Modeling and Forecasting the COVID-19 Temporal Spread in Greece: An Exploratory Approach Based on Complex Network Defined Splines
Konstantinos Demertzis (),
Dimitrios Tsiotas () and
Lykourgos Magafas ()
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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
Lykourgos Magafas: Laboratory of Complex Systems, Department of Physics, Faculty of Sciences, International Hellenic University, Kavala Campus, 65404 St. Loukas, Greece
IJERPH, 2020, vol. 17, issue 13, 1-17
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.
Keywords: COVID-19 coronavirus pandemic; outbreak; modeling; prediction; regression splines; modularity optimization algorithm (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:gam:jijerp:v:17:y:2020:i:13:p:4693-:d:378124
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