Understanding COVID-19 nonlinear multi-scale dynamic spreading in Italy

Abstract

The outbreak of COVID-19 in Italy took place in Lombardia, a densely populated and highly industrialized northern region, and spread across the northern and central part of Italy according to quite different temporal and spatial patterns. In this work, a multi-scale territorial analysis of the pandemic is carried out using various models and data-driven approaches. Specifically, a logistic regression is employed to capture the evolution of the total positive cases in each region and throughout Italy, and an enhanced version of an SIR-type model is tuned to fit the different territorial epidemic dynamics via a differential evolution algorithm. Hierarchical clustering and multidimensional analysis are further exploited to reveal the similarities/dissimilaritiesof the remarkably different geographical epidemic developments. The combination of parametric identifications and multi-scale data-driven analyses paves the way towards a closer understanding of the nonlinear, spatially nonuniform epidemic spreading in Italy.

The first wave of the SARS-CoV-2 epidemic in Tuscany (Italy): A SI2R2D compartmental model with uncertainty evaluation

“…With the aim of taking into account the undetected infected, our model includes more compartments than a standard SIRD. In the same spirit of our work, other authors proposed compartmental models allowing for the presence of undetected asymptomatic individuals in the population [9][10][11][12][13][14][15]. Calafiore and colleagues [12] resort to a standard SIRD model and take into account the undetected infected individuals assuming that they represent a fixed fraction of the notified ones, as we do.…”

“…In order to express a greater belief in the deaths counts, rather than in the observed notified infections, which are more prone to registration errors, false positives (albeit probably negligible) or delays, we a priori set w 1 = 0.6 and w 2 = 0.4 [11].…”

“…The interested reader can find an accurate description of the literature in [8]. Some of the proposed compartmental models explicitly account for the presence of undetected asymptomatic individuals, thus for the fact that part of the epidemic is submerged [9][10][11][12][13][14][15].…”

Managing awareness can avoid hysteresis in disease spread: an application to coronavirus Covid-19

“…susceptibles, exposed, symptomatic infectives, asymptomatic infectives, susceptibles in quarantine, exposed in quarantine, hospitalized, recovered) has been introduced to estimate the risk of transmission of the novel coronavirus [3] , [4] and to evaluate the effectiveness, even in the long period, of control measures against epidemic [5] , [6] . The role played by asymptomatic individuals was widely stressed, recognizing how a prompt isolation of asymptomatic infectives would change the dynamics of Covid19 spread [7] , [8] , [9] . In particular, the impact of not officially documented cases on the spread of the novel Coronavirus epidemic was recognized to be enormous since, without the transmission due to the undocumented cases, the number of confirmed infections would have been far less in the whole of China with an estimated decrease of about 80 % [10] .…”

Analysis of COVID-19 based on SEIR epidemic models in a multi-patch environment

“…Saha et al [12] proposed an SEIRS epidemic model to explore coronavirus infection and suggested that susceptible individuals can avoid infection by taking appropriate precautions. Quaranta et al [13] performed a multi-scale dynamic analysis of COVID-19 outbreak in Italy to show changes at different geographic scales. Leung et al [14] established a susceptible-exposedinfectious-recovered (SEIR) model and the basic reproduction was estimated.…”