Laboratory of Complex Systems, Department of Physics, Faculty of Sciences, International Hellenic University, Kavala Campus, 65404 St. Loukas, Greece.
Department of Regional and Economic Development, Agricultural University of Athens, Greece, Nea Poli, 33100 Amfissa, Greece.
Int J Environ Res Public Health. 2020 Jun 30;17(13):4693. doi: 10.3390/ijerph17134693.
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 accuracy in the modeling of temporal spread can prove to be 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.
在复杂的新冠疫情防控健康管理框架中，各国之间的诊断检测标准、公共卫生资源和服务的可及性以及实施的新冠疫情防控政策存在差异，因此，对时间传播进行可靠且准确的建模可以在全球抗击疾病的斗争中发挥重要作用。本文对希腊疫情的发展进行了探索性时间序列分析，希腊目前在新冠疫情防控方面被认为是一个成功的案例。所提出的方法基于对时间序列中连接社区的检测的最新概念化，并开发了一种新的样条回归模型，其中节点向量由复杂网络中的社区检测确定。总的来说，本研究通过提出一种无过去数据脱节且可靠的预测框架，为新冠疫情研究做出了贡献，该框架可以为决策和管理现有卫生资源提供便利。
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