ArticleProceedings. Mathematical, physical, and engineering sciences2021
Covid-19: predictive mathematical formulae for the number of deaths during lockdown and possible scenarios for the post-lockdown period.
Article in Proceedings. Mathematical, physical, and engineering sciences, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed.
- An algebraic formula, deep learning and a novel SEIR-type model for the COVID-19 pandemic.Royal Society open science · 2023Article
- Article
- A model for the spread of infectious diseases compatible with case data.Proceedings. Mathematical, physical, and engineering sciences · 2021Article
- SARS-CoV-2: The Second Wave in Europe.Journal of medical Internet research · 2021Article
- A quantitative framework for exploring exit strategies from the COVID-19 lockdown.Chaos, solitons, and fractals · 2020Article
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3 authors.
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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.
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