Time-window SIQR analysis of COVID-19 outbreak and containment measures in italy

Abstract

The COVID-19 disease caused by the coronavirus SARS-nCoV2 is currently a global public health threat and Italy is one of the countries mostly suffering from this epidemic. It is therefore important to analyze epidemic data, considering also that the government deployed laws limiting the societal activities. We model COVID-19 dynamics with a SIQR (susceptible - infectious - quarantined - recovered) model, where we take into account the temporal variability of its parameters. Particle Swarm Optimization is used to find out the best parameters in the case of Italy and of Italian regions where the epidemic has the greatest impact. The basic reproductive number is estimated by a novel approach that averages out different PSO fits computed considering different temporal time-windows and reducing possible noise in the data. The results on data collected from February 24 to April 24 show that our approach is able to fit the data with low errors and that the basic reproductive number is characterized by a descending trend in time from 3.5 to a value below 1.