wwinference: joint inference and forecasting
from wastewater and epidemiological count data

[!CAUTION] This package is still in development. Note the package is still flagged as in development, though the authors plan on using it for production work in the coming weeks. 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.

Overview

This project is an in-development R package, {wwinference} that estimates latent incident infections from wastewater concentration data and data on epidemiological count data, with an initial assumed structure that the wastewater concentration data comes from subsets of the population contributing to the "global" epidemiological count 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 a model for the observed viral RNA concentration in wastewater, adding hierarchical structure to link the subpopulations represented by the observed wastewater concentrations in each wastewater catchment area.

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.

Project Admins

• Kaitlyn Johnson (kaitejohnson)
• Dylan Morris (dylanhmorris)
• George Vega Yon (gvegayon)
• Sam Abbott (seabbs)
• Damon Bayer (damonbayer)

Package workflow

The following depicts the suggested workflow for fitting the wastewater-informed forecasting model. See the "Getting Started" vignette for a full example.

Installing and running code

Install R

To run our code, you will need a working installation of R (version 4.1.0 or later). You can find instructions for installing R on the official R project website.

Install cmdstanr and CmdStan

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.

R 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:

R 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: R 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.

Install wwinference

Once cmdstanr and CmdStan are installed, the next step is to install the package, wwinference. The package provides tools for specifying and running the model, and installs other needed dependencies. The package can be installed directly from github by running the following within an R session: R install.packages("remotes") remotes::install_github("CDCgov/ww-inference-model") Confirm that package installation has succeeded by running the following within an R session:

R library(wwinference)

Contributing to this package

We welcome and encourage contributions. Open an issue in the repository to request changes. To contribute, fork the repository locally and open a pull request into the main branch.

Public Domain Standard Notice

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.

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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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The source code forked from other open source projects will inherit its license.

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Additional Standard Notices

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