BayesHealth
Bayesian Hub for Health Sciences
A collaborative hub advancing Bayesian methods for healthcare, biomedical research, and public health.
Connecting researchers, statisticians, clinicians, and students to learn, collaborate, and apply modern Bayesian strategies.
About BayesHealth
BayesHealth is a collaborative hub focused on Bayesian statistical methodology and data-driven health research.
We aim to make Bayesian approaches easier to understand and provide a platform for sharing tools, tutorials, and research insights.
Bayesian methods allow researchers to:
- Combine prior knowledge with data in a meaningful way
- Quantify uncertainty and make probabilistic predictions
- Build hierarchical, multilevel, and network models
- Perform adaptive and sequential analyses for efficient decision-making
Our goal is to create a community for learning, collaboration, and innovation, making Bayesian methods accessible to all health researchers and data scientists.
Our Mission
- Communicate and promote Bayesian methodology for health research
- Foster collaboration across statistics, epidemiology, medicine, and genomics
- Provide training, tutorials, and resources for students and professionals
- Showcase methodological innovations and best practices
Research Wings
BayesHealth organizes its research into five integrated wings, each focused on a major domain in health sciences where Bayesian methods provide transformative insights.
Focuses on population-level health patterns, infectious disease dynamics, environmental health and health policy evaluation* using Bayesian approaches.
Key topics
- Population health risk estimation across regions
- Surveillance systems and early warning models
- Hierarchical modeling for multi-region or global comparisons
- Spatiotemporal modeling of infectious diseases
Focuses on long-term disease progression, chronic condition risk, and prevention strategies using Bayesian approaches.
Key topics
- Longitudinal and cohort studies for chronic disease progression
- Survival and time-to-event modeling for long-term outcomes
- Multimorbidity and comorbidity modeling
- Patient-level disease trajectory modeling
Focuses on designing, monitoring, and analyzing clinical research to improve patient outcomes and trial efficiency.
Key topics
- Bayesian dose-finding and platform trials
- Adaptive trial designs, interim monitoring, and early stopping
- Bayesian clustered randomized trials
- Probabilistic benefit–risk assessment for treatments
Focuses on decision-making under uncertainty, supporting cost-effective healthcare and policy evaluation.
Key topics
- Probabilistic cost-effectiveness analysis
- Value-of-information and risk-benefit modeling
- Scenario simulations for healthcare strategies
- Resource allocation and policy planning
Focuses on analyzing complex biological data to enable personalized medicine and predictive modeling.
Key topics
- Genome-wide association studies (GWAS)
- Probabilistic gene and protein network modeling
- Hierarchical integration of multi-omics datasets
- Biomarker discovery and patient stratification
Collaboration
BayesHealth welcomes collaborations and participation from students, researchers, and institutions interested in learning, applying, and advancing Bayesian methods in health sciences.
We provide tutorials, workshops, and open resources to foster knowledge exchange across statistics, epidemiology, medicine, genomics, and computational sciences.
If you would like to collaborate or learn more, please fill out the form below: