Modeling Growth, Containment and Decay of the COVID-19 Epidemic in Italy

Abstract

A careful inspection of the cumulative curve of confirmed COVID-19 infections in Italy and in other hard-hit countries reveals three distinct phases: i) an initial exponential growth (unconstrained phase), ii) an algebraic, power-law growth (containment phase), and iii) a relatively slow decay. We propose a parsimonious compartment model based on a time-dependent rate of depletion of the susceptible population that captures all such phases for a plausible range of model parameters. The results suggest an intimate interplay between the growth behavior, the timing and implementation of containment strategies, and the subsequent saturation of the outbreak.

Gaussian Parameters Correlate with the Spread of COVID-19 Pandemic: The Italian Case

“…On the other hand, complex and innovative strategies, making use of machine learning or regression, have been implemented with the aim to project SARS-CoV-2 cases into the future [15,16]. Mechanistic models based on differential equations, such as the well-known SIR model (Susceptible-Infectious-Recovered) and its numerous modifications, are the most used to mimic and predict the spread of COVID-19 by including specific assumptions on the chosen parameters which control transmission, disease, testing capacity and immunity [17][18][19][20]. These models, on the contrary of crude statistical approaches, are able to consider nonlinear dependences such as the increasing diffusion speed with the number of infected people.…”