stevencarlislewalker / McMasterPandemic

SEIR+ model

McMasterPandemic

Compartmental epidemic models for forecasting and analysis of infectious disease pandemics: contributions from Ben Bolker, Jonathan Dushoff, David Earn, Morgan Kain, Michael Li, Irena Papst (in alphabetical order). Feedback is welcome at the issues list, or e-mail us.

Installation

The repository contains an R package and various workflows/analyses. You can fork/clone the repository (from here) and install locally or use remotes::install_github("bbolker/McMasterPandemic") to install the package. You will need to first install the developer version of bbmle (remotes::install_github("bbolker/bbmle")) before installing McMasterPandemic.

For developers

• to re-install the package, including re-building and incorporating vignettes, use make build
• If you modify function arguments, you should change the roxygen documentation accordingly. If you change the roxygen documentation, please use make doc-update to update the .Rd files.
• please test/check the package periodically as you go (use make pkgcheck and make pkgtest from the shell or devtools::check() and devtools::test() from within R). (Tests are also run on GitHub Actions; if you want to skip CI testing, e.g. for a trivial commit, put [skip ci] somewhere in your commit message.) Please don't make a habit of pushing without testing.
• Code that is used in the refactoring process should go in the top-level refactor folder.

Documentation

The documentation is a little bit scattered right now, working on cleaning it up. In addition to the standard short descriptions of the functions (help(package="McMasterPandemic")), stuff can be found:

• in the vignettes (look at the source code in the [vignettes] directory or vignette(<title>, package="McMasterPandemic"))
  • getting_started
  • model: design decisions and information for developers
  • calibration (very out of date)
  • farr: stuff on Farr's law and phenomenological curve-fitting (very incomplete and likely to remain so for now)
  • testing_flow: incorporating testing dynamics (ditto)
• McMasterReport.Rnw: this is a more or less up-to-date description of calibration to Ontario data
• ontario_calibration_report.html: more technical and less up-to-date than the preceding document
• TODO.md: active to-do list
• issues list on github

More bits and pieces: notes/refactor.Rmd, testing.md, reimplementation.md

DISCLAIMER

All use of this package is at your own risk. Quantitative forecasts are only as good as their parameter estimates.