A phenomenological algorithm for short-range predictions of the Covid-19 pandemics 2020
Chrusciel, P. T.; Szybka, S. J.
Show abstract
We present an algorithm for dynamical fitting of a logistic curve to the Covid-19 epidemics data, with fit-parameters linearly evolving to the future. We show that the algorithm would have given reasonable short- and medium-range predictions for the mid-range evolution of the epidemics for several countries. We introduce the double-logistic curve, which provides a very good description of the epidemics data at any given time of the epidemics. We analyse the predictability properties of some naive models.
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