Bayesian data tools to map and fight syphilis and congenital syphilis
Official title: SCH: Bayesian Phenotyping and Multilayer Network Inference for Actionable STI Epidemic Intelligence and Dynamics
Funded at University of Central Florida · NIH-11575522 · Data updated 2026-11-12
A data-driven, bayesian approach will turn messy health data into clear, local-level insights to prevent congenital syphilis and reduce syphilis in communities.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of Central Florida NIH-funded |
| Lab location | 1 site (Orlando, United States) |
| Project start | September 2, 2026 |
| Project end | August 31, 2030 |
| Funding year | 2026 |
| Award (this year) | $276,113 |
| NIH project number | 1R01AI206603-01 |
| Project ID | NIH-11575522 on NIH RePORTER |
Who could benefit from this research
✓Could help
Public health professionals and researchers working with syphilis surveillance, prevention programs, and maternal-fetal health.
✕Less likely to help
Direct patient participation is not the focus of this mathematical-epidemiological project.
What this research studies
This project will combine noisy, real-world data from labs, clinics, and public health with advanced Bayesian methods to produce small-area estimates of syphilis incidence and pregnancy-related pathways. It will create interpretable phenotypes, test-accuracy aware metrics, and visualize how programs affect outcomes over time. The approach will integrate mobility, venues, and partner services to map community exposure and inform targeted interventions. A longitudinal, multilayer network and a pregnancy/vertical transmission submodule will connect screening, treatment timing, and congenital outcomes to forecast policy impacts.
Why it matters
Potential benefit: If successful, the work could sharpen local public health actions to prevent congenital syphilis and reduce syphilis transmission.
How similar studies have gone: Prior work shows promise for data-integrative Bayesian models in infectious disease surveillance, with potential for policy-relevant forecasting.
Where this research is happening
-
University of Central Florida
Orlando, United States Active
Researchers
- Principal investigator: Huang, Hsin-Hsiung — University of Central Florida
- Study coordinator: Huang, Hsin-Hsiung
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About this research
- This is an active NIH-funded research project — typically early-stage science, not a clinical trial accepting patient enrollment.
- Some NIH-funded labs run parallel clinical studies or seek volunteers for related work. To check, contact the principal investigator or institution listed above.
- For full project details, budget, and progress reports, visit the official NIH RePORTER page.