Caution
This project is a work-in-progress. It meant to be used to develop spatial components of the wastewater informed forecasting model. Despite this project's early stage, all development is in public as part of the Center for Forecasting and Outbreak Analytics' goals around open development. Questions and suggestions are welcome through GitHub issues or a PR.
This project is an in-development R package, {wwinference} that estimates latent incident infections from wastewater concentration data and data on epidemiological indicators, with an initial assumed structure that the wastewater concentration data comes from subsets of the population contributing to the "global" epidemiological indicator data, such as hospital admissions.
In brief, our model builds upon EpiNow2, a widely used R and Stan package for Bayesian epidemiological inference.
We modify EpiNow2 to add model for the observed viral RNA concentration in wastewater, adding hierarchical structure to link the subpopulations represented by the osberved wastewater concentrations in each wastewater catchment area.
See our Model Definition page for a mathematical description of the generative model, and the Getting Stated vignette to see an example of how to run the inference model on simulated data.
The intention is for {wwinference} to provide a user-friendly R-package interface for running forecasting models that use wastewater concentrations combined with other more traditional epidemiological signals such as cases or hospital admissions. It aims to be a re-implementation of the modeling components contained in the wastewater-informed-covid-forecasting project repository, with an emphasis here on making it easier for users to supply their own data.
We recommend reading the model definition to learn more about how the model is structured and running the "Getting Started" vignette for an example of how to fit the model to simulated data of COVID-19 hospital admissions and wastewater concentrations. This will help make clear the data requirements and how to structure this data to fit the model.
- Kaitlyn Johnson (kaitejohnson)
- Dylan Morris (dylanhmorris)
- Sam Abbott (seabbs)
- Damon Bayer (damonbayer)
To run our code, you will need a working installation of R (version 4.3.0 or later). You can find instructions for installing R on the official R project website.
We do inference from our models using CmdStan (version 2.35.0 or later) via its R interface cmdstanr (version 0.8.0 or later).
Open an R session and run the following command to install cmdstanr per that package's official installation guide.
install.packages("cmdstanr", repos = c("https://mc-stan.org/r-packages/", getOption("repos")))cmdstanr provides tools for installing CmdStan itself. First check that everything is properly configured by running:
cmdstanr::check_cmdstan_toolchain()You should see the following:
The C++ toolchain required for CmdStan is setup properly!
If you do, you can then install CmdStan by running:
cmdstanr::install_cmdstan()If installation succeeds, you should see a message like the following:
CmdStan path set to: {a path on your file system}
If you run into trouble, consult the official cmdstanr website for further installation guides and help.
Once cmdstanr and CmdStan are installed, the next step is to download this repository and install the package, wwinference. The package provides tools for specifying and running the model, and installs other needed dependencies.
Once you have downloaded this repository, navigate to it within an R session and run the following:
install.packages('remotes')
remotes::install_local()Installing the project package should take care of almost all dependencies installations. Confirm that package installation has succeeded by running the following within an R session:
library(wwinference)This repository constitutes a work of the United States Government and is not subject to domestic copyright protection under 17 USC § 105. This repository is in the public domain within the United States, and copyright and related rights in the work worldwide are waived through the CC0 1.0 Universal public domain dedication. All contributions to this repository will be released under the CC0 dedication. By submitting a pull request you are agreeing to comply with this waiver of copyright interest.
The repository utilizes code licensed under the terms of the Apache Software License and therefore is licensed under ASL v2 or later.
This source code in this repository is free: you can redistribute it and/or modify it under the terms of the Apache Software License version 2, or (at your option) any later version.
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