Entropy-Based Applications in Economics, Finance, and Management

ISBN 978-3-0365-5805-9 (Hbk); ISBN 978-3-0365-5806-6 (PDF)

 Joanna Olbryś (Ed.) Pages: 276Published: November 2022(This book is a reprint of the Special Issue Entropy-Based Applications in Economics, Finance, and Management that was published in Entropy)

Aleksandra Łuczak and Sławomir Kalinowski, Fuzzy Clustering Methods to Identify the Epidemiological Situation and Its Changes in European Countries during COVID-19 Reprinted from: Entropy 2021, 24, 14, doi:10.3390/e24010014

Abstract: The main research question concerned the identification of changes in the COVID-19 epidemiological situation using fuzzy clustering methods. This research used cross-sectional time series data obtained from the European Centre for Disease Prevention and Control. The identification of country types in terms of epidemiological risk was carried out using the fuzzy c-means clustering method. We also used the entropy index to measure the degree of fuzziness in the classification and evaluate the uncertainty of epidemiological states. The proposed approach allowed us to identify countries’ epidemic states. Moreover, it also made it possible to determine the time of transition from one state to another, as well as to observe fluctuations during changes of state. Three COVID-19 epidemic states were identified in Europe, i.e., stabilisation, destabilisation, and expansion. The methodology is universal and can also be useful for other countries, as well as the research results being important for governments, politicians and other policy-makers working to mitigate the effects of the COVID-19 pandemic. Keywords: fuzzy c-means classification method; entropy; COVID-19; epidemic states; Europe