This is an experimental rewrite of EpiNow2 that replaces all internal Stan models with calls to the epinowcast package. It is not yet released and is under active development. See the feature status below.
{epinow2cast} provides the familiar EpiNow2 interface
(estimate_infections(), epinow(), regional_epinow()) but delegates
all Bayesian inference to {epinowcast}. This eliminates ~9,000 lines
of Stan code from EpiNow2 and unifies the two packages under a single
computational backend.
| Feature | Status | Notes |
|---|---|---|
estimate_infections() |
Working | Renewal equation via epinowcast |
estimate_truncation() |
Working | Requires epinowcast PR #756 |
simulate_infections() |
Working | Pure R implementation |
epinow() |
Working | |
regional_epinow() |
Working | |
get_samples() |
Working | R, infections, growth_rate, reported_cases |
get_predictions() |
Working | summary, sample, quantile formats |
get_parameters() |
Working | Returns generation time and delays |
summary() / plot() |
Working | |
| Random walk on Rt | Working | rt_opts(rw = 7) for weekly |
| Day-of-week effects | Working | obs_opts(week_effect = TRUE) |
| Forecasting | Working | forecast_opts(horizon = N) |
| Poisson / NegBin | Working | obs_opts(family = ...) |
| Reporting delays | Working | Via latent_reporting_delay |
| Gaussian process | Removed | Use rt_opts(rw = ...) instead |
| Back-calculation | Removed | |
| Population depletion | Removed | |
estimate_secondary() |
Removed | Use epinowcast directly |
forecast_infections() |
Removed | Use forecast_opts() in estimate_infections() |
| rstan backend | Removed | cmdstanr only (via epinowcast) |
This package is not on CRAN. Install from this repository:
# install.packages("pak")
pak::pkg_install("sbfnk/epinow2cast@epinowcast-backend")Requires cmdstanr and a working
CmdStan installation. See the cmdstanr
documentation for
setup instructions.
Getting Started
The Getting Started vignette (see vignette("EpiNow2")) is your
quickest entry point to the package. It provides a quick run through of
the two main functions in the package and how to set up them up. It also
discusses how to summarise and visualise the results after running the
models.
More broadly, users can also learn the details of estimating delay distributions, nowcasting, and forecasting in a structured way through the free and open short-course, “Nowcasting and forecasting infectious disease dynamics”, developed by some authors of this package.
Package website
The package has two websites: one for the stable release version on CRAN, and another for the version in development. These two provide various resources for learning about the package, including the function reference, details about each model (model definition), workflows for each model (usage), and case studies or literature of applications of the package. However, the development website may contain experimental features and information not yet available in the stable release.
End-to-end workflows
The workflow vignette (see vignette("estimate_infections_workflow"))
provides guidance on the end-to-end process of estimating reproduction
numbers and performing short-term forecasts for a disease spreading in a
Model definitions
In different vignettes we provide the mathematical definition of each
model. For example, the model definition vignette for
estimate_infections() can be found in
vignette("estimate_infections").
Example implementations
A simple example of using the package to estimate a national Rt for Covid-19 can be found here.
We welcome all contributions. If you have identified an issue with the package, you can file an issue here. We also welcome additions and extensions to the underlying model either in the form of options or improvements. If you wish to contribute in any form, please follow the package contributing guide.
All contributions to this project are gratefully acknowledged using the
allcontributors package
following the allcontributors
specification. Contributions of any kind are welcome!
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