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

Demertzis, Konstantinos; Tsiotas, Dimitrios; Magafas, Lykourgos

doi:10.3390/ijerph17134693

Submit to this Journal Review for this Journal Edit a Special Issue

Article Menu

...

Open AccessArticle

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

Konstantinos Demertzis

^1,*

Dimitrios Tsiotas

^1,2,3

and

Lykourgos Magafas

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

Author to whom correspondence should be addressed.

Int. J. Environ. Res. Public Health 2020, 17(13), 4693; https://doi.org/10.3390/ijerph17134693

Received: 3 May 2020 / Revised: 27 June 2020 / Accepted: 29 June 2020 / Published: 30 June 2020

View Full-Text Download PDF

Browse Figures

Review Reports Citation Export

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. View Full-Text

Keywords: COVID-19 coronavirus pandemic; outbreak; modeling; prediction; regression splines; modularity optimization algorithm

▼ Show Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

Share and Cite

MDPI and ACS Style

Demertzis, K.; Tsiotas, D.; Magafas, L. Modeling and Forecasting the COVID-19 Temporal Spread in Greece: An Exploratory Approach Based on Complex Network Defined Splines. Int. J. Environ. Res. Public Health 2020, 17, 4693. https://doi.org/10.3390/ijerph17134693

AMA Style

Demertzis K, Tsiotas D, Magafas L. Modeling and Forecasting the COVID-19 Temporal Spread in Greece: An Exploratory Approach Based on Complex Network Defined Splines. International Journal of Environmental Research and Public Health. 2020; 17(13):4693. https://doi.org/10.3390/ijerph17134693

Chicago/Turabian Style

Demertzis, Konstantinos, Dimitrios Tsiotas, and Lykourgos Magafas. 2020. "Modeling and Forecasting the COVID-19 Temporal Spread in Greece: An Exploratory Approach Based on Complex Network Defined Splines" International Journal of Environmental Research and Public Health 17, no. 13: 4693. https://doi.org/10.3390/ijerph17134693

Find Other Styles

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Metrics

Abstract views Pdf views Html views

Article Menu

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

Abstract

Share and Cite

Article Metrics

Article Access Statistics

Article Access Map by Country/Region

Further Information

Guidelines

MDPI Initiatives

Follow MDPI