Evaluating the impact of modelling Omicron strain dynamics on short-term COVID-19 forecast performance

This repository contains the code, and analysis used to evaluate the impact of modelling Omicron strain dynamics on short-term COVID-19 forecast performance. In addition it also contains the write up of this work. See the documentation for further details.

Citation

If making use of the results of this analysis or reusing the analysis pipeline please cite:

If making using of the models evaluated in this analysis please also cite forecast.vocs:

Documentation

Document	Purpose
Summary	A summary of this work.
Paper	The academic paper write up of this work.
Supplementary information	The supplementary information for the write up of this work.
Project README	Overarching project README. Includes links to resources, a summary of key files, and reproducibility information.
Analysis pipeline	The targets based analysis pipeline.
Analysis archive	An archived version of the _targets directory. Download using get_targets_archive().
Data	Documentation for input data and summarised output from the analysis.
bin	Documentation for orchestration of nowcast estimation, publishing, and archiving.
News	Dated development notes.
epinowcast	The documentation for forecast.vocs the R package used to implement the models evaluated here.

Key files and folders

Folder/File	Purpose
writeup	Summary paper and additional supplementary information as Rmarkdown documents.
_targets.R	Analysis workflow. Combines separate sections from_targets_r.
R	R functions used in the analysis and for evaluation.
data	Input data and summarised output generated by steps in the analysis.
analyses	Ad-hoc analyses not part of the overarching workflow.
.devcontainer	Resources for reproducibility using vscode and docker.

Dependencies

All dependencies can be installed using the following,

remotes::install_dev_deps()

If cmdstanr has not been installed previously then stan may need to be installed. This can be done using the following,

cmdstanr::install_cmdstan()

Alternatively a docker container and image is provided. An easy way to make use of this is using the Remote development extension of vscode.

Analyses

This analysis in this repository has been implemented using the targets package and associated packages. The workflow is defined in _targets.md and can be explored interactively using _targets.Rmd Rmarkdown document. The workflow can be visualised as the following graph.

This complete analysis can be recreated using the following (note this may take quite some time even with a fairly large amount of available compute),

bash bin/update-targets.sh

Alternative the following targets functions may be used to interactively explore the workflow:

• Run the workflow sequentially.

targets::tar_make()

• Run the workflow using all available workers.

targets::tar_make_future(workers = future::availableCores())

• Explore a graph of the workflow.

targets::tar_visnetwork(targets_only = TRUE)

Watch the workflow as it runs in a shiny app.

targets::tar_watch(targets_only = TRUE)

Objects from the workflow can be loaded into an interactive session using the following,

targets::tar_load("<target>")

To use our archived version of the interim results (and so avoid long run times) use the following to download it. Note that this process has not been rigorously tested across environments and so may not work seamlessly).

source(here::here("R", "targets-archive.R"))
get_targets_archive()