Forecasting epidemics: the Time Series Growth Curves (tsgc) package

What is tsgc?

Time Series Growth Curves (tsgc) package for R is designed for forecasting epidemics, including the detection of new waves and turning points. The package implements time series growth curve methods founded on a dynamic Gompertz model and can be estimated using techniques based on state space models and the Kalman filter. The model is suitable for predicting future values of any variable which, when cumulated, is subject to some unknown saturation level. In the context of epidemics, the model can adjust to changes in social behaviour and policy. It is also relevant for many other domains, such as the diffusion of new products.


The tsgc package is described in:

(Submission to The Journal of Statistical Software is in process.)


To install the latest version of the ‘tsgc’ package from GitHub:


(Submission to CRAN is in process.)


Time series growth curve methods applied in the package are described in:


We are grateful to the Cambridge Centre for Health Leadership and Enterprise, Cambridge Judge Business School, Public Health England/UK Health Security Agency and Cambridge Keynes Fund for support. We would like to thank all contributors to the development and documentation of the ‘tsgc’ package, and particularly, Thilo Klein and Stefan Scholtes for constructive comments.