论文标题
抗科维德 - 19政策对疾病进化的影响：对希腊成功案例的复杂网络分析
The effect of anti-COVID-19 policies to the evolution of the disease: A complex network analysis to the successful case of Greece
论文作者
论文摘要
在希腊有望在与疾病斗争中取得成功的背景下，本文提出了一种新的方法，以研究希腊共vid-19感染 - 曲线的演变，涉及用于控制大流行的反covid-19政策。基于COVID-19的持续扩散以及应用经典时间序列方法的数据不足，该分析基于可见性图算法，以研究希腊covid-19-19感染曲线作为复杂的网络。通过使用模块化优化算法，将生成的可见性图分为定义时间序列中不同连通性的社区。这些时期揭示了疾病演变中的一系列不同类型的序列，从功率模式开始，其中二阶多项式（U形）模式中间体中间体，随后是几个指数式模式，最终以当前的对数模式显示出希腊covid-19 Invection-19 Invection-Curve curve to Apation at ate Adusation the Greek covid covid模式。网络分析还说明了枢纽的稳定性和培养基和低度节点的不稳定性，这意味着在未来满足最大（感染）值的可能性较低，而其他值低于平均值的变异性的不确定性很高。总体方法通过提出一种新颖的方法来促进科学研究，以将时间序列的结构分解为时期，这允许从该系列中删除脱节的过去数据，从而促进了更好的预测，并提供了良好的政策和决策实践和管理的见解，可以帮助其他国家在反对COVID的战争中提高其绩效。
Within the context that Greece promises a success story in the fight against the disease, this paper proposes a novel method to study the evolution of the Greek COVID-19 infection-curve in relation to the anti-COVID-19 policies applied to control the pandemic. Based on the ongoing spreading of COVID-19 and the insufficient data for applying classic time-series approaches, the analysis builds on the visibility graph algorithm to study the Greek COVID-19 infection-curve as a complex network. By using the modularity optimization algorithm, the generated visibility graph is divided into communities defining periods of different connectivity in the time-series body. These periods reveal a sequence of different typologies in the evolution of the disease, starting with a power pattern, where a second order polynomial (U-shaped) pattern intermediates, being followed by a couple of exponential patterns, and ending up with a current logarithmic pattern revealing that the evolution of the Greek COVID-19 infection-curve tends into saturation. The network analysis also illustrates stability of hubs and instability of medium and low-degree nodes, implying a low probability to meet maximum (infection) values at the future and high uncertainty in the variability of other values below the average. The overall approach contributes to the scientific research by proposing a novel method for the structural decomposition of a time-series into periods, which allows removing from the series the disconnected past-data facilitating better forecasting, and provides insights of good policy and decision-making practices and management that may help other countries improve their performance in the war against COVID-19.
