Real-time tracking and prediction of vaccine-preventable infections using online data and AI
Digital data streams and machine learning for real-time modeling of vaccine-preventable infectious diseases
['FUNDING_OTHER'] · BOSTON CHILDREN'S HOSPITAL · NIH-11406016
This project combines online information (news, social media, searches, and movement data) with machine learning to provide earlier warnings and forecasts of vaccine-preventable disease outbreaks for communities and health officials.
Quick facts
| Phase | ['FUNDING_OTHER'] |
|---|---|
| Study type | Nih_funding |
| Sex | All |
| Sponsor | BOSTON CHILDREN'S HOSPITAL (nih funded) |
| Locations | 1 site (BOSTON, UNITED STATES) |
| Trial ID | NIH-11406016 on ClinicalTrials.gov |
What this research studies
The team collects digital data sources—such as social media posts, news trends, search queries, and anonymized mobility data—and applies machine-learning methods to detect signals linked to disease spread. They will develop tools to estimate how transmissible an infection is over time, monitor behaviors that change transmission risk, and forecast where outbreaks may grow. The proposal includes an ensemble approach that merges estimates from multiple groups and a surveillance system that links behavior data to forecasts. Results are intended to inform public health actions that could reduce infections in communities.
Who could benefit from this research
Good fit: Ideal candidates are people living in communities experiencing vaccine-preventable infections or individuals and public-health partners willing to allow use of de-identified digital or mobility data for surveillance.
Not a fit: People with no digital footprint or those not affected by vaccine-preventable diseases may not directly benefit from the project.
Why it matters
Potential benefit: If successful, this work could give earlier, more accurate outbreak warnings and help public health teams target vaccinations and interventions to prevent illness.
How similar studies have performed: Related digital surveillance and machine-learning approaches have improved flu and COVID-19 tracking before, but integrating many data streams and standardizing transmissibility estimates is still relatively new.
Where this research is happening
BOSTON, UNITED STATES
- BOSTON CHILDREN'S HOSPITAL — BOSTON, UNITED STATES (ACTIVE)
Researchers
- Principal investigator: MAJUMDER, MAIMUNA SHAHNAZ — BOSTON CHILDREN'S HOSPITAL
- Study coordinator: MAJUMDER, MAIMUNA SHAHNAZ
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 below.