COVID-19曲线趋平:全球大流行中的“希腊”案例

Konstantinos Demertzis, L. Magafas, D. Tsiotas
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引用次数: 1

摘要

2019冠状病毒病大流行引发的全球危机,加上经济后果和卫生系统崩溃,引起了欧洲的严重关切。欧洲是受疫情影响最严重的大陆,共记录了2388694例病例和190,091例死亡(占全球总数的39.6%),其中71.7%(136,238例)发生在英国(43,414例)、意大利(34708例)、法国(29,778例)和西班牙(28,338例)。与其他国家不同,在对这一现象的研究和分析中,希腊是一个明显的例外,确诊病例约为310例,每百万人中有18人死亡。针对希腊新冠肺炎疫情在流行病学和实施方面的特点,对希腊新冠肺炎疫情的时间传播进行了探索性分析,提出了基于回归样条算法和总感染变化率的疾病建模和预测方法。此外,本文还提出了一种混合样条回归和社会距离测量的复杂网络模型,以评估和解释疾病的传播。整体方法有助于公共卫生系统的决策和支持,并有助于抗击大流行。
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Flattening the COVID-19 Curve: The “Greek” case in the Global Pandemic
The global crisis caused by the COVID-19 pandemic, in conjunction with the economic consequences and the collapse of health systems, has raised serious concerns in Europe, which is the most affected continent by the pandemic since it recorded 2,388,694 cases and 190,091 deaths (39.6% of the worldwide total), of which 71.7% (136,238) are in the United Kingdom (43,414), Italy (34,708), France (29,778), and Spain (28,338). Unlike other countries, Greece, with about 310 confirmed cases and 18 deaths per million, is one bright exception in the study and analysis of this phenomenon. Focusing on the peculiarities of the disease spreading in Greece, both in epidemiological and in implementation terms, this paper applies an exploratory analysis of COVID-19 temporal spread in Greece and proposes a methodological approach for the modelling and prediction of the disease based on the Regression Splines algorithm and the change rate of the total infections. Also, it proposes a hybrid spline regression and complex network model of social distance measures evaluating and interpreting the spread of the disease. The overall approach contributes to decision making and support of the public health system and to the fight against the pandemic.
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