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Covid-19: predictive mathematical formulae for the number of deaths during lockdown and possible scenarios for the post-lockdown period.

Athanassios S FokasNikolaos DikaiosGeorge A Kastis
Published in: Proceedings. Mathematical, physical, and engineering sciences (2021)
In a recent article, we introduced two novel mathematical expressions and a deep learning algorithm for characterizing the dynamics of the number of reported infected cases with SARS-CoV-2. Here, we show that such formulae can also be used for determining the time evolution of the associated number of deaths: for the epidemics in Spain, Germany, Italy and the UK, the parameters defining these formulae were computed using data up to 1 May 2020, a period of lockdown for these countries; then, the predictions of the formulae were compared with the data for the following 122 days, namely until 1 September. These comparisons, in addition to demonstrating the remarkable predictive capacity of our simple formulae, also show that for a rather long time the easing of the lockdown measures did not affect the number of deaths. The importance of these results regarding predictions of the number of Covid-19 deaths during the post-lockdown period is discussed.
Keyphrases
  • sars cov
  • deep learning
  • coronavirus disease
  • respiratory syndrome coronavirus
  • electronic health record
  • magnetic resonance imaging
  • magnetic resonance
  • convolutional neural network
  • neural network