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Analysis of the evolution of the Sars-Cov-2 in Italy, the role of the asymptomatics and the success of Logistic model

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  • Martelloni, Gabriele
  • Martelloni, Gianluca

Abstract

In this article we study the temporal evolution of the pandemic Sars-Cov-2 in Italy by means of dynamic population models. The time window of the available population data is between February 24, and March 25. After we upgrade the data until April 1. We perform the analysis with 4 different models and we think that the best candidate to correctly described the italian situation is a generalized Logistic equation. We use two coupled differential equations that model the evolution of the severe infected and the dead. This choice is due to the fact that in Italy the pharyngeal swabs are made only to severe infected, therefore we have no information about asymptomatic people. Moreover, an important observation is that the virus spreads between Regions with some delay. Indeed, we suggest that a different analysis, region by region, would be more sensible than one on the whole Italy. In particular the region Lombardy has a behaviour very fast compared to the other ones. We show the fit and forecast of the dead and total severe infected for Italy and five regions: Lombardy, Piedmont, Emilia-Romagna, Veneto and Tuscany. Finally we perform an analysis of the peak (intended, in our study, as the maximum of the daily total severe infected) and an estimation of how many lives have been saved by means of the LockDown.

Suggested Citation

  • Martelloni, Gabriele & Martelloni, Gianluca, 2020. "Analysis of the evolution of the Sars-Cov-2 in Italy, the role of the asymptomatics and the success of Logistic model," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    • Handle: RePEc:eee:chsofr:v:140:y:2020:i:c:s0960077920305464
    DOI: 10.1016/j.chaos.2020.110150
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    1. Martelloni, Gabriele & Martelloni, Gianluca, 2020. "Modelling the downhill of the Sars-Cov-2 in Italy and a universal forecast of the epidemic in the world," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
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    Cited by:

    1. Matvey Pavlyutin & Marina Samoyavcheva & Rasul Kochkarov & Ekaterina Pleshakova & Sergey Korchagin & Timur Gataullin & Petr Nikitin & Mohiniso Hidirova, 2022. "COVID-19 Spread Forecasting, Mathematical Methods vs. Machine Learning, Moscow Case," Mathematics, MDPI, vol. 10(2), pages 1-19, January.

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