Mark Levene is Professor of Computer Science in Birkbeck’s Department of Computer Science and Information Systems. He shares insights from research into modelling the waves of an epidemic.
Epidemics such as COVID-19 come in “waves”, although the precise definition of a wave in this context is somewhat elusive. A standard way to model the epidemic is as a time series that records, say the number of daily hospitalisation or deaths, and these can be plotted to view the progress of the epidemic.
Waves in the time series span from one valley to another with a peak in between them. The shape of an individual wave can be modelled as a statistical distribution and several waves can be sequentially combined. More often than not waves are not symmetric, that is, the rate at which, say hospitalisations, increase is not the same rate at which they decrease once the peak of the wave has been reached. This non-symmetrical nature of a wave is called its skewness.
To take into account the skewness of epidemic waves we introduce the skew logistic distribution, which is a novel yet simple extension of the symmetric logistic distribution widely used in the modelling of epidemic data.
To validate our model, we provide a full analysis of the first four waves of COVID-19 deaths in the UK from the 30 January 2020 to 30 July 2021.
Our results show a good fit to the proposed skew logistic distribution, and thus could potentially augment existing more established models that are being used to forecast the trajectory of an epidemic.
Our findings have been published in MDPI Entropy.
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