Differential evolution to estimate the parameters of a SEIAR model with dynamic social distancing

Abstract This chapter describes the application of a recent compartment-based epidemiological model, namely the SEIAR (Susceptible-Exposed-Infectious-Asymptomatic-Recovered), to estimate the spreading of the coronavirus COVID-19 in Italy and in some of its regions. The model is here extended through the definition of a time-dependent dynamic social distancing (DSD) function, thus introducing the SEIAR–DSD model. To profitably use the SEIAR–DSD model, the most suitable values of its parameters must be found. This is performed through differential evolution, a heuristic optimization technique. This allows approximately evaluating, for each of the above-mentioned scenarios, the daily number of infectious individuals, the day(s) in which this number will reach its maximum, the corresponding value, and the future evolution of the spread.