In this paper, we present a model to predict the spread of the Covid-19 epidemic andapply it to the specific case of Italy. We started from a simple Susceptible, Infected, Recovered (SIR) model and we added the condition that, after a certain time, thebasic reproduction numberR0exponentially decays in time, as empirically suggestedby world data. Using this model, we were able to reproduce the real behavior of theepidemic within a confidence interval of 5 \%. Moreover, we illustrate possible futurescenarios, associated to different intervals ofR0. This model has been used since thebeginning of March 2020, predicting the Italian peak of the epidemic in April 2020with about 100.000 detected active cases. The real peak of the epidemic happened onthe 20th of April 2020, with 108.000 active cases. This result shows that the modelhas precise predictive power. \\%DIF > . Moreover, we illustrate possible future scenarios, associated to different intervals of R_0. This model has been used since the beginning of March 2020, predicting the Italian peak of the epidemic in April 2020 with about 100.000 detected active cases. The real peak of the epidemic happened on the 20th of April 2020, with 108.000 active cases. This result shows that the model had predictive power for the italian case.
In this paper, we present a model to predict the spread of the Covid-19 epidemic and apply it to the specific case of Italy. We started from a simple Susceptible, Infected, Recovered (SIR) model and we added the condition that, after a certain time, the basic reproduction number R_0 exponentially decays in time, as empirically suggested by world data. Using this model, we were able to reproduce the real behavior of the epidemic with an average error of 5 \\%DIF > . Moreover, we illustrate possible future scenarios, associated to different intervals of R_0. This model has been used since the beginning of March 2020, predicting the Italian peak of the epidemic in April 2020 with about 100.000 detected active cases. The real peak of the epidemic happened on the 20th of April 2020, with 108.000 active cases. This result shows that the model had predictive power for the italian case.
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\%. Moreover, we illustrate possible futurescenarios, associated to different intervals ofR0. This model has been used since thebeginning of March 2020, predicting the Italian peak of the epidemic in April 2020with about 100.000 detected active cases. The real peak of the epidemic happened onthe 20th of April 2020, with 108.000 active cases. This result shows that the modelhas precise predictive power.