Data Science Team

The Insight Discovery and Computational Modeling team is diverse team with backgrounds in statistics, computational biology, economics, manufacturing, and data science, and is member of the Cone Health Enterprise Analytics team. We specialize in applying advanced statistical, mathematical, and data science techniques to problems across the health system. Such topics covered include evaluation of methods, population cluster analysis, discrete/ agent based modeling, Bayesian inference, network analysis, time series analysis, and online prediction/ change-point detection.

Insight Discovery and Computational Modeling

Name
Michael DeWitt
Jennifer Wenner

Special Thanks

This website is largely inspired by the Epiforecasts team and their SARS-CoV-2 reporting. Additionally, their open sourcing of cutting edge modeling approaches was hugely beneficial.

The website was built using the distill framework (Allaire, Iannone, and Xie 2020) and R Markdown (Allaire et al. 2020).

Allaire, JJ, Rich Iannone, and Yihui Xie. 2020. Distill: ’R Markdown’ Format for Scientific and Technical Writing. https://CRAN.R-project.org/package=distill.

Allaire, JJ, Yihui Xie, Jonathan McPherson, Javier Luraschi, Kevin Ushey, Aron Atkins, Hadley Wickham, Joe Cheng, Winston Chang, and Richard Iannone. 2020. Rmarkdown: Dynamic Documents for R. https://github.com/rstudio/rmarkdown.

References

Reuse

Text and figures are licensed under Creative Commons Attribution CC BY 4.0. The figures that have been reused from other sources don't fall under this license and can be recognized by a note in their caption: "Figure from ...".