Fanghui Shi
University of South Carolina-Columbia, SC·Public Health & Epidemiology·Early-career
Sexually transmitted infections (STIs) such as syphilis, gonorrhea and chlamydia are rising. They raise the risk of acquiring HIV.
- Fields
Public Health & Epidemiology
Computer Science
Medicine & Clinical Research
- Topics
sexually transmitted infections
HIV prevention
viral suppression
- Grants
- 1 known grant, through Jun 2031
What they work on
- Build computable phenotype algorithms that identify sexually transmitted infection (STI) patterns from electronic health records and survey data in the All of Us research program.
- Develop machine learning models that use STI patterns and other modifiable and non-modifiable factors to predict HIV acquisition, disengagement from care, and failure to achieve viral suppression, focusing on an understudied population.
- Turn the model findings into clinical prevention and management guidelines that integrate STI surveillance, diagnosis and treatment into HIV care.
More about the research
Some reasons are biological, such as damage to protective tissue barriers and inflammation. Others are behavioral, such as sexual networks with higher risk. STIs are also linked to worse HIV care outcomes. Yet STI data are rarely built into models that predict who will get HIV, who will drop out of care, or whose virus will stay unsuppressed. Better predictions could let clinicians tailor prevention and treatment to each person.
Why it matters
People at risk for HIV and people living with HIV could benefit from care that accounts for their STI history. By showing how STI patterns predict HIV infection, dropout from care and viral suppression, this work could help clinicians and public health programs identify high-risk patients earlier. It also aims to produce practical clinical guidelines that may improve diagnosis and treatment retention and reduce new HIV infections, supporting the Ending the HIV Epidemic initiative.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Closest labs
Rachel Solnick
Icahn School of Medicine at Mount Sinai, NY·Medicine & Clinical Research·Early-career
Develop the TIPPED protocol, which uses routine post-discharge callbacks about STI results to offer HIV pre-exposure prophylaxis (PrEP), together with an implementation strategy shaped by stakeholder input.
Makella Coudray
University of Central Florida, FL·Public Health & Epidemiology·Early-career
Assess the acceptability, feasibility and accuracy of self-collected versus clinician-collected genital and non-genital specimens for STI detection among trans women, using mixed methods.
Marcy Ekanayake-Weber
University at Albany, NY·Public Health & Epidemiology·Early-career
Characterize how sexual behavior and partner networks vary across people in the New York Capital Region, using survey data and community-based group model building workshops.
The school
University of South Carolina-Columbia: 22 labs on PI Pages, $277M a year on research, 425 research doctorates a year.
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