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Modeling and Forecasting the COVID-19 Temporal Spread in Greece: An Exploratory Approach Based on Complex Network Defined Splines

Author

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  • Konstantinos Demertzis

    (Laboratory of Complex Systems, Department of Physics, Faculty of Sciences, International Hellenic University, Kavala Campus, 65404 St. Loukas, Greece)

  • Dimitrios Tsiotas

    (Laboratory of Complex Systems, Department of Physics, Faculty of Sciences, International Hellenic University, Kavala Campus, 65404 St. Loukas, Greece
    Department of Regional and Economic Development, Agricultural University of Athens, Greece, Nea Poli, 33100 Amfissa, Greece
    Department of Planning and Regional Development, University of Thessaly, Pedion Areos, 38334 Volos, Greece)

  • Lykourgos Magafas

    (Laboratory of Complex Systems, Department of Physics, Faculty of Sciences, International Hellenic University, Kavala Campus, 65404 St. Loukas, Greece)

Abstract

Within the complex framework of anti-COVID-19 health management, where the criteria of diagnostic testing, the availability of public-health resources and services, and the applied anti-COVID-19 policies vary between countries, the reliability and accuracy in the modeling of temporal spread can prove to be effective in the worldwide fight against the disease. This paper applies an exploratory time-series analysis to the evolution of the disease in Greece, which currently suggests a success story of COVID-19 management. The proposed method builds on a recent conceptualization of detecting connective communities in a time-series and develops a novel spline regression model where the knot vector is determined by the community detection in the complex network. Overall, the study contributes to the COVID-19 research by proposing a free of disconnected past-data and reliable framework of forecasting, which can facilitate decision-making and management of the available health resources.

Suggested Citation

  • Konstantinos Demertzis & Dimitrios Tsiotas & Lykourgos Magafas, 2020. "Modeling and Forecasting the COVID-19 Temporal Spread in Greece: An Exploratory Approach Based on Complex Network Defined Splines," IJERPH, MDPI, vol. 17(13), pages 1-17, June.
    • Handle: RePEc:gam:jijerp:v:17:y:2020:i:13:p:4693-:d:378124
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    References listed on IDEAS

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    1. Cleo Anastassopoulou & Lucia Russo & Athanasios Tsakris & Constantinos Siettos, 2020. "Data-based analysis, modelling and forecasting of the COVID-19 outbreak," PLOS ONE, Public Library of Science, vol. 15(3), pages 1-21, March.
    2. Fotios Petropoulos & Spyros Makridakis, 2020. "Forecasting the novel coronavirus COVID-19," PLOS ONE, Public Library of Science, vol. 15(3), pages 1-8, March.
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    Cited by:

    1. Angelo Spena & Leonardo Palombi & Massimo Corcione & Alessandro Quintino & Mariachiara Carestia & Vincenzo Andrea Spena, 2020. "Predicting SARS-CoV-2 Weather-Induced Seasonal Virulence from Atmospheric Air Enthalpy," IJERPH, MDPI, vol. 17(23), pages 1-14, December.
    2. Lakshman S. Thakur & Mikhail A. Bragin, 2021. "Data Interpolation by Near-Optimal Splines with Free Knots Using Linear Programming," Mathematics, MDPI, vol. 9(10), pages 1-12, May.
    3. Raúl Jiménez-Cruz & José-Luis Velázquez-Rodríguez & Itzamá López-Yáñez & Yenny Villuendas-Rey & Cornelio Yáñez-Márquez, 2021. "Supervised Classification of Diseases Based on an Improved Associative Algorithm," Mathematics, MDPI, vol. 9(13), pages 1-25, June.
    4. Yiannis Contoyiannis & Stavros G. Stavrinides & Michael P. Hanias & Myron Kampitakis & Pericles Papadopoulos & Rodrigo Picos & Stelios M. Potirakis, 2020. "A Universal Physics-Based Model Describing COVID-19 Dynamics in Europe," IJERPH, MDPI, vol. 17(18), pages 1-19, September.
    5. Dunfrey Pires Aragão & Davi Henrique dos Santos & Adriano Mondini & Luiz Marcos Garcia Gonçalves, 2021. "National Holidays and Social Mobility Behaviors: Alternatives for Forecasting COVID-19 Deaths in Brazil," IJERPH, MDPI, vol. 18(21), pages 1-24, November.
    6. Chaharborj, Sarkhosh Seddighi & Nabi, Khondoker Nazmoon & Feng, Koo Lee & Chaharborj, Shahriar Seddighi & Phang, Pei See, 2022. "Controlling COVID-19 transmission with isolation of influential nodes," Chaos, Solitons & Fractals, Elsevier, vol. 159(C).

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