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Article

Pandemic Analytics by Advanced Machine Learning for Improved Decision Making of COVID-19 Crisis

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Laboratory of Complex Systems, Department of Physics, Faculty of Sciences, Kavala Campus, International Hellenic University, 65404 St. Loukas, Greece
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Faculty of Mathematics Programming and General Courses, Department of Civil Engineering, School of Engineering, Democritus University of Thrace, Kimmeria, 67100 Xanthi, Greece
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Hellenic National Defence General Staff, Stratopedo Papagou, Mesogeion 227-231, 15561 Athens, Greece
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Department of Regional and Economic Development, Agricultural University of Athens, Nea Poli, 33100 Amfissa, Greece
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Department of Planning and Regional Development, University of Thessaly, Pedion Areos, 38334 Volos, Greece
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School of Science, Department of Computer Science, University of Thessaly, Galaneika, 35131 Lamia, Greece
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Author to whom correspondence should be addressed.
Academic Editor: Jae-Yoon Jung
Processes 2021, 9(8), 1267; https://doi.org/10.3390/pr9081267
Received: 29 June 2021 / Revised: 19 July 2021 / Accepted: 20 July 2021 / Published: 22 July 2021
(This article belongs to the Special Issue Advance in Machine Learning)
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. View Full-Text
Keywords: COVID-19; pandemic; data analytics; prediction; decision making; machine learning COVID-19; pandemic; data analytics; prediction; decision making; machine learning
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MDPI and ACS Style

Demertzis, K.; Taketzis, D.; Tsiotas, D.; Magafas, L.; Iliadis, L.; Kikiras, P. Pandemic Analytics by Advanced Machine Learning for Improved Decision Making of COVID-19 Crisis. Processes 2021, 9, 1267. https://doi.org/10.3390/pr9081267

AMA Style

Demertzis K, Taketzis D, Tsiotas D, Magafas L, Iliadis L, Kikiras P. Pandemic Analytics by Advanced Machine Learning for Improved Decision Making of COVID-19 Crisis. Processes. 2021; 9(8):1267. https://doi.org/10.3390/pr9081267

Chicago/Turabian Style

Demertzis, Konstantinos, Dimitrios Taketzis, Dimitrios Tsiotas, Lykourgos Magafas, Lazaros Iliadis, and Panayotis Kikiras. 2021. "Pandemic Analytics by Advanced Machine Learning for Improved Decision Making of COVID-19 Crisis" Processes 9, no. 8: 1267. https://doi.org/10.3390/pr9081267

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