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Time Series Models Based on Growth Curves with Applications to Forecasting Coronavirus

Published onAug 25, 2020
Time Series Models Based on Growth Curves with Applications to Forecasting Coronavirus
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You're viewing an older Release (#1) of this Pub.

  • This Release (#1) was created on Jul 09, 2020 ()
  • The latest Release (#2) was created on Aug 25, 2020 ().

Abstract

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 focused 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.

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7/9/20: To preview this content, click below for the Just Accepted version of the article. This peer-reviewed version has been accepted for its content and is currently being copyedited to conform with HDSR’s style and formatting requirements.


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