With the advent of the first pandemic wave of Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2), the question arises as to whether the spread of the virus will be controlled by the application of preventive measures or will follow a different course, regardless of the pattern of spread already recorded. These conditions caused by the unprecedented pandemic have highlighted the importance of reliable data from official sources, their complete recording and analysis, and accurate investigation of epidemiological indicators in almost real time. There is an ongoing research demand for reliable and effective modeling of the disease but also the formulation of substantiated views to make optimal decisions for the design of preventive or repressive measures by those responsible for the implementation of policy in favor of the protection of public health. The main objective of the study is to present an innovative data-analysis system of COVID-19 disease progression in Greece and her border countries by real-time statistics about the epidemiological indicators. This system utilizes visualized data produced by an automated information system developed during the study, which is based on the analysis of large pandemic-related datasets, making extensive use of advanced machine learning methods. Finally, the aim is to support with up-to-date technological means optimal decisions in almost real time as well as the development of medium-term forecast of disease progression, thus assisting the competent bodies in taking appropriate measures for the effective management of the available health resources.

中文翻译：

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通过高级机器学习进行大流行分析以改进 COVID-19 危机的决策

随着第一波严重急性呼吸系统综合症冠状病毒-2 (SARS-CoV-2) 大流行的到来，出现了一个问题，即病毒的传播是通过采取预防措施来控制还是走不同的路线，不管已经记录的传播模式。史无前例的大流行造成的这些情况凸显了来自官方来源的可靠数据、完整记录和分析以及几乎实时准确调查流行病学指标的重要性。持续的研究需要对疾病进行可靠和有效的建模，但也需要制定有根据的观点，以便负责执行有利于保护公共健康的政策的人员设计预防或压制措施的最佳决策。该研究的主要目的是通过流行病学指标的实时统计，在希腊及其边境国家展示一个创新的 COVID-19 疾病进展数据分析系统。该系统利用研究期间开发的自动化信息系统生成的可视化数据，该系统基于对与大流行相关的大型数据集的分析，广泛使用先进的机器学习方法。最后，