Using advanced data analysis to understand long-term effects of COVID-19.
Official title: Applying a Targeted Machine Learning and Causal Inference Approach to Analyzing Long-Term Sequelae of COVID-19 Infection Through the National COVID Cohort Collaborative.
Funded at University of California Berkeley · NIH-10865395 · Data updated 2026-11-12
This study is looking at how COVID-19 can affect your health in the long run, using data from many patients to find patterns that could help improve care for those dealing with long COVID.
Quick facts
| Grant type | Career grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of California Berkeley NIH-funded |
| Lab location | 1 site (Berkeley, United States) |
| Project start | September 4, 2024 |
| Project end | August 31, 2029 |
| Funding year | 2024 |
| Award (this year) | $134,923 |
| NIH project number | 1K01AI182501-01 |
| Project ID | NIH-10865395 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research are individuals who have experienced COVID-19 and are facing ongoing health issues related to the infection.
✕Less likely to help
Patients who have not been infected with COVID-19 or those without long-term symptoms may not benefit from this research.
What this research studies
This research investigates the long-term health effects of COVID-19 by utilizing advanced machine learning and causal inference techniques. The study analyzes data from the National COVID Cohort Collaborative, which includes electronic health records from a diverse population of COVID-19 patients. By identifying patterns and risk factors associated with long COVID, the research aims to provide insights that could improve patient care and treatment strategies. The project is led by a team of experts in biostatistics and epidemiology, ensuring a robust and interdisciplinary approach to the analysis.
Why it matters
Potential benefit: If successful, this research could lead to better understanding and management of long-term COVID-19 symptoms, ultimately improving patient outcomes.
How similar studies have gone: Other research has shown promise in using machine learning to analyze health outcomes related to COVID-19, indicating that this approach is both relevant and potentially impactful.
Where this research is happening
-
University of California Berkeley
Berkeley, United States Active
Researchers
- Principal investigator: Butzin-Dozier, Zachary — University of California Berkeley
- Study coordinator: Butzin-Dozier, Zachary
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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.