Imperial College have published their COVID-19 modelling code on GitHub. Their modelling has apparently been used in forming UK policy.
It makes use of two epidemiology packages.
earlyRfor estimation of infectiousness, as measured by the reproduction number (R), in the early stages of an outbreak.
EpiEstimfor estimating the time varying instantaneous reproduction number during epidemics
These packages, and many more, are part of the R Epidemics Consortium (RECON). They
[…] gather experts in data science, modelling methodology, public health, and software development to create the next generation of analytics tools for informing the response to disease outbreaks, health emergencies and humanitarian crises, using the R software and other free, open-source resources.
Andy Kirk at Visualising Data ran a Twitter poll about the relative accessibility of R and Python to non-developers.
59% said that R was more accessible.
Obviously, the poll is far from scientific, but the comments he received reflect my own experiences of teaching both languages—such as the significance of the RStudio IDE and the
tidyverse packages in getting people off the ground.
However, there are some downsides to notebooks—mostly to do with software engineering best practices.
Both authors make a good case and have interesting points. As ever, the truth is that notebooks are good in some situations and not so good in others.