- February 23, 2023
YSPH biostatistician part of award-winning Yale team
- February 14, 2023Source: Yale Public Health magazine
Multidisciplinary COPPER Center Brings a Public Health Lens to Cancer Care
- January 26, 2023
3 Essential Questions: New vaccine recommendations for COVID-19
- January 20, 2023Source: USA Today
How often do you need a COVID booster shot? Yearly, new data suggests.
Data Science
The Yale School of Public Health has a long history of contributing to the development of quantitative methodologies and tools for rigorous scientific research to address the most challenging problems in biology, medicine and public health.
Our researchers are working to:
- Improve public health and epidemiological study design
- Data collection
- Computation
- Statistical analysis
- Interpretation of findings.
The school is a leader in:
- The development and application of statistical methods in genetics/genomics and bioinformatics
- Epidemiology
- Causal inference
- Clinical trials
- Spatial/spatiotemporal data
- High-dimensional analysis
- Machine learning
- Biomedical imaging
- Network analysis and statistical computation
- Implementation science and health informatics
Recent Publications
-
Genome Biology
SUPERGNOVA: Local genetic correlation analysis reveals heterogeneous etiologic sharing of complex traits -
The Annals of Applied Statistics
A Hierarchical Baysian Model for Single-cell CLustering Using RNA-Sequencing Data -
PNAS
A Polynomial Algorithm for Best-subset Selection Problem -
Journal of the National Cancer Institute
Effect Sizes of Somatic Mutations in Cancer -
Epidemiology
Transmission Modeling with Regression Adjustment for Analyzing Household-based Studies of Infectious Disease: Application to Tuberculosis
Centers and other resources
for Faculty working in this area.