A data-driven model of the COVID-19 spread among interconnected populations: epidemiological and mobility aspects following the lockdown in Italy

Abstract

An epidemic multi-group model formed by interconnected SEIR-like structures is formulated and used for data fitting to gain insight into the COVID-19 dynamics and into the role of non-pharmaceutical control actions implemented to limit the infection spread since its outbreak in Italy. The single submodels provide a rather accurate description of the COVID-19 evolution in each subpopulation by an extended SEIR model including the class of asymptomatic infectives, which is recognized as a determinant for disease diffusion. The multi-group structure is specifically designed to investigate the effects of the inter-regional mobility restored at the end of the first strong lockdown in Italy (June 3, 2020). In its time-invariant version, the model is shown to enjoy some analytical stability properties which provide significant insights on the efficacy of the implemented control measurements. In order to highlight the impact of human mobility on the disease evolution in Italy between the first and second wave onset, the model is applied to fit real epidemiological data of three geographical macro-areas in the period March–October 2020, including the mass departure for summer holidays. The simulation results are in good agreement with the data, so that the model can represent a useful tool for predicting the effects of the combination of containment measures in triggering future pandemic scenarios. Particularly, the simulation shows that, although the unrestricted mobility alone appears to be insufficient to trigger the second wave, the human transfers were crucial to make uniform the spatial distribution of the infection throughout the country and, combined with the restart of the production, trade, and education activities, determined a time advance of the contagion increase since September 2020.

Graph‐theoretic approach to finite‐time synchronization and topology identification of delayed multi‐group models with stochastic perturbation and multiple dispersal

“…In recent years, multi-group models have been extensively researched owing to widespread applications in various fields such as ecology and epidemiology [1][2][3][4]. For example, in an ecological environment, the habitats of many biological species can be divided into several patches due to the spatial heterogeneity and the increasing spread of human activities [5].…”

An Analytical Framework for Understanding Infection Progression Under Social Mitigation Measures

“…Progress has been made in various directions, e.g. by taking into account the effects of quarantine [1,2], temporary immunity [3], recurrent outbreaks [4], vaccination [5][6][7][8], different levels of population susceptibility [9], comorbidities [8,10], stratification by age [11][12][13], competitive virus strains of different severity and/or transmissibility [14,15], spatial diffusion [16], human mobility between different regions [17][18][19], availability of testing kits [20], hospital infrastructure [21] and media coverage [22,23] to name a few. In particular, the attempts to combat the disease led to the introduction of social distancing measures on the scales hardly imaginable before [24].…”

Generic approach for mathematical model of multi-strain pandemics

“…Their model assumes a fixed number of locations (using a graph-based model) such that each location has a unique strain-like simulation. Furthermore, Di Giamberardino et al [ 44 ] proposed a multi-group model formed by interconnected SEIR-like structures which include asymptomatic infected individuals. The authors fitted the data to the COVID-19 pandemic in Italy to study the influence of different IPs on the pandemic spread.…”