Overall spread of diseases such as tuberculosis (TB), malaria and gonorrhea can be reduced with an algorithm developed by USC Viterbi School of Engineering scientists. Ensuring the public outreach campaigns effectively that reach undiagnosed patient can prevent the spread of treatable diseases. The algorithm makes use of the most of limited resources, such as advertising budgets.To create the algorithm, the researchers used data, including behavioral, demographic and epidemic disease trends, to create a model of disease spread that captures underlying population dynamics and contact patterns between people.
‘AI algorithm uses the subtle interactions between variables that can substantially reduce disease spread overall.’Using computer simulations, the researchers tested the algorithm on two real-world cases: tuberculosis (TB) 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.
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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."
Revealing disease dynamics
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. For instance, people may not be cured instantly, so reducing prevalence at age 30 could mean creating targeted public health communications for people at age 27.
"Fewer still consider how to use an algorithmic approach to optimize these policies given the uncertainty of our estimates of these disease dynamics. We take both of these effects into account in our approach."
In the future, the study's insights could also shed light on health outcomes for other infectious disease interventions, such as HIV or the flu.