Case Western Reserve University team led by computer and data scientist awarded $1.2 million National Science Foundation grant to develop AI-powered framework to combat infectious disease outbreaks
Computer and data science researchers at Case Western Reserve University are developing an artificial intelligence (AI)-powered tool to help public health and other officials make more informed decisions when faced with infectious disease outbreaks.
The National Science Foundation (NSF) recently awarded a $1.2 million grant to support the project, led by Yanfang (Fanny) Ye, the Theodore L. and Dana J. Schroeder Associate Professor of computer and data sciences at Case Western Reserve’s Case School of Engineering.
“It will help people because it helps the public health officials looking out for them,” Ye said. “This is among the leading lessons from the COVID-19 pandemic: Give good information as quickly and accurately as possible, so government and the general public can make informed, safe decisions.”
Ye is also working with Case Western Reserve researchers in anthropology and economics. They will help her write more detailed computer algorithms that consider the demographics of a given community and how people are likely to respond to the disease outbreak itself—and to specific efforts to contain it.
The project is related to one led by Ye in April 2020: an online risk-assessment tool that produces timely location-specific information about COVID-19 exposure. That tool analyzes and combines an array of information into a cohesive format to reveal potential health-problem areas—and possible solutions—for a community.
Ye’s 2020 project resulted in a web- and mobile-based resource for anyone in the United States who wanted to assess their risk level. This new framework, however, will be geared for officials, organization and community leaders so they can make timely and effective collaborative decisions in response to infectious disease outbreaks.
This new project’s goal is to help officials and decision makers know how and when to use specific non-pharmaceutical intervention (NPI) or community mitigation strategies.
These interventions are actions—apart from getting vaccinated and taking medicine—that people and communities can take to help slow the spread of illnesses like pandemic influenza or COVID-19.
NPIs such as staying home when sick, covering coughs and sneezes, washing hands often and postponing mass gatherings are among the best ways of controlling pandemic flu when vaccines are not yet available, according to the Centers for Disease Control.
The Case Western Reserve researchers will develop an AI-augmented framework that will consider disease-related data from public health officials, but also social and behavioral data, Ye said. That could mean anything from social statistics like crime rates or suicide rates, to economic data like income or unemployment rates, she said.
“For public officials to know when and how hard to pull any given lever (the NPIs), they have to consider so many other issues, that it’s difficult to know how to analyze and apply them,” she said. “This framework will help them make those decisions more confidently and effectively.”
Other Case Western Reserve researchers on the project include Kenneth Loparo, the Arthur L. Parker Professor in the Department of Electrical, Computer and Systems Engineering; Lee Hoffer, associate professor of anthropology; and Daniel Shoag; associate professor of economics.
For more information, contact Mike Scott at email@example.com.
This article was originally published Aug. 26, 2021.