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This paper investigates the spatio-temporal spread pattern of COVID-19 in Italy, during the first wave of infections, from February to October 2020. Disease mappings of the virus infections by using the Besag–York–Mollié model and some spatio-temporal extensions are provided. This modeling framework, which includes a temporal component, allows the studying of the time evolution of the spread pattern among the 107 Italian provinces. The focus is on the effect of citizens' mobility patterns, represented here by the three distinct phases of the Italian virus first wave, identified by the Italian government, also characterized by the lockdown period. Results show the effectiveness of the lockdown action and an inhomogeneous spatial trend that characterizes the virus spread during the first wave. Furthermore, the results suggest that the temporal evolution of each province's cases is independent of the temporal evolution of the other ones, meaning that the contagions and temporal trend may be caused by some province-specific aspects rather than by the subjects' spatial movements. [ABSTRACT FROM AUTHOR]

We applied a flexible modeling technique capable of representing dynamics of large populations interacting in space and time, namely Markovian Agents, to study the evolution of COVID-19 in Italy. Our purpose was to show that this modeling approach, that is based on mean field analysis models, provides good performances in describing the diffusion of phenomena, like COVID-19. The paper describes the application of this modeling approach to the Italian scenario and results are validated against real data available about the Italian official documentation of the diffusion of COVID-19. The model of each agent is organized similarly to what largely established in literature in the Susceptible-Infected-Recovered (SIR) family of approaches. Results match the main events taken by the Italian government and their effects. [ABSTRACT FROM AUTHOR]

In this short communication, we investigate whether the intensity of the second wave of infection from SARS-CoV-2 that hit Italy in October–November–December 2020 is related to the intensity of the first wave, which took place in March–April 2020. We exploit the variation of the wave intensities across the 107 Italian provinces. Since the first wave has affected not only different regions, but also different provinces of the same region, at a heterogenous degree, this comparison allows useful insights to be drawn about the characteristics of the virus. We estimate a strong negative correlation between the new daily infections among provinces during the first and second waves and show that this result is robust to different specifications. This empirical result can be of inspiration to biologists on the nature of collective immunity underlying COVID-19. [ABSTRACT FROM AUTHOR]