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Article

The Effect of Anti-COVID-19 Policies on the Evolution of the Disease: A Complex Network Analysis of the Successful Case of Greece

1
Department of Regional and Economic Development, Agricultural University of Athens, Greece, Nea Poli, 33100 Amfissa, Greece
2
Department of Planning and Regional Development, University of Thessaly, Pedion Areos, 38334 Volos, Greece
3
Laboratory of Complex Systems, Department of Physics, International Hellenic University, Kavala Campus, 65404 St. Loukas, Greece
*
Author to whom correspondence should be addressed.
Physics 2020, 2(2), 325-339; https://doi.org/10.3390/physics2020017
Received: 22 April 2020 / Revised: 16 June 2020 / Accepted: 19 June 2020 / Published: 22 June 2020
(This article belongs to the Special Issue Physics Methods in Coronavirus Pandemic Analysis)
Within the context of Greece promising a success story in the fight against the disease, this paper proposes a novel method for studying the evolution of the Greek COVID-19 infection curve in relation to the anti-COVID-19 policies applied to control the pandemic. Based on the ongoing spread of COVID-19 and the insufficient data for applying classic time-series approaches, the analysis builds on the visibility graph algorithm to study the Greek COVID-19 infection curve as a complex network. By using the modularity optimization algorithm, the generated visibility graph is divided into communities defining periods of different connectivity in the time-series body. These periods reveal a sequence of different typologies in the evolution of the disease, starting with a power pattern, where a second order polynomial (U-shaped) pattern intermediates, being followed by a couple of exponential patterns, and ending up with a current logarithmic pattern revealing that the evolution of the Greek COVID-19 infection curve tends towards saturation. In terms of Gaussian modeling, this successive compression of the COVID-19 infection curve into five parts implies that the pandemic in Greece is about to reach the second (decline) half of the bell-shaped distribution. The network analysis also illustrates stability of hubs and instability of medium and low-degree nodes, implying a low probability of meeting maximum (infection) values in the future and high uncertainty in the variability of other values below the average. The overall approach contributes to the scientific research by proposing a novel method for the structural decomposition of a time-series into periods, which allows removing from the series the disconnected past-data facilitating better forecasting, and provides insights of good policy and decision-making practices and management that may help other countries improve their performance in the war against COVID-19. View Full-Text
Keywords: coronavirus; pandemics; infectious disease; natural visibility algorithm; community detection; modularity optimization coronavirus; pandemics; infectious disease; natural visibility algorithm; community detection; modularity optimization
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MDPI and ACS Style

Tsiotas, D.; Magafas, L. The Effect of Anti-COVID-19 Policies on the Evolution of the Disease: A Complex Network Analysis of the Successful Case of Greece. Physics 2020, 2, 325-339. https://doi.org/10.3390/physics2020017

AMA Style

Tsiotas D, Magafas L. The Effect of Anti-COVID-19 Policies on the Evolution of the Disease: A Complex Network Analysis of the Successful Case of Greece. Physics. 2020; 2(2):325-339. https://doi.org/10.3390/physics2020017

Chicago/Turabian Style

Tsiotas, Dimitrios; Magafas, Lykourgos. 2020. "The Effect of Anti-COVID-19 Policies on the Evolution of the Disease: A Complex Network Analysis of the Successful Case of Greece" Physics 2, no. 2: 325-339. https://doi.org/10.3390/physics2020017

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