In epiforecasts/eval-germany-sp-nowcasting: Evaluating Semi-Parametric Nowcasts of COVID-19 Hospital Admissions in Germany

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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

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()

epiforecasts/eval-germany-sp-nowcasting documentation built on July 7, 2022, 8:56 p.m.