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

Author:

Fokas Athanassios S.123ORCID,Dikaios Nikolaos24ORCID,Kastis George A.2ORCID

Affiliation:

1. Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, UK

2. Mathematics Research Center, Academy of Athens, Athens, Greece

3. Department of Biomedical Engineering, University of Southern California, Los Angeles, CA, USA

4. Centre for Vision, Speech and Signal Processing, University of Surrey, Guildford, UK

Abstract

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.

Publisher

The Royal Society

Subject

General Physics and Astronomy,General Engineering,General Mathematics

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