论文标题
COVID-19缓解策略和欧洲相关研究结果的概述
COVID-19 mitigation strategies and overview on results from relevant studies in Europe
论文作者
论文摘要
2019年12月，中国武汉的第一批患者被诊断出患有原发性非典型肺炎，该患者表现出未知且具有传染性。从那时起，被称为Covid-19疾病，负责的病毒病原体SARS-COV-2在全球遍布全球。关于如何应对危机的决定通常是基于对病毒大流行传播的模拟。本文将更详细地介绍其中一些结果以及它们的方法和改进的可能性，以超越当前公共卫生教条，称为“扁平曲线”。为了模拟疾病的传播，有几种模拟流行病的方法。根据相关实际数据的及时性，范围和质量，这些多变量模型在使用的参数的价值上有所不同，并且在选择考虑影响因素的选择方面也有所不同。示例性的表明，其课程中的流行病是通过假设次指数生长的模型更真实地模拟的。此外，欧洲的观点介绍了Covid-19大流行的各种模拟，相互比较并更详细地进行了讨论。很难估计当前大流行模型的模拟有多可信，因此，是否可以通过采取的措施有效地减少大流行的传播还有待观察。模型在现实中是否效果很好，主要取决于其基本数据的质量和范围。过去的研究表明，对策能够降低流行病的繁殖数量或传输速率。除此之外，提出的建模研究为创建亚指数增长模型的框架提供了一个良好的框架，用于评估COVID-19的传播。
In December 2019, the first patients in Wuhan, China were diagnosed with a primary atypical pneumonia, which showed to be unknown and contagious. Since then, known as COVID-19 disease, the responsible viral pathogen, SARS-CoV-2, has spread around the world in a pandemic. Decisions on how to deal with the crisis are often based on simulations of the pandemic spread of the virus. The results of some of these, as well as their methodology and possibilities for improvement, will be described in more detail in this paper in order to inform beyond the current public health dogma called "flatten-the-curve". There are several ways to model an epidemic in order to simulate the spread of diseases. Depending on the timeliness, scope and quality of the associated real data, these multivariable models differ in the value of used parameters, but also in the selection of considered influencing factors. It was exemplarily shown that epidemics in their course are simulated more realistically by models that assume subexponential growth. Furthermore, various simulations of the COVID-19 pandemic were presented in an European perspective, compared against each other and discussed in more detail. It is difficult to estimate how credible the simulations of the pandemic models currently are, so it remains to be seen whether the spread of the pandemic can be effectively reduced by the measures taken. Whether a model works well in reality is largely determined by the quality and scope of its underlying data. Past studies have shown that countermeasures are able to reduce reproduction numbers or transmission rates in epidemics. In addition to that, the presented modelling study provides a good framework for the creation of subexponential-growth-models for assessing the spread of COVID-19.
