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AI can help reduce the risk of HIV in high-risk communities

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The coronavirus pandemic has shown that one of the most important weapons in the fight against any disease is accurate information — and trusted individuals within a community to disseminate it.

How to get good information to the people who need it is a question that has long plagued public health officials.

One approach, known as peer change agents, is to recruit peer leaders to promote healthy behaviors and information about disease prevention within their social networks. This strategy has been used, with mixed results, in communities at high risk for HIV infection and transmission, specifically among young people experiencing homelessness.

Youth experiencing homelessness are 10 times more likely to test positive for HIV than young people who have access to stable housing.  Social workers and public health officials have used the peer change agents strategy to promote behaviors such as condom usage and regular HIV testing within these communities, but success seems tied to choosing the right peer leaders who will have the largest impact within their communities.

That’s where AI can help. For nearly two decades, computer scientists have been exploring how a limited number of nodes in a social network can be used to maximize information dissemination.

Now, researchers at the Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS), in collaboration with the University of Southern California and Pennsylvania State University, have developed an AI system that can identify the people within a social network who can most effectively promote information about HIV prevention to their peers. In a field trial conducted with more than 700 homeless youth, the researchers found that their algorithm significantly reduced key risk behaviors for HIV transmission within the population.

The research was presented at the Association for the Advancement of Artificial Intelligence (AAAI) Conference.

“Counterintuitively, the most popular people aren’t usually the most connected within a network,” said Milind Tambe, the Gordon McKay Professor of Computer Science at SEAS and senior author of the paper. “As computer scientists, we can optimize the selection of a peer leader based on who has the most connections to different groups.”

The research team partnered with social workers at drop-in centers for homeless youth, places where young people experiencing homeless can receive food, clothing, case management and mobile HIV testing. The team recruited more than 700 participants across three centers.

Source link The Harvard

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