10.17863/CAM.57179
Harvey, A
Kattuman, P
Time series models based on growth curves with applications to forecasting coronavirus
MIT Press
2020
Apollo - University of Cambridge Repository
University of Cambridge
013meh722
2020-09-09
2020-09-09
2020-08
eng
Article
https://www.repository.cam.ac.uk/handle/1810/310093
10.1162/99608f92.828f40de
All rights reserved
open.access
Time series models are developed for predicting future values of a variable which when cumulated is subject to an unknown saturation level. Such models are relevant for many disciplines, but here attention is focussed on the spread of epidemics and the applications are for coronavirus. The time series models are relatively simple but are such that their specification can be assessed by standard statistical test procedures. In the generalized logistic class of models, the logarithm of the growth rate of the cumulative series depends on a time trend. Allowing this trend to be time-varying introduces further flexibility and enables the effects of changes in policy to be tracked and evaluated.