RtGam: Estimating $R_t$ with Generalized Additive Models

Caution

This project is an early-stage work-in-progress. Any outputs may be misleading or even incorrect. 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.

Overview

This project is an in-development R package for $R_t$ estimation using generalized additive models (GAMs). Over the last few years, GAMs have been used with some success to estimate epidemic growth rates and nowcast cases (see Ward, 2021 and Mellor, 2023 for an overview of methods). GAM-based models enable rapid, adaptable prototyping, testing different model structures and data sources with minimal computational and development cost. The R package {mgcv} provides a flexible, fast, and robust interface to fit penalized-spline based models. However, the substantial research effort into spline-based approaches has not been unified into a shared R package.

This R package aims to become an opinionated re-implementation of the literature methods, with a focus on enabling hierarchical modeling. Development effort is optimized around real-time use-cases, with potential for right-truncation, noisy reporting, and uncertain data-generating processes. It is meant to be a simple drop-in tool to be run alongside more computationally intensive implementations like {EpiNow2}.

At the moment, the package has some simple functionality to fit an adaptive smooth trend to a single epidemic timeseries and produce a short forecast. It has not yet been benchmarked relative to other approaches.

Installation

The package can be installed from GitHub:

remotes::install_github("cdcgov/[email protected]")

Use

The package can be used to fit smooth trends of time to noisy epidemic data. It includes a simulated dataset from Gostic et al., 2020 "Practical considerations for measuring the effective reproductive number, $R_t$". This snippet uses the simulated dataset to demonstrate how to use the main model-fitting function:

library(RtGam)

fit <- RtGam(
        cases = stochastic_sir_rt[["obs_cases"]],
        # Randomly chosen date
        reference_date = stochastic_sir_rt[["reference_date"]]
       )
print(fit)

For more, see the Getting Started vignette.

Project Admin

• Zachary Susswein (@zsusswein)
• Katelyn Gostic (@kgostic)
• Sam Abbott (@seabbs)

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