Tuesday, February 20, 2018

Artificial intelligence algorithm to predict and prevent spread of infectious diseases

Team of researchers from USC Viterbi School of Engineering has created an algorithm that can help policymakers reduce the overall spread of disease. The algorithm is optimized to make the most of limited resources, such as advertising budgets, thus helping cash strapped public health agencies.

To create the artificial intellegence algorithm, the researchers used behavioral, demographic and epidemic disease trends data to generate a model of disease spread that captures underlying population dynamics and contact patterns between people. Using computer simulations, the researchers tested the algorithm on tuberculosis (TB) spread in India and gonorrhea in the United States. In both cases, they found the algorithm did a better job at reducing disease cases than current health outreach policies by sharing information about these diseases with individuals who might be most at risk.

The study was published in the AAAI Conference on Artificial Intelligence. The authors are Bryan Wilder, a candidate for a PhD in computer science, Milind Tambe, the Helen N. and Emmett H. Jones Professor in Engineering, a professor of computer science and industrial and systems engineering and co-founder of the USC Center for AI in Society and Sze-chuan Suen, an assistant professor in industrial and systems engineering.

"Our study shows that a sophisticated algorithm can substantially reduce disease spread overall," says Wilder, the first author of the paper. "We can make a big difference, and even save lives, just by being a little bit smarter about how we use resources and share health information with the public."

The algorithm also appeared to make more strategic use of resources. The team found it concentrated heavily on particular groups and did not simply allocate more budget to groups with a high prevalence of the disease. This seems to indicate that the algorithm is leveraging non-obvious patterns and taking advantage of sometimes-subtle interactions between variables that humans may not be able to pinpoint. The team's mathematical models also take into account that people move, age, and die, reflecting more realistic population dynamics than many existing algorithms for disease control.

Adapted from press release by University of South California.
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