Abstract
Time series models are developed for predicting future values of a variable that 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.
Highlights
The progress of an epidemic typically starts off with the number of cases following an exponential growth path
Time series models are developed for predicting future values of a variable that when cumulated is subject to an unknown saturation level
“phenomenological.” Similar issues arise in economics where there is a contrast between calibrated and Bayesian models based on economic theory and data-based time series models
Summary
The progress of an epidemic typically starts off with the number of cases following an exponential growth path. Over time the growth rate falls and the total number of cases approaches a final level—. Syntax Error (1495239): Dictionary key must be a name object parent time series models may offer an alternative way of making predictions of the trajectory of the epidemic; see, for example, section 2 in Chowell et al (2016), where such approaches are called “phenomenological.” Similar issues arise in economics where there is a contrast between calibrated and Bayesian models based on economic theory and data-based time series models. The deterministic time trend in the estimating equations can be replaced by a stochastic one This is effective with a Gompertz function.
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