epiforecasts / eval-germany-sp-nowcasting

Evaluating Semi-Parametric Nowcasts of COVID-19 Hospital Admissions in Germany

Evaluating Semi-Parametric Nowcasts of COVID-19 Hospital Admissions in Germany

This repository contains the documentation, results, and code of a project evaluating the use of a semi-parametric nowcasting approach for COVID-19 hospitalisations in Germany. See the documentation for further details. This project is part of a wider collaboration assessing a range of nowcasting methods whilst providing an ensemble nowcast of COVID-19 Hospital admissions in Germany by date of positive test. This ensemble should be used for any policy related work rather than the nowcasts provided in this repository. See here for more on this nowcasting collaboration.

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 epinowcast:

Sam Abbott (2021). epinowcast: Hierarchical nowcasting of right censored epidemological counts, DOI: 10.5281/zenodo.5637165

A BibTeX entry for LaTeX users is also available:

@Article{, title = {epinowcast: Hierarchical nowcasting of right censored epidemological counts}, author = {Sam Abbott}, journal = {Zenodo}, year = {2021}, doi = {10.5281/zenodo.5637165}, }

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.
Real-time model evaluation	A report visualising and evaluating nowcasts from the various model configurations considered here in real-time.
Real-time method evaluation	A report visualising and evaluating nowcasts from the various methods (from this project and other groups) submitted to the Germany nowcasting hub in real-time.
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Analysis archive	An archived version of the _targets directory.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 epinowcast the R package used to implement the models evaluated here. See this case study for a simplified version of this analysis.
Germany nowcasting hub	The homepage (containing a dashboard and information) for the Germany nowcasting hub project to which nowcasts from this evaluation are submitted along with others produced by independent groups.

Key files and folders

Folder/File	Purpose
writeup	Summary paper and additional supplementary information as Rmarkdown documents.
_targets.Rmd	Analysis workflow for interactive use.
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. This includes a synthetic case study and a simplified example using Germany hospitalisation data.
.devcontainer	Resources for reproducibility using vscode and docker.

Dependencies

All dependencies can be installed using the following,

remotes::install_dev_deps()

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)

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", "targetss-archive.R"))
get_targets_archive()