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.
Highlights
The “coronavirus disease 2019” (COVID-19), caused by SARS-CoV-2, posed novel challenges to all world countries, often evidencing their vulnerability in the practical management of emergency states, concerning the health effects, and the implications for economic growth and social development.Understanding and quantifying the dominant variables that govern the current pandemic evolution, especially in order to limit the impact of future outbreaks, imposes the need of framing the determinants of the epidemic dynamics [48]
Among the growing number of literature papers on the topic, we focus on the quantitative studies concerning the effects of limiting social distancing and human mobility
Some variants of the original SIR/SEIR framework, modeling peculiar aspects of epidemic transmission dynamics, have been proposed in the past to generate insights into the evolution of specific infectious diseases and assess the potential impact of different intervention strategies; so, these studies can usefully support the research on the present COVID-19 pandemic
Summary
The “coronavirus disease 2019” (COVID-19), caused by SARS-CoV-2, posed novel challenges to all world countries, often evidencing their vulnerability in the practical management of emergency states, concerning the health effects, and the implications for economic growth and social development. Among the most recent (and huge) literature on the COVID-19 modeling, we mention some papers proposing interesting variants of the classical SIR model and using different approaches: the age-structured SIR model [36], the stochastic, discrete, age-structured compartmental model [1] in which distributed time delays related to the periods of incubation, infection, and quarantine are considered as in [35] Another aspect common to these latter works that matters in relation to our study is that they analyze the first epidemic period (until October 2020) in order to highlight how different strategies implemented in that peculiar time interval can affect the future epidemic trajectory. Some variants of the original SIR/SEIR framework, modeling peculiar aspects of epidemic transmission dynamics, have been proposed in the past to generate insights into the evolution of specific infectious diseases and assess the potential impact of different intervention strategies; so, these studies can usefully support the research on the present COVID-19 pandemic.
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