Welcome to the CoDatMo site

CoDatMo (COvid DATa MOdel) exists to replicate and host COVID-19 models written in the Bayesian modeling languages like Stan or PyMC. The goals include:

• Reproduce COVID-19 models with an eye to:
  • Get the word out on important COVID-19 models by offering well documented examples approachable by statiticians, programmers and researchers.
  • Increase the impact of the original researchers and get them additional exposure.
  • Help researchers improve their models via the replication process
• Provide a common language for implementing various models.
• Facillitate information exchange between model developers.
• Provide public access to models.

Models/Resources

The top level organization is https://github.com/codatmo which contains:

• The repository for this website https://github.com/codatmo/codatmo.github.io
. This file is index.html.
• An example model to work from https://codatmo.github.io/model_template/
• Active repos
• https://codatmo.github.io/Liverpool/
• https://codatmo.github.io/UNINOVE_Brazil/
• https://codatmo.github.io/Simple_SIR/
• Place holder repos for the following:
  • https://github.com/codatmo/Manchester
  • https://github.com/codatmo/Cambridge
  • https://github.com/codatmo/Lancaster
  • https://github.com/codatmo/Imperial

Contact

Email: Breck Baldwin, breckbadlwin@gmail.com

Leadership

• Columbia-US: Andrew Gelman (NSF PI)
• Liverpool-UK: Simon Maskell (Liverpool PI)
• UNINOVE-Brazil: Jose Storopoli (Universidade Nove de Julho PI)

Acknowledgements

This site is supported by NSF RAPID grant 2055251. The opinions, findings, and conclusions or recommendations expressed are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.