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首页> 美国卫生研究院文献>International Journal of Environmental Research and Public Health >A Clustering Approach to Classify Italian Regions and Provinces Based on Prevalence and Trend of SARS-CoV-2 Cases
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A Clustering Approach to Classify Italian Regions and Provinces Based on Prevalence and Trend of SARS-CoV-2 Cases

机译：基于SARS-COV-2案例的意大利地区和省份对意大利地区和省份进行分类的聚类方法

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While several efforts have been made to control the epidemic of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in Italy, differences between and within regions have made it difficult to plan the phase two management after the national lockdown. Here, we propose a simple and immediate clustering approach to categorize Italian regions working on the prevalence and trend of SARS-CoV-2 positive cases prior to the start of phase two on 4 May 2020. Applying both hierarchical and k-means clustering, we identified three regional groups: regions in cluster 1 exhibited higher prevalence and the highest trend of SARS-CoV-2 positive cases; those classified into cluster 2 constituted an intermediate group; those in cluster 3 were regions with a lower prevalence and the lowest trend of SARS-CoV-2 positive cases. At the provincial level, we used a similar approach but working on the prevalence and trend of the total SARS-CoV-2 cases. Notably, provinces in cluster 1 exhibited the highest prevalence and trend of SARS-CoV-2 cases. Provinces in clusters 2 and 3, instead, showed a median prevalence of approximately 11 cases per 10,000 residents. However, provinces in cluster 3 were those with the lowest trend of cases. K-means clustering yielded to an alternative cluster solution in terms of the prevalence and trend of SARS-CoV-2 cases. Our study described a simple and immediate approach to monitor the SARS-CoV-2 epidemic at the regional and provincial level. These findings, at present, offered a snapshot of the epidemic, which could be helpful to outline the hierarchy of needs at the subnational level. However, the integration of our approach with further indicators and characteristics could improve our findings, also allowing the application to different contexts and with additional aims.
机译：虽然已经进行了几种努力来控制意大利的严重急性呼吸综合征冠状病毒2（SARS-COV-2）的疫情，但地区之间的差异使得在国家锁定之后难以规划第二阶段管理。在这里，我们提出了一种简单和即时的聚类方法，可以在2020年5月4日之前对SARS-COV-2阳性病例的普遍存在和趋势进行分类来分类意大利地区。申请分层和K-means聚类，我们确定了三个区域组：聚集体中的地区患病率较高，SARS-COV-2积极案件的最高趋势;分为集群2的人构成了中间组;那些在群中的那些是具有较低患病率和SARS-COV-2阳性病例的最低趋势的地区。在省级，我们使用了类似的方法，但致力于SARS-COV-2案件的患病率和趋势。值得注意的是，集群1的省份表现出最高的患病率和SARS-COV-2案件的趋势。相反，集群2和3的省份显示了每10,000名居民大约11例中位患病率。然而，集群3中的省份是患者趋势最低的人。 K-Means聚类在SARS-COV-2案例的患病率和趋势方面产生了替代聚类解决方案。我们的研究描述了在区域和省级监测SARS-COV-2流行病的简单和即时的方法。目前这些调查结果提供了疫情的快照，这可能有助于在地方一级概述需求的层次结构。然而，我们的方法与进一步指标和特征的整合可以改善我们的调查结果，也允许申请到不同的背景和额外的目标。

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