Abstract
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.
Highlights
The coronavirus SARS-CoV-2 (COVID-19) and the triggered disease were unknown before the outbreak began in Wuhan, China, in December 2019, and spread worldwide quickly
Ref. [9] studies the epidemic spread in Iran through linear spatial models to identify the variables that have significantly impacted the virus infections size
As discussed in [15], this paper focuses on the COVID-19 data limitations terms of availability and quality
Summary
The coronavirus SARS-CoV-2 (COVID-19) and the triggered disease were unknown before the outbreak began in Wuhan, China, in December 2019, and spread worldwide quickly. The COVID-19 diffusion in Italy from February to October 2020, is modeled by the Besag–York–Mollié (BYM) model [17] and its spatio-temporal formulation. This model has been applied in disease mapping for areal aggregated data [18,19], supplying risk surfaces and spotting high-risk areas or hot-spots. The BYM model accounts for the neighborhood structure of the available count data, modeling the number of cases per district (denoting the general spatial unit), identifying the high-risk areas. The spatio-temporal extension of the BYM model, accounting for the temporal domain, explores whether it is possible to highlight any specific evolution of the risk disease among the different phases of the Italian virus first wave.
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