Math alumna’s disease modeling at national lab aiding public health initiatives to combat COVID-19

Mapping epidemics from Zika to COVID-19

Manore was modeling the spread of infectious diseases like Zika and West Nile virus at the beginning of 2020 when news about the novel coronavirus in China got the team’s attention. Manore’s modeling team started working on the COVID-19 outbreak in late January and their efforts intensified in mid-March when the infection started emerging globally and within the U.S. As their primary forecasting model has become stable, automated, requiring less day-to-day work, Manore and her team have moved on to answering other questions about a second wave of infections and different possible scenarios for reopening schools.

Manore’s work on forecasting models is primarily based on data, predicting what will happen, given current trends of positive cases and deaths. The national and global COVID-19 models by the team are made publicly available every Monday and Thursday to project case counts for every state in the U.S. as well as every country that has more than 100 cases.

As a mathematical epidemiologist, Manore helps ensure the model accurately captures the transmission dynamics of COVID-19 to forecast the national and global spread of the disease. Manore and her team utilize a probabilistic artificial intelligence computer model for COVID-19, developed by a Los Alamos statistician, that garnered national recognition when a version of it won the CDC’s flu forecasting challenge in 2019, beating 23 other teams.

“We needed to revise and adapt this model for COVID-19 forecasting because we have 20 years of flu data but only a few months of data for COVID-19,” said Manore. The Los Alamos model offers distinct advantages and stands out among other models because “it is truly data driven,” Manore observed. “Quite a few of the other models have a lot of assumptions about how people may behave and their possible decisions. Our model is just really driven by the data on cases and deaths. In particular, that gives more accurate short-term forecasts than some of the other models.”

In response to the reopening of businesses as well as many schools, Manore’s team has also moved forward simultaneously with other models and simulations that account for asymptomatic people, changes in people’s behavior and the consequences of relaxing restrictions. “We do think there is a lot of utility in having multiple models with multiple assumptions. Because we really don’t know what’s going to happen. So, that can give us an idea for the range of possible outcomes,” Manore said.

Manore got her start in disease modeling in the mathematics department at OSU, which she joined in 2006 after studying mathematics at Whitworth University and Eastern Washington University for her undergraduate and master’s degrees. At OSU, she was an NSF IGERT (Integrative Graduate Education and Research Traineeship Program) Fellow in Ecosystem Informatics and worked at the intersection of mathematics, biology, geosciences and computer science. Manore was advised by Vrushali Bokil, a professor of mathematics, and completed her dissertation on population dynamics and epidemiology.

“I got a really strong background in math at OSU, which not only helped me acquire mathematical skills, but also a way of thinking,” Manore said. “The IGERT program was truly interdisciplinary as it involved working collaboratively with academics from different disciplines and I found that extremely useful in my training as a mathematical epidemiologist. It prepared me to work on real problems in the world like I am doing now.”

Manore was an NSF postdoctoral fellow involved with emerging infectious diseases research at Tulane University before joining LANL.