Using predictive models to help public health departments control infectious diseases
Official title: Real-time predictive modeling for public health departments to control infectious diseases
Funded at Stanford University · NIH-10898073 · Data updated 2026-11-12
This study is working on creating tools to help health departments make smart choices about vaccinations during outbreaks, like COVID-19, by figuring out which groups and areas need them the most, so everyone can stay healthier.
Quick facts
| Grant type | NIH-funded research |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Stanford University NIH-funded |
| Lab location | 1 site (Stanford, United States) |
| Project start | July 1, 2023 |
| Project end | July 31, 2027 |
| Funding year | 2024 |
| Award (this year) | $463,200 |
| NIH project number | 5DP2AI170485-03 |
| Project ID | NIH-10898073 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research include individuals living in high-risk areas for infectious diseases, particularly those susceptible to pertussis, seasonal influenza, and hepatitis A.
✕Less likely to help
Patients who are not at risk for infectious diseases or who do not reside in targeted high-risk areas may not benefit from this research.
What this research studies
This research focuses on developing real-time predictive models that assist public health departments in making informed decisions about controlling infectious diseases, particularly during the COVID-19 pandemic. By analyzing epidemiological data, the project aims to identify high-risk populations and locations for targeted vaccination efforts against diseases like pertussis and seasonal influenza. The goal is to optimize resource allocation and improve public health outcomes by translating predictive modeling into actionable policies. Collaborating with the California Department of Public Health, the research seeks to enhance the effectiveness of vaccination strategies.
Why it matters
Potential benefit: If successful, this research could lead to more effective vaccination strategies that significantly reduce the incidence of infectious diseases.
How similar studies have gone: Other research has shown success in using predictive modeling for public health interventions, indicating that this approach has potential for impactful outcomes.
Where this research is happening
-
Stanford University
Stanford, United States Active
Researchers
- Principal investigator: Lo, Nathan — Stanford University
- Study coordinator: Lo, Nathan
Related research
Other NIH-funded projects on the same topics.
-
Multimodal AI to predict CAR-T patient response and guide safer, smarter cell design
A Stanford team is building AI tools that use patient and CAR-T cell data to predict how well a CAR-T therapy will work and what design changes might boost effectiveness and safety for people with cancer.
-
Community-driven model to reduce overdose deaths through smarter resource use
A Stanford-led project uses a data-driven model to help counties pick the best ways to prevent opioid and stimulant overdoses and save lives.
-
Center for Dissemination and Implementation at Stanford: Research Core
A Stanford-based program is advancing how evidence-based addiction treatments are implemented across settings to help people with substance use disorders receive better care.
-
Center for Dissemination and Implementation At Stanford (C-DIAS)
A center-based program at Stanford aims to speed people who need addiction treatments to get evidence-based care.
-
Mitochondrial structure and neuron health in Alzheimer's and ALS
This grant studies how mitochondrial cristae structure in neurons is controlled and how its disruption may contribute to diseases like Alzheimer's and ALS.
-
Pediatric EMS seizures: simpler age-based midazolam dosing
A nationwide trial at EMS agencies tests whether using fixed, age-based dosing of midazolam for seizures in children improves treatment speed and safety.
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.