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Article

Identifying Country-Level Risk Factors for the Spread of COVID-19 in Europe Using Machine Learning

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AIDEAS OÜ, Narva mnt 5, 10117 Tallinn, Estonia
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Department of Physical Education and Sport Science, Democritus University of Thrace, 69100 Komotini, Greece
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Center for Research and Technology Hellas, Institute for Bio-Economy & Agri-Technology, 38333 Volos, Greece
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Joint Rheumatology Program, First Department of Propaedeutic Internal Medicine, Medical School, National and Kapodistrian University of Athens, 11527 Athens, Greece
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Fourth Department of Internal Medicine, Attikon Hospital, Medical School, National and Kapodistrian University of Athens, 11527 Athens, Greece
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Department of Hygiene, Epidemiology and Medical Statistics, Medical School, National and Kapodistrian University of Athens, 11527 Athens, Greece
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Second Department of Paediatrics, “P. & A. Kyriakou” Children’s Hospital, Medical School, National and Kapodistrian University of Athens, 11527 Athens, Greece
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National Public Health Organization, 15123 Athens, Greece
*
Author to whom correspondence should be addressed.
Academic Editor: Koichi Watashi
Viruses 2022, 14(3), 625; https://doi.org/10.3390/v14030625
Received: 31 January 2022 / Revised: 6 March 2022 / Accepted: 14 March 2022 / Published: 17 March 2022
(This article belongs to the Special Issue State-of-the-Art Virus Research in Greece)
Coronavirus disease 2019 (COVID-19) has resulted in approximately 5 million deaths around the world with unprecedented consequences in people’s daily routines and in the global economy. Despite vast increases in time and money spent on COVID-19-related research, there is still limited information about the factors at the country level that affected COVID-19 transmission and fatality in EU. The paper focuses on the identification of these risk factors using a machine learning (ML) predictive pipeline and an associated explainability analysis. To achieve this, a hybrid dataset was created employing publicly available sources comprising heterogeneous parameters from the majority of EU countries, e.g., mobility measures, policy responses, vaccinations, and demographics/generic country-level parameters. Data pre-processing and data exploration techniques were initially applied to normalize the available data and decrease the feature dimensionality of the data problem considered. Then, a linear ε-Support Vector Machine (ε-SVM) model was employed to implement the regression task of predicting the number of deaths for each one of the three first pandemic waves (with mean square error of 0.027 for wave 1 and less than 0.02 for waves 2 and 3). Post hoc explainability analysis was finally applied to uncover the rationale behind the decision-making mechanisms of the ML pipeline and thus enhance our understanding with respect to the contribution of the selected country-level parameters to the prediction of COVID-19 deaths in EU. View Full-Text
Keywords: COVID-19; machine learning; data mining; explainability COVID-19; machine learning; data mining; explainability
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MDPI and ACS Style

Moustakidis, S.; Kokkotis, C.; Tsaopoulos, D.; Sfikakis, P.; Tsiodras, S.; Sypsa, V.; Zaoutis, T.E.; Paraskevis, D. Identifying Country-Level Risk Factors for the Spread of COVID-19 in Europe Using Machine Learning. Viruses 2022, 14, 625. https://doi.org/10.3390/v14030625

AMA Style

Moustakidis S, Kokkotis C, Tsaopoulos D, Sfikakis P, Tsiodras S, Sypsa V, Zaoutis TE, Paraskevis D. Identifying Country-Level Risk Factors for the Spread of COVID-19 in Europe Using Machine Learning. Viruses. 2022; 14(3):625. https://doi.org/10.3390/v14030625

Chicago/Turabian Style

Moustakidis, Serafeim, Christos Kokkotis, Dimitrios Tsaopoulos, Petros Sfikakis, Sotirios Tsiodras, Vana Sypsa, Theoklis E. Zaoutis, and Dimitrios Paraskevis. 2022. "Identifying Country-Level Risk Factors for the Spread of COVID-19 in Europe Using Machine Learning" Viruses 14, no. 3: 625. https://doi.org/10.3390/v14030625

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