Baseline nowcast

Summary

Nowcasting right-truncated epidemiological data is critical for timely public health decision-making, as reporting delays can create misleading impressions of declining trends in recent data. This package provides nowcasting methods based on using empirical delay distributions and uncertainty from past performance. It is also designed to be used as a baseline method for developers of new nowcasting methods. For more details on the performance of the method(s) in this package applied to case studies of COVID-19 and norovirus, see our recent paper at https://wellcomeopenresearch.org/articles/10-614. The package supports standard data frame inputs with reference date, report date, and count columns, as well as the direct use of reporting triangles, and is compatible with ‘epinowcast’ objects. Alongside an opinionated default workflow, it has a low-level pipe-friendly modular interface, allowing context-specific workflows. It can accommodate a wide spectrum of reporting schedules, including mixed patterns of reference and reporting (daily-weekly, weekly-daily). It also supports sharing delay distributions and uncertainty estimates between strata, as well as custom uncertainty models and delay estimation methods.

Installation

Installing the package

You can install the latest released version using:

install.packages("baselinenowcast")

Alternatively, you can install the latest GitHub release from our r-universe repository:

install.packages("baselinenowcast", repos = "https://epinowcast.r-universe.dev")

To install the development version from GitHub, use the pak package:

pak::pak(file.path("epinowcast", "baselinenowcast"))

Another option for installation is using the remotes package:

remotes::install_github(file.path("epinowcast", "baselinenowcast"))

Resources

We provide a range of other documentation, case studies, and community spaces to ask (and answer!) questions:

Organisation Website

Our organisation website includes links to other resources, guest posts, and seminar schedule for both upcoming and past recordings.

Community Forum

Our community forum has areas for question and answer and considering new methods and tools, among others. If you are generally interested in real-time analysis of infectious disease, you may find this useful even if you do not use baselinenowcast.

Contributing

We welcome contributions and new contributors! We particularly appreciate help on identifying and identified issues. Please check and add to the issues, and/or add a pull request and see our contributing guide for more information.

How to make a bug report or feature request

Please briefly describe your problem and what output you expect in an issue. See our contributing guide for more information.

Code of Conduct

Please note that the baselinenowcast project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.

Citation

If you use baselinenowcast in your work, please consider citing it with citation("baselinenowcast") (or print(citation("baselinenowcast"), bibtex = TRUE)):

To cite baselinenowcast in publications please use the following.

  Johnson KE, Tang M, Tyszka E, Nemcova B, Wolffram D, Ergas R, Reich
  NG, Funk S, Mellor J, Bracher J, Abbott S (2025). "Baseline
  nowcasting methods for handling delays in epidemiological data."
  _Wellcome Open Research_. doi:10.12688/wellcomeopenres.25027.1
  <https://doi.org/10.12688/wellcomeopenres.25027.1>,
  <https://wellcomeopenresearch.org/articles/10-614>.

A BibTeX entry for LaTeX users is

  @Article{,
    title = {Baseline nowcasting methods for handling delays in epidemiological data},
    author = {Kaitlyn E. Johnson and Maria Tang and Emily Tyszka and Barbora Nemcova and Daniel Wolffram and Rosa Ergas and Nicholas G. Reich and Sebastian Funk and Jonathon Mellor and Johannes Bracher and Sam Abbott},
    year = {2025},
    journal = {Wellcome Open Research},
    doi = {10.12688/wellcomeopenres.25027.1},
    url = {https://wellcomeopenresearch.org/articles/10-614},
  }

  Johnson KE, Tyszka E, Bracher J, Funk S, Abbott S (2025).
  _baselinenowcast: Methods for baseline nowcasting right-truncated
  epidemiological data_.
  <https://github.com/epinowcast/baselinenowcast/>.

A BibTeX entry for LaTeX users is

  @Manual{,
    license = {MIT},
    title = {baselinenowcast: Methods for baseline nowcasting right-truncated epidemiological data},
    author = {Kaitlyn E. Johnson and Emily Tyszka and Johannes Bracher and Sebastian Funk and Sam Abbott},
    year = {2025},
    url = {https://github.com/epinowcast/baselinenowcast/},
  }

Contributors

All contributions to this project are gratefully acknowledged using the allcontributors package following the allcontributors specification. Contributions of any kind are welcome!