Spassiani Ilaria, Gubian Lorenzo, Palù Giorgio, Sebastiani Giovanni
Istituto Nazionale di Geofisica e Vulcanologia, 00143 Rome, Italy.
UOC Sistemi Informativi Azienda Zero-Regione del Veneto, 35131 Padua, Italy.
Vaccines (Basel). 2020 Dec 15;8(4):766. doi: 10.3390/vaccines8040766.
SARS-CoV-2 is highly contagious, rapidly turned into a pandemic, and is causing a relevant number of critical to severe life-threatening COVID-19 patients. However, robust statistical studies of a large cohort of patients, potentially useful to implement a vaccination campaign, are rare. We analyzed public data of about 19,000 patients for the period 28 February to 15 May 2020 by several mathematical methods. Precisely, we describe the COVID-19 evolution of a number of variables that include age, gender, patient's care location, and comorbidities. It prompts consideration of special preventive and therapeutic measures for subjects more prone to developing life-threatening conditions while affording quantitative parameters for predicting the effects of an outburst of the pandemic on public health structures and facilities adopted in response. We propose a mathematical way to use these results as a powerful tool to face the pandemic and implement a mass vaccination campaign. This is done by means of priority criteria based on the influence of the considered variables on the probability of both death and infection.
严重急性呼吸综合征冠状病毒2（SARS-CoV-2）具有高度传染性，迅速演变成一场大流行，导致相当数量的新冠肺炎患者病情危急甚至危及生命。然而，针对大量患者群体进行的、可能有助于开展疫苗接种运动的有力统计研究却很少见。我们运用多种数学方法分析了2020年2月28日至5月15日期间约19000名患者的公开数据。确切地说，我们描述了包括年龄、性别、患者护理地点和合并症在内的多个变量的新冠肺炎病情发展情况。这促使我们考虑针对更易发展为危及生命状况的人群采取特殊的预防和治疗措施，同时为预测大流行爆发对公共卫生结构和设施的影响以及相应采取的应对措施提供量化参数。我们提出一种数学方法，将这些结果用作应对大流行和开展大规模疫苗接种运动的有力工具。这是通过基于所考虑变量对死亡和感染概率的影响的优先标准来实现的。
