Effectively reaching people who are unwittingly spreading treatable diseases like malaria, gonorrhea and tuberculosis is the challenge many public health agencies face with current prevention efforts. As such, researchers from the USC Viterbi School of Engineering have developed an algorithm to help reduce the spread of such diseases by targeting prevention efforts at those most at risk.

Using data concerning behavioral, demographic and epidemic disease trends, researchers developed a model of disease spread, capturing information such as underlying population dynamics and contact patterns among people.

Researchers, using computer simulations, tested the algorithm on cases involving tuberculosis in India and gonorrhea in the U.S. In both instances, the researchers determined that the algorithm better prevented the spread of the diseases by sharing disease information with individuals most at risk — surpassing traditional outreach methods.

"Our study shows that a sophisticated algorithm can substantially reduce disease spread overall," said Bryan Wilder, a candidate for a PhD in computer science and 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 strength of the algorithm, according to researchers, is in pinpointing patterns that were not so obvious to researchers.

"While there are many methods to identify patient populations for health outreach campaigns, not many consider the interaction between changing population patterns and disease dynamics over time," said Sze-chuan Suen, an assistant professor in industrial and systems engineering.

"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."

For more on the study, click here.