as about 10 epidemic modeling groups has grown to dozens since the NIH program was founded.
The interest in epidemic modeling has increased greatly since the coronavirus pandemic, she said.
“The amount of modeling being done is at least 10 times what was happening four months ago,” she said.
Meyers is considered somewhat of a pioneer in her field.
She was named one of the top 100 global innovators younger than 35 by the MIT Technology Review in 2004. She received the Joseph Lieberman Award for Significant Contributions to Science in 2017.
Finding her path
Meyers’ focus on education was inspired not only by her parents, both of whom were professors, but her mother’s parents, who survived the concentration camps in Nazi Germany.
Meyers’ mother, Esther Ancel, a retired professor at the University of Wisconsin-Milwaukee’s Lubar School of Business, said her parents, Pincus and Bluma Weinstock, moved to the U.S. from Germany in 1952.
The Weinstocks instilled in their children and grandchildren the importance of education, which they saw as the key to success in American society.
When Lauren was in eighth grade at Bayside Middle School, her grandfather Pincus Weinstock would drive her to Nicolet High School for algebra class.
During high school, she participated in elite summer programs, such as the Arnold Ross mathematics program at Ohio State University and the Research Science Institute, sponsored by the Center for Excellence in Education.
“We realized at an early age she had a gift for abstract thinking and mathematical thinking,” said her father, Fredric Ancel, a retired professor of mathematics at UW-Milwaukee.
In her senior year of high school, USA Today selected her as one of 20 “Academic All-Stars.”
In her junior year at Nicolet, her father gave her a math problem that arose in one of his student’s dissertation papers. She was able to solve the problem, and her writing on the topic was published in an academic journal.
After graduating from Nicolet in 1991,
Meyers went to Harvard University, where she majored in mathematics and philosophy.
The summer after her sophomore year in college, she did cryptology for the National Security Agency, using high-level math to ensure the country’s encoding systems were secure, while also attempting to break the encoding systems of other countries.
Inspired by the opportunity to apply math to real-world problems, she signed up for classes in physics, sociology and biology, looking for “ways that math could unlock the mysteries of life,” she said.
It was around this time that her interest in biology inspired her to spend a summer as a river rafting guide on the West Coast, where she met her nowhusband, Steven, another Nicolet grad who was biking down the Pacific coast at the time.
“Although we grew up across the ravine from each other in Bayside, we met for the first time in the woods of Oregon when we were in our early 20s,” she said.
During graduate school at Stanford University, Meyers studied evolutionary biology, which led her to study how populations change over time, how to build mathematical models to understand those changes in population and the evolutionary dynamics of viruses.
After graduating with a doctorate in biological sciences, she conducted her postdoctoral research at Emory University, just down the street from the CDC.
At the CDC’s request, Meyers started researching various statistical models that could be used to find pressure points of outbreaks, in order to more effectively stop walking pneumonia in nursing homes.
“This was the first time I got deep into the weeds of the math of stopping an outbreak,” she said.
When she started teaching at the University of Texas at Austin, the British Columbia Centre for Disease Control asked for help in modeling the SARS virus. She has since developed models for H1N1, Ebola and the Zika virus.
Researchers like Meyers have spent years preparing for an influenza outbreak, so the new and unknown strain of the coronavirus has left epidemiologists and medical professionals in uncharted territory.
Meyers said it has been a stressful time for her and her team, with many long hours for the past several months.
“Even though I have studied pandemics for the last 20 years, this is much more of a personal and global threat than anything I’ve ever been through,” she said. “I feel much more responsibility to bring scientific data to decision makers so they have as much knowledge as possible when they are making decisions to protect us.”