A Note on the Evolution of Covid-19 in Italy

TL;DR

This paper applies physics-inspired dynamical systems methods, including logistic and Hubbert functions, to analyze and predict the evolution of Covid-19 infections in Italy, comparing different mathematical models for their predictive accuracy.

Contribution

It introduces the use of logistic and Hubbert functions, borrowed from physics, to model and predict Covid-19 infection patterns in Italy, offering a novel interdisciplinary approach.

Findings

Hubbert function provides effective daily infection predictions

Comparison shows different models have varying predictive capabilities

Mathematical tools from physics can be adapted for epidemiological analysis

Abstract

We employ methods largely exploited in Physics, in the analysis of the evolution of dynamical systems, to study the pattern of the Covid-19 infection in Italy. The techniques we employ are based on the use of logistic function and of its derivative, namely the Hubbert function. The latter is exploited to give a prediction on the number of infected per day. We also mention the possibility of taking advantage from other mathematical tools based e.g. on the Gompertz equation and make some comparison on the different predictive capabilities.