Between Geography and Demography: Key Interdependencies and Exit Mechanisms for Covid-19

Antonio Scala; Andrea Flori; Alessandro Spelta; Emanuele Brugnoli; Matteo Cinelli; Walter Quattrociocchi; Fabio Pammolli

To have an idea of the variability of our parameters [20, 22, 23] , we both estimate the variability through a bootstrap procedure and through the sensibility to the variations of ρ. While the variations of ρ is the factor that mostly shifts the parameter, for our policy-modelling scopes we find a good stability of the fitted parameters even varying ρ in a range [10% . . . 100%]. Namely, we find β = 0.35±0.01 day −1 , γ = h = 10 −1 day −1 , R 0 = 3.5±0.1 day −1 , α = 0.49±0.01 for t Lock = 15 and t 0 = −30 ± 5 days. Being t Lock the time of the lockdown implementation and t 0 the starting time of the epidemic (i 0 = i(t 0 ) = 1). In our analysis we set ρ = 40%, R 0 = 3.5 day −1 and α = 0.49. Notice that the attenuation α in the transmission parameter measures the reduction of the contact rate after the lockdown.