UC San Diego Researcher Wins $5.6M NIH Avant-Garde Award to Develop AI Tools for HIV, Hepatitis C, and Overdose Prevention
核心洞察
Natasha Martin, DPhil, at UC San Diego School of Medicine (搜索) has received a five-year, $5.6 million NIDA (搜索) Avant-Garde Award to develop AI-driven public health tools.
The AMPLIFY project will create "digital twins" using large language models to simulate intervention preferences of people who use drugs across diverse U.S. communities.
These AI simulations will be integrated into transmission models to test prevention strategies for HIV (搜索), hepatitis C (搜索), and overdose before real-world implementation.
Natasha Martin, DPhil, professor and vice chief of Global Public Health in the Division of Infectious Diseases and Global Public Health at UC San Diego School of Medicine (搜索), has been awarded a five-year, $5.6 million Avant-Garde Award from the National Institute on Drug Abuse (NIDA (搜索)). The funding will support the development of innovative artificial intelligence tools designed to improve public health responses to Human Immunodeficiency Virus (HIV (搜索)), hepatitis C (搜索) virus (HCV), and overdose among people who use drugs across the United States.
The NIDA (搜索) Avant-Garde Award is one of the most prestigious and competitive NIH research awards, given to only one to three researchers each year. It supports exceptionally creative scientists pursuing bold, high-impact research with the potential to transform HIV (搜索) and substance use disorder research.
"For too long, local intervention preferences of people who use drugs have been absent in the data that guide responses to HIV (搜索), hepatitis C (搜索) and overdose," said Martin. "This project will combine community perspectives with cutting-edge AI tools to help health departments better understand local needs, allocate resources more effectively and ultimately save lives. The project is called 'AMPLIFY,' because it is designed to amplify the voices of people who use drugs and ensure their priorities shape the interventions delivered in their communities."
Bridging the data gap with AI-powered digital twins
Despite major advances in HIV (搜索) prevention and treatment, including long-acting therapies, the U.S. continues to face persistent challenges in preventing HIV, HCV, and overdose among people who use drugs. A key obstacle is that public health agencies often lack timely information about the preferences, needs, and behaviors of the populations they serve, particularly in communities with limited surveillance and research infrastructure.
Martin's project aims to address that gap by creating realistic, community-informed "digital twins" — AI-powered simulations that reflect the intervention preferences and decision-making patterns of people who use drugs. Using data from multiple cohorts and a community-based participatory approach, the research team will train large language models to generate digital twins that can represent people in different regions and circumstances across the country.
These digital twins will then be integrated into sophisticated models that simulate HIV (搜索) and HCV transmission, overdose, and related health outcomes. Researchers will use these models to test different prevention and treatment strategies before they are implemented in the real world, helping public health leaders identify approaches that deliver the greatest benefit at the lowest cost for their communities.
"Applying AI to predict preferences of people who use drugs could be transformative," Martin said. "Beyond this project, this approach could help design new health services, identify barriers to care and tailor interventions for populations whose needs are poorly understood."
Building on established public health collaborations
The project builds on years of collaboration between UC San Diego researchers and public health agencies, including work conducted through the Resilient Shield initiative, a Centers for Disease Control-funded center led by Martin and Eliah Aronoff-Spencer, MD, PhD, professor of medicine at UC San Diego School of Medicine (搜索). That initiative provides outbreak analytics and modeling support to health departments. Through that work, researchers observed that strategies successful in one location did not always translate effectively to another because intervention preferences differed, highlighting the need for more personalized and locally informed approaches.
A central component of the new project is engaging people with lived and living experience of substance use to help guide the development of the AI models. Community members will play a key role in shaping how the digital twins are designed, tested, and applied, helping establish safeguards, build trust, and ensure the technology addresses the needs and priorities of the communities it is intended to serve.
Translating research into actionable tools
The team will ultimately develop an interactive dashboard that public health departments can use to evaluate prevention strategies, assess resource allocation options, and respond more effectively to emerging health challenges. By providing rapid, data-driven insights, the platform is designed to help decision-makers direct limited resources where they can achieve the greatest impact.
In addition to Martin, the project includes UC San Diego collaborators: Ravi Goyal, PhD; Eli Aronoff-Spencer, MD, PhD; Steffanie Strathdee, PhD; Annick Bórquez, PhD; Laramie Smith, PhD; Britt Skaathun, PhD; and John Ayers, PhD, all of the Division of Infectious Diseases and Global Public Health at UC San Diego School of Medicine (搜索), and Angela Bazzi, PhD, of the Herbert Wertheim School of Public Health and Human Longevity Science.