With an estimated 7 million Americans affected by the flu so far this year, leading researchers at New York University (NYU) have found a novel approach to help prevent the spread of seasonal bugs: use math.
NYU professor Maurizio Porfiri is collaborating with two Italian researchers with visiting appointments at NYU to develop a mathematical model that will ultimately help healthcare decision-makers lessen the impact of the flu by predicting when the season will peak, who should be vaccinated and when, and whether to quarantine infected patients. They’re finding answers by studying human behavior and social interactions.
“We all know that human contact plays an important role in the spread of disease, but up until now we haven’t had a good way to predict its effect — both before and during an outbreak,” said Porfiri, a professor of Mechanical and Aerospace Engineering, and Biomedical Engineering. Working with researchers Alessandro Rizzo and Lorenzo Zino of Politecnico di Torino, Italy, Porfiri has created a computer simulation that behaves the way people do — providing a more accurate measure of how social activity contributes to the eventual spread of a disease such as the flu.
In a recent paper published by Society for Industrial and Applied Mathematics in the SIAM Journal on Applied Dynamical Systems, the researchers discuss their advanced techniques for simulating real-world interactions between people — activities such as grabbing a coffee together, paying a visit to a friend or sharing a bus ride — and show how that information can be applied to more accurately model a flu outbreak. Their method relies on precise mathematical equations and algorithms, and is very easy to implement in everyday computer environments, Porfiri said.
“Social scientists have already proven that human interaction occurs in bursts of activity,” he explained. “Our computer model accurately predicts when those bursts will happen over time, helping to paint a better picture of how quickly disease is likely to spread or die down.”
The information provided by the tool is expected to help healthcare organizations perform better what-if analyses, ultimately leading to more targeted vaccination policies and more informed public service announcements related to the flu. Porfiri noted that the advanced analytical tool is on track to be ready for use by organizations such as the Centers for Disease Control and Prevention (CDC) within a few years.
“Our goal is to tackle diseases such as the flu using an accurate real-world simulation to raise public awareness and preparedness, so that policy makers can suggest best approaches and behaviors to avoid their spread,” Rizzo said.
Zino added that, “We know that flu season is going to arrive each year. By providing more accurate information that includes how people will behave, our tool can help to reduce the number of people affected.”
The ongoing work is funded by the Alexandria, VA-based National Science Foundation with Dr. Biagio Pedalino, an international consultant in public health and former CDC epidemiologist, serving as advisor. In 2016, the researchers proved the validity of their model using data from the 2014 Ebola outbreak in Liberia. Now they are working to refine their tool using historical influenza data to validate its accuracy.
Source: Society for Industrial and Applied Mathematics