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. 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 data.

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("Craig-PT/tsgc")

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)

• Dynamic Gompertz Model: Implements time series growth curve methods based on a dynamic Gompertz model.
• Flexible Applications: While focused on epidemics, tsgc is also applicable in other areas, such as marketing.
• Reinitialization Support: Offers functionalities for reinitializing data series and model to account for multiple waves in an epidemic.
• Comprehensive Documentation: Includes detailed examples and vignettes to guide users through forecasting exercises.

This package requires R (version 3.5.0 or higher) and depends on several other R packages for handling state space models and time series data, including KFAS, xts, zoo, and here.

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.