Tools to enable flexible and efficient hierarchical nowcasting of right-truncated epidemiological time-series using a semi-mechanistic Bayesian model with support for a range of reporting and generative processes. Nowcasting, in this context, is gaining situational awareness using currently available observations and the reporting patterns of historical observations. This can be useful when tracking the spread of infectious disease in real-time: without nowcasting, changes in trends can be obfuscated by partial reporting or their detection may be delayed due to the use of simpler methods like truncation. While the package has been designed with epidemiological applications in mind, it could be applied to any set of right-truncated time-series count data.
Installing the package
Install the latest released version of the package with:
install.packages("epinowcast", repos = "https://epinowcast.r-universe.dev")Alternatively, you can use the remotes
package to install the development version
(warning: this version may contain breaking changes and/or bugs) from
GitHub using:
remotes::install_github("epinowcast/epinowcast", dependencies = TRUE)Similarly, you can install historical releases by adding the release tag
(e.g. this installs
0.2.0):
remotes::install_github(
"epinowcast/epinowcast", dependencies = TRUE, ref = "v0.2.0"
)Note: A similar method can be used to install a particular commit of the package which may be useful for some users who are unable to use a fixed release but concerned about the stability of their dependencies.
Installing CmdStan
If you wish to do model fitting and nowcasting, you will need to install
CmdStan. We recommend
using cmdstanr and the
cmdstanr::install_cmdstan() to do so, which needs a suitable C++
toolchain. Instructions are provided in the Getting started with
cmdstanr
vignette. See the cmdstanr
documentation for further details and
support.
cmdstanr::install_cmdstan()Note: This install process can be sped up using the cores argument
and past versions can be installed using the version argument (which
may be useful if install historical package releases).
Alternative: Docker
We also provide a [Docker](https://www.docker.com/get-started/) image with [`epinowcast` and all dependencies installed](https://github.com/orgs/epinowcast/packages/container/package/epinowcast). This image can be used to run `epinowcast` without installing dependencies locally.As you use the package, the documentation available via ?enw_ should
be your first stop for troubleshooting. We also provide a range of other
documentation, case studies, and spaces for the community to interact
with each other. Below is a short list of current resources.
- Package website: This includes a
function reference, model outline, and case studies using the package.
The package site covers the release version, which can be installed
from our Universe or from the latest GitHub release (see installation
instructions). Documentation for the development
version (corresponding to the
mainbranch on GitHub) is also available. - Package Vignettes: These provide tutorials and case studies, focused discussions of particular aspects, or demonstrate case studies. The Getting Started with Epinowcast: Nowcasting is a good place to start.
- Organisation website: This includes links to our other resources as well as guest posts from community members and schedules for any related seminars being run by community members.
- Directory of example scripts: Not as fleshed out as our complete case studies these scripts are used during package development and each showcase a subset of package functionality. Often newly introduced features will be explored here before surfacing in other areas of our documentation.
- Community forum: Our community
forum is where development of methods and tools is discussed, along
with related research from our members and discussions between users.
If you are interested in real-time analysis of infectious disease this
is likely a good place to start regardless of if you end up making use
of
epinowcast.
We welcome contributions and new contributors! We particularly appreciate help on priority problems in the issues. Please check and add to the issues, and/or add a pull request. See our contributing guide for more information.
If interested in expanding the functionality of the underlying model
note that epinowcast allows users to pass in their own models meaning
that alternative parameterisations, for example altering the forecast
model used for inferring expected observations, may be easily tested
within the package infrastructure. Once this testing has been done
alterations that increase the flexibility of the package model and
improves its defaults are very welcome via pull request or other
communication with the package authors. Even if not wanting to add your
updated model to the package please do reach out as we would love to
hear about your use case.
Please briefly describe your problem and what output you expect in an issue. If you have a question, please don’t open an issue. Instead, ask on our Q and A page. See our contributing guide for more information.
Please note that the epinowcast project is released with a
Contributor Code of
Conduct. By
contributing to this project, you agree to abide by its terms.
If you use epinowcast in your work, please consider citing it using
the following,
citation("epinowcast")To cite package ‘epinowcast’ in publications use:
Sam Abbott, Lison A, Funk S, Pearson C, Gruson H, Guenther F (NULL). epinowcast: Flexible Hierarchical Nowcasting. doi:10.5281/zenodo.5637165 https://doi.org/10.5281/zenodo.5637165.
A BibTeX entry for LaTeX users is
@Manual{, title = {epinowcast: Flexible Hierarchical Nowcasting}, author = {{Sam Abbott} and Adrian Lison and Sebastian Funk and Carl Pearson and Hugo Gruson and Felix Guenther}, year = {NULL}, doi = {10.5281/zenodo.5637165}, }
If making use of our methodology or the methodology on which ours is based, please cite the relevant papers from our model outline.