希腊COVID-19时间扩散的建模和预测：基于复杂网络定义样条的探索性方法
Modeling and forecasting the COVID-19 temporal spread in Greece: an exploratory approach based on complex network defined splines
摘要：
在抗COVID-19健康管理的复杂框架内，各国的诊断检测标准、公共卫生资源和服务的可用性以及所应用的抗COVID-19政策各不相同，时间传播建模的可靠性和准确性可以证明在全球抗击疾病方面是有效的。本文对希腊的疾病演变进行了探索性的时间序列分析，这是目前COVID-19管理的成功案例。该方法建立在最近的概念化检测连接社区的时间序列，并开发了一种新的样条回归模型，其中节点向量是由社区检测在复杂的网络。总体而言，该研究通过提出一个无间断的过去数据和可靠的预测框架，有助于COVID-19研究，这可以促进可用卫生资源的决策和管理。
英文摘要：
Within the complex framework of anti-COVID-19 health management, where the criteria of diagnostic testing, the availability of public-health resources and services, and the applied anti-COVID-19 policies vary between countries, the reliability and the accuracy in the modeling of temporal spread can be proven effective in the worldwide fight against the disease. This paper applies an exploratory time-series analysis to the evolution of the disease in Greece, which currently suggests a success story of COVID-19 management. The proposed method builds on a recent conceptualization of detecting connective communities in a time-series and develops a novel spline regression model where the knot vector is determined by the community detection in the complex network. Overall, the study contributes to the COVID-19 research by proposing a free of disconnected past-data and reliable framework of forecasting, which can facilitate decision-making and management of the available health resources.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
