Warning! This package is a prototype and is under active development. Breaking changes are likely.
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.
To learn more about epidist we recommend reading the vignettes in this
order:
- Getting started with
epidist - Using
epidistto estimate delay between symptom onset and positive test for an Ebola outbreak in Sierra Leone - Approximate Bayesian inference in
epidist
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(
file.path("epinowcast", "epidist"),
dependencies = TRUE
)Installing CmdStan (optional)
By default epidist uses the rstan package for fitting models. If you
wish to use the cmdstanr package instead, you will need to install
CmdStan, which also
entails having a suitable C++ toolchain setup. We recommend using the
cmdstanr package to manage CmdStan.
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 have 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.
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.
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.
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 With brms_.
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 With brms},
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)
All contributions to this project are gratefully acknowledged using the
allcontributors
package following the
all-contributors specification.
Contributions of any kind are welcome!
seabbs, athowes, parksw3, damonbayer, medewitt