An international team of researchers has developed a new mathematical tool that could help scientists to deliver more accurate predictions of how diseases, including COVID-19, spread through towns and cities around the world.
Rebecca Morrison, an assistant professor of computer science at the University of Colorado Boulder, led the research. For years, she has run a repair shop of sorts for mathematical models—those strings of equations and assumptions that scientists use to better understand the world around them, from the trajectory of climate change to how chemicals burn up in an explosion.
As Morrison put it, “My work starts when models start to fail.”
She and her colleagues recently set their sights on a new challenge: epidemiological models. What can researchers do, in other words, when their forecasts for the spread of infectious diseases don’t match reality?
In a study published today in the journal Chaos, Morrison and Brazilian mathematician Americo Cunha turned to the 2016 outbreak of the Zika virus as a test case. They report that a new kind of tool called an “embedded discrepancy operator” might be able to help scientists fix models that fall short of their goals—effectively aligning model results with real-world data.
Morrison is quick to point out that her group’s findings are specific to Zika. But the team is already trying to adapt their methods to help researchers to get ahead of a second virus, COVID-19.
“I don’t think this tool is going to solve any epidemiologic crisis on its own,” Morrison said. “But I hope it will be another tool in the arsenal of epidemiologists and modelers moving forward.”
When models fail
The study highlights a common issue that modelers face.
“There are very few situations