Milano Marianna, Agapito Giuseppe, Cannataro Mario
Department of Medical and Surgical Sciences, Data Analytics Research Center, University Magna Græcia, 88100 Catanzaro, Italy.
Department of Law, Economics and Social Sciences, University Magna Græcia of Catanzaro, 88100 Catanzaro, Italy.
BioTech (Basel). 2022 Aug 11;11(3):33. doi: 10.3390/biotech11030033.
Italy was one of the European countries most afflicted by the COVID-19 pandemic. From 2020 to 2022, Italy adopted strong containment measures against the COVID-19 epidemic and then started an important vaccination campaign. Here, we extended previous work by applying the COVID-19 Community Temporal Visualizer (CCTV) methodology to Italian COVID-19 data related to 2020, 2021, and five months of 2022. The aim of this work was to evaluate how Italy reacted to the pandemic in the first two waves of COVID-19, in which only containment measures such as the lockdown had been adopted, in the months following the start of the vaccination campaign, the months with the mildest weather, and the months affected by the new COVID-19 variants. This assessment was conducted by observing the behavior of single regions. CCTV methodology allows us to map the similarities in the behavior of Italian regions on a graph and use a community detection algorithm to visualize and analyze the spatio-temporal evolution of data. The results depict that the communities formed by Italian regions change with respect to the ten data measures and time.
意大利是受新冠疫情影响最严重的欧洲国家之一。从2020年到2022年,意大利对新冠疫情采取了强有力的防控措施,随后启动了一项重要的疫苗接种运动。在此,我们通过将新冠疫情社区时间可视化工具(CCTV)方法应用于与2020年、2021年以及2022年前五个月相关的意大利新冠疫情数据,扩展了之前的工作。这项工作的目的是评估意大利在新冠疫情的前两波中如何应对疫情,在前两波疫情中仅采取了诸如封锁等防控措施,评估在疫苗接种运动开始后的几个月、天气最温和的几个月以及受新冠病毒新变种影响的几个月里的情况。这项评估是通过观察各个地区的行为来进行的。CCTV方法使我们能够在图表上描绘意大利各地区行为的相似性,并使用社区检测算法来可视化和分析数据的时空演变。结果表明,由意大利各地区形成的社区在十种数据指标和时间方面会发生变化。