RtForecastR

A lightweight R package for real-time effective reproduction number (R_t) estimation and one-step-ahead forecasting from case-count time series. Disease-agnostic: works for any pathogen with a known generation interval.

Installation

install.packages("RtForecastR")

The development version can be installed from GitHub:

# install.packages("remotes")
# remotes::install_github("rajsubediresearch/RtForecastR")
install.packages("RtForecastR", repos = c('https://rajsubediresearch.r-universe.dev', 'https://cloud.r-project.org'))

Usage

library(RtForecastR)

data(measles_cdmx)
fit <- rt_forecast(measles_cdmx$time, measles_cdmx$cases,
                    mean_GI = 11/7, var_GI = (4/7)^2)
fit
plot(fit, which = "Rt")
plot(fit, which = "forecast")

See vignette("rtforecastr-walkthrough") for the full walkthrough, and ?rt_forecast, ?wis, ?score_batches for calibration/forecast scoring (mean absolute error, mean squared error, root mean squared error, empirical coverage, weighted interval score).

What it does

• Filtered (real-time/causal) and smoothed (retrospective) R_t estimates, each with 95% credible intervals
• One-step-ahead predicted case counts (in-sample), for checking model adequacy
• A genuine out-of-sample forecast one time-step beyond your most recent data point, with 50%/95% predictive intervals
• Elimination probability, P(R_t < 1), at every time point
• Forecast calibration metrics: mean absolute error, mean squared error, root mean squared error, empirical coverage, and the weighted interval score (WIS)

Attribution

This package builds directly on existing, published methods. Please cite accordingly if you use it:

Core R_t estimation algorithm - R/epiFilter.R, R/epiSmoother.R, and the base logic in R/recursPredict.R are ports of the EpiFilter algorithm:

Parag KV. (2021) "Improved estimation of time-varying reproduction numbers at low case incidence and between epidemic waves." PLOS Computational Biology 17(9): e1009347. https://github.com/kpzoo/EpiFilter

Generation-interval discretization - compute_lambda() follows the convention used in get_Rt.m, part of the GrowthPredict toolbox:

Chowell G, Bleichrodt A, Dahal S, Tariq A, Roosa K, Hyman JM, Luo R. (2024) "GrowthPredict: A toolbox and tutorial-based primer for fitting and forecasting growth trajectories using phenomenological growth models." Scientific Reports 14, 1630. https://github.com/gchowell/forecasting_growthmodels

Weighted interval score - wis() implements:

Bracher J, Ray EL, Gneiting T, Reich NG. (2021) "Evaluating epidemic forecasts in an interval format." PLOS Computational Biology 17(2): e1008618.

Everything else - the R package interface, the configurable prediction grid, the out-of-sample forecast extension, the multi-level quantile prediction (for WIS), and the calibration/scoring functions - is original work by Raj Subedi, built on top of the above.

History

This package supersedes an earlier script-based version of the same tool (run_rtforecast.R + core/*.R). That version's archived weekly output is preserved on the pre-package-v0 tag for reference.

License

GPL-3. Copyright holders and their specific contributions are declared in Authors@R (see DESCRIPTION) - Kris V. Parag holds copyright on the unmodified/lightly modified EpiFilter files (R/epiFilter.R, R/epiSmoother.R, R/recursPredict.R's original logic); Raj Subedi holds copyright on everything else.