Ashby, M., Harvey, A., Kattuman, P., Thamotheram, C.
Forecasting epidemic trajectories: Time Series Growth Curves package tsgc
CWPE2407
Abstract: This paper documents the Time Series Growth Curves (tsgc) package for R, which 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 behavior and policy. It is also relevant for many other domains, such as the diffusion of new products. The tsgc package is demonstrated using data on COVID-19 confirmed cases.
Keywords: Covid-19, Gompertz growth curve, Kalman filter, reproduction number, state space model, stochastic trend, turning points
JEL Codes: C22 I10 C63
Author links: Michael Ashby Andrew Harvey
PDF: https://www.econ.cam.ac.uk/research-files/repec/cam/pdf/cwpe2407.pdf 
Open Access Link: https://doi.org/10.17863/CAM.107470
Computer Programme: https://github.com/Craig-PT/tsgc
