Covid-19: predictive mathematical formulae for the number of deaths during lockdown and possible scenarios for the post-lockdown period.
Proc Math Phys Eng Sci
The Royal Society
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Fokas, A. S., Dikaios, N., & Kastis, G. A. (2021). Covid-19: predictive mathematical formulae for the number of deaths during lockdown and possible scenarios for the post-lockdown period.. Proc Math Phys Eng Sci https://doi.org/10.1098/rspa.2020.0745
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.
Inverse Problems, Integrable Systems, Riccati Equation, Covid-19, Mathematical Modelling Of Epidemics
External DOI: https://doi.org/10.1098/rspa.2020.0745
This record's URL: https://www.repository.cam.ac.uk/handle/1810/335212
Attribution 4.0 International
Licence URL: https://creativecommons.org/licenses/by/4.0/