The tsgc package is designed for forecasting epidemics, including the detection of new waves and turning points, using a dynamic Gompertz model. It is suitable for predicting future values of variables that, when cumulated, are subject to some unknown saturation level. A leading-indicator extension allows a related series that moves ahead of the variable of interest, such as cases anticipating hospitalisations, to improve short-horizon forecasts, with model and lag selection supported by rolling-origin cross-validation. The underlying state space framework applies equally to data observed at daily, weekly, monthly, quarterly, or annual frequency. This approach is not only applicable to epidemics but also to domains like the diffusion of new products, thanks to its flexibility in adapting to changes in social behavior and policy. The tsgc package is demonstrated using COVID-19 confirmed cases and hospitalisation data, as well as non-epidemic applications.
To install the latest version of the tsgc package from GitHub, use the following R command:
# Install from GitHub
install.packages("devtools")
library(devtools)
# devtools::install_github("edwintang903/tsgc")
install.packages("tsgc", repos = c('https://edwintang903.r-universe.dev', 'https://cloud.r-project.org'))
or install from the locally downloaded package as:
devtools::install()
Here is a basic example of setting up and estimating a model with the tsgc package:
library(tsgc)
# Load example data
data("gauteng", package = "tsgc")
# Initialize and estimate the model
model <- SSModelDynamicGompertz$new(Y = gauteng)
results <- model$estimate()
# View results
print(results)
A leading-indicator model can be set up in much the same way, using a second series that moves ahead of the variable of interest:
library(tsgc)
# Load example data: daily cases and hospitalisations for England
data("england", package = "tsgc")
# Initialize and estimate the model, with cases as a 4-day leading
# indicator for hospitalisations
model <- SSModelLeadingIndicator$new(Y = england[, 1:2], n.lag = 4)
results <- model$estimate()
# View results
print(results)
cross_val()) supports comparing candidate models and selecting the leading-indicator lag empirically.estimate_r0()).tsgc is also applicable in other areas, such as marketing, product diffusion, and other domains with a growth-curve-like trajectory.This package requires R (version 3.5.0 or higher) and depends on several other R packages for handling state space models, time series data, and visualization, including KFAS, xts, zoo, and ggplot2.
For detailed documentation and examples, refer to the package's vignettes. Should you encounter any issues or have questions, please file them in the GitHub Issues section of the tsgc repository.
Contributions to tsgc are welcome, including bug reports, feature requests, and pull requests. Please see the GitHub repository for contribution guidelines.
This package is released under the GNU General Public License v3.0.
If you use the tsgc package in your research, please cite it as follows:
Ashby, M., Harvey, A., Kattuman, P., & Thamotheram, C. (2021). Forecasting epidemic trajectories: Time Series Growth Curves package tsgc. Cambridge Centre for Health Leadership & Enterprise. URL: [https://www.jbs.cam.ac.uk/wp-content/uploads/2024/03/cchle-tsgc-paper-2024.pdf]
Our gratitude goes to the Cambridge Centre for Health Leadership & Enterprise, University of Cambridge Judge Business School, and Public Health England/UK Health Security Agency for their support. Special thanks to Thilo Klein and Stefan Scholtes for their constructive comments, and to all contributors to the development and documentation of the tsgc package.