Estimate epidemiological delay distributions for infectious diseases

Warning! This package is a prototype and is under active development. Breaking changes are likely.

Summary

Understanding and accurately estimating epidemiological delay distributions is important for public health policy. These estimates directly influence epidemic situational awareness, control strategies, and resource allocation. In this package, we provide methods to address the key challenges in estimating these distributions, including truncation, interval censoring, and dynamical biases. Despite their importance, these issues are frequently overlooked, often resulting in biased conclusions.

Quickstart

To learn more about epidist we recommend reading the vignettes in this order:

• Getting started with epidist
• Using epidist to estimate delay between symptom onset and positive test for an Ebola outbreak in Sierra Leone
• Approximate Bayesian inference in epidist

Installation

Installing the package

You can use the remotes package to install the development version from Github (warning! this version may contain breaking changes and/or bugs):

remotes::install_github(
  "epinowcast/epidist", dependencies = TRUE
)

Installing CmdStan

If you wish to do model fitting, you will need to install CmdStan, which also entails having a suitable C++ toolchain setup. We recommend using the cmdstanr package. The Stan team provides instructions in the Getting started with cmdstanr vignette, with other details and support at the package site, but the brief version is:

# if you not yet installed `epidist`, or you installed it without `Suggests` dependencies
install.packages("cmdstanr", repos = c("https://mc-stan.org/r-packages/", getOption("repos")))
# once `cmdstanr` is installed:
cmdstanr::install_cmdstan()

Note: You can speed up CmdStan installation using the cores argument. If you are installing a particular version of epidist, you may also need to install a past version of CmdStan, which you can do with the version argument.

Resources

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 do not use epidist.

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.

Citation

If you use epidist in your work, please consider citing it using citation("epidist").

Package citation information

citation("epidist")
To cite package 'epidist' in publications use:

  Adam Howes, Park S, Sam Abbott (NULL). _epidist: Estimate
  epidemiological delay distributions for infectious diseases_.
  doi:10.5281/zenodo.5637165 <https://doi.org/10.5281/zenodo.5637165>.

A BibTeX entry for LaTeX users is

  @Manual{,
    title = {epidist: Estimate epidemiological delay distributions for infectious diseases},
    author = {{Adam Howes} and Sang Woo Park and {Sam Abbott}},
    year = {NULL},
    doi = {10.5281/zenodo.5637165},
  }

If using our methodology, or the methodology on which ours is based, please cite the relevant papers. This may include:

• Estimating epidemiological delay distributions for infectious diseases by Park et al. (2024)
• Best practices for estimating and reporting epidemiological delay distributions of infectious diseases using public health surveillance and healthcare data by Charniga et al. (2024)

Contributors

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

Code

seabbs, athowes, parksw3, damonbayer, medewitt

Issue Authors

kgostic, TimTaylor, jamesmbaazam

Issue Contributors

pearsonca, sbfnk, SamuelBrand1, zsusswein