RtEval

Outline

• The goal of this repository is to evaluate software packages for estimating the effective reproduction number.
• We do this by fitting models from a range of software packages to simulated and real data
• In the future, these data will be stored in another repository ("rtdata")
• In the future, we will summarise the output of each package consistently using the summrt R package
• We hope that this repository will contain evaluations which are run semi-regularly using GitHub actions so as to serve as a living, rather than static, comparison

Live vignettes

The following vignettes (which are hosted under the vignettes folder) were automatically built using GitHubActions:

• epiestim: https://epistorm.github.io/RtEval/epiestim_vignette.html
• EpiLPS: https://epistorm.github.io/RtEval/EpiLPS_vignette.html
• EpiNow2: https://epistorm.github.io/RtEval/EpiNow2_vignette.html
• RtEstim: https://epistorm.github.io/RtEval/RtEstim_vignette.html
• Joint: https://epistorm.github.io/RtEval/eval_vignette.html

Structure

Remains to scope!

Set-up

To run the code in this repository you'll need at least R version 4.0.0:

required_version <- "4.0.0"
if (getRversion() < required_version) {
  stop(paste("Your R version is", getRversion(), ". Please update to version", required_version, "or higher."))
} else {
  cat(paste("R version is sufficient:", getRversion(), "\n"))
}

You will also need these packages:

install.packages(
  c(
    "rmarkdown", "shiny", "knitr", "plotly",
    "purrr", "splines", "tidyverse", "readr",
    "EpiLPS", "EpiNow2", "lubridate", "rstan",
    "cmdstanr", "R0", "remotes", "ggpubr",
    "ggtext", "lemon", "patchwork"
  )
)

remotes::install_github("dajmcdon/rtestim")

install.packages("EpiEstim", repos = c("https://mrc-ide.r-universe.dev", "https://cloud.r-project.org"))

Rt Collabathon 2024

This work is a part of the Epistorm Rt Collabathon.

Participants in the workshop may be interested in these links:

• Folder
• Developer/Implementor
• User/Evaluator
• Decision Maker
• Workshop slides
• Join the Slack workspace
• Synthetic GLEAM data from Jessica Davis, Ph.D

ToDo:

Some remaining todo's include:

• having some distributions vary over time in the simulated data (e.g., mean reporting delay gets shorter over time)