Forecasting epidemic trajectories: Time Series Growth Curves package tsgc

Michael Ashby, Andrew Harvey, Paul Kattuman and Craig Thamotheram

Cambridge Working Papers in Economics from Faculty of Economics, University of Cambridge

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 (search for similar items in EconPapers)
JEL-codes: C22 C63 I10 (search for similar items in EconPapers)
Date: 2024-02-12
New Economics Papers: this item is included in nep-for
Note: mwa22, ach34, pak13
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.econ.cam.ac.uk/sites/default/files/pub ... pe-pdfs/cwpe2407.pdf

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:cam:camdae:2407

Access Statistics for this paper

More papers in Cambridge Working Papers in Economics from Faculty of Economics, University of Cambridge
Bibliographic data for series maintained by Jake Dyer ().