Modeling and forecasting the covid-19 temporal spread in Greece: An exploratory approach based on complex network defined splines
dc.creator | Demertzis K., Tsiotas D., Magafas L. | en |
dc.date.accessioned | 2023-01-31T07:53:33Z | |
dc.date.available | 2023-01-31T07:53:33Z | |
dc.date.issued | 2020 | |
dc.identifier | 10.3390/ijerph17134693 | |
dc.identifier.issn | 16617827 | |
dc.identifier.uri | http://hdl.handle.net/11615/73210 | |
dc.description.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. © 2020 by the authors. Licensee MDPI, Basel, Switzerland. | en |
dc.language.iso | en | en |
dc.source | International Journal of Environmental Research and Public Health | en |
dc.source.uri | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85087403713&doi=10.3390%2fijerph17134693&partnerID=40&md5=7fb4d8d195bb6e634cc11af04b65a4a0 | |
dc.subject | COVID-19 | en |
dc.subject | decision making | en |
dc.subject | disease spread | en |
dc.subject | forecasting method | en |
dc.subject | management | en |
dc.subject | modeling | en |
dc.subject | public health | en |
dc.subject | respiratory disease | en |
dc.subject | viral disease | en |
dc.subject | adult | en |
dc.subject | aged | en |
dc.subject | Article | en |
dc.subject | clinical decision making | en |
dc.subject | conceptual framework | en |
dc.subject | coronavirus disease 2019 | en |
dc.subject | cross-sectional study | en |
dc.subject | Greece | en |
dc.subject | health care planning | en |
dc.subject | human | en |
dc.subject | major clinical study | en |
dc.subject | mathematical model | en |
dc.subject | prediction | en |
dc.subject | public health service | en |
dc.subject | virus detection | en |
dc.subject | virus virulence | en |
dc.subject | Betacoronavirus | en |
dc.subject | Coronavirus infection | en |
dc.subject | forecasting | en |
dc.subject | isolation and purification | en |
dc.subject | pandemic | en |
dc.subject | public health | en |
dc.subject | spatiotemporal analysis | en |
dc.subject | virus pneumonia | en |
dc.subject | Greece | en |
dc.subject | Coronavirus | en |
dc.subject | Betacoronavirus | en |
dc.subject | Coronavirus Infections | en |
dc.subject | Forecasting | en |
dc.subject | Greece | en |
dc.subject | Humans | en |
dc.subject | Pandemics | en |
dc.subject | Pneumonia, Viral | en |
dc.subject | Public Health | en |
dc.subject | Spatio-Temporal Analysis | en |
dc.subject | MDPI AG | en |
dc.title | Modeling and forecasting the covid-19 temporal spread in Greece: An exploratory approach based on complex network defined splines | en |
dc.type | journalArticle | en |
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