Professor Andrea Bertozzi, an IDRE Executive Board member, is lending her mathematical modeling expertise to the fight against coronavirus.
Bertozzi and her fellow UCLA mathematics professor, Mason Porter, are modeling the virus’s spread in particular networks, such as universities and care facilities.
Is it safe to reopen a bustling college campus in the midst of coronavirus?
There are varying opinions. Some universities plan to continue all online courses, others– UCLA included— will opt for an in-person and online hybrid, and recent case spikes have spurred a few to reverse their fall 2020 decisions. Less than a week after announcing their initial in-person reopening plans, the University of Southern California asked Trojans to stay home and continue remote learning.
Bertozzi and Porter’s National Science Foundation grant-funded research project will offer insights and rigorous analysis to help colleges make informed decisions for the upcoming academic terms.
“We had a student project look at some of the UCLA courses offered and simulate what happens if they have so many courses online versus in person,” Bertozzi said.
The same project will explore how the virus moves through care facilities, such as skilled nursing homes. These facilities also have a particular network structure in which nurses treat and come in contact with multiple patients in a day.
Bertozzi’s work also focuses on expanding the epidemic modeling field. In a paper co-authored with Elisa Franco, George Mohler, Martin B. Short, and Daniel Sledge, she tested three different models for early time data.
The paper aims to demystify epidemic modeling for the scientific community.
There are fewer than 8,000 employed epidemiologists in the United States, and Bertozzi said she believes that scientists from other fields can understand the basic modeling and help out during an epidemic crisis.
“Every single county in the United States wants to have their own forecast,” the mathematics professor said. “There could be a small town with a brilliant scientist who doesn’t know much about epidemics, but they could take our paper and learn about the models and maybe do something with it.”
The paper, published in Proceedings of the National Academy of Sciences of the United States of America, found models with the fewest parameters are the best choice for early time data sets with limited information.