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Ιδρυματικό Αποθετήριο [SANDBOX]
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| URI | http://purl.tuc.gr/dl/dias/75C4DB88-95FF-47D6-B85D-0630C7E089CB | - |
| Αναγνωριστικό | https://doi.org/10.32604/cmes.2020.013280 | - |
| Αναγνωριστικό | https://www.techscience.com/CMES/v125n2/40324 | - |
| Γλώσσα | en | - |
| Μέγεθος | 14 pages | en |
| Μέγεθος | 1,35 megabytes | en |
| Τίτλος | A novel heuristic algorithm for the modeling and risk assessment of the COVID-19 pandemic phenomenon | en |
| Δημιουργός | Asteris, Panagiotis G., 1964- | en |
| Δημιουργός | Douvika Maria G. | en |
| Δημιουργός | Karamani Chrysoula A. | en |
| Δημιουργός | Skentou Athanasia D. | en |
| Δημιουργός | Chlichlia, Aikaterini | en |
| Δημιουργός | Cavaleri Liborio | en |
| Δημιουργός | Daras Tryfonas | en |
| Δημιουργός | Δαρας Τρυφωνας | el |
| Δημιουργός | Armaghani Danial J. | en |
| Δημιουργός | Zaoutis, Theoklis E | en |
| Εκδότης | Tech Science Press | en |
| Περιγραφή | This article belongs to the special issue: Soft computing techniques in materials science and engineering | en |
| Περίληψη | The modeling and risk assessment of a pandemic phenomenon such as COVID-19 is an important and complicated issue in epidemiology, and such an attempt is of great interest for public health decision-making. To this end, in the present study, based on a recent heuristic algorithm proposed by the authors, the time evolution of COVID-19 is investigated for six different countries/states, namely New York, California, USA, Iran, Sweden and UK. The number of COVID-19-related deaths is used to develop the proposed heuristic model as it is believed that the predicted number of daily deaths in each country/state includes information about the quality of the health system in each area, the age distribution of population, geographical and environmental factors as well as other conditions. Based on derived predicted epidemic curves, a new 3D-epidemic surface is proposed to assess the epidemic phenomenon at any time of its evolution. This research highlights the potential of the proposed model as a tool which can assist in the risk assessment of the COVID-19. Mapping its development through 3D-epidemic surface can assist in revealing its dynamic nature as well as differences and similarities among different districts. | en |
| Τύπος | Peer-Reviewed Journal Publication | en |
| Τύπος | Δημοσίευση σε Περιοδικό με Κριτές | el |
| Άδεια Χρήσης | http://creativecommons.org/licenses/by/4.0/ | en |
| Ημερομηνία | 2021-09-27 | - |
| Ημερομηνία Δημοσίευσης | 2020 | - |
| Θεματική Κατηγορία | Algorithm | en |
| Θεματική Κατηγορία | COVID-19 | en |
| Θεματική Κατηγορία | Gaussian-function | en |
| Θεματική Κατηγορία | Heuristic model | en |
| Θεματική Κατηγορία | Pandemic trend | en |
| Θεματική Κατηγορία | Prediction | en |
| Θεματική Κατηγορία | SARS-CoV-2 | en |
| Βιβλιογραφική Αναφορά | P. G. Asteris, M. G. Douvika, C. A. Karamani, A. D. Skentou, K. Chlichlia, L. Cavaleri, T. Daras, D. J. Armaghani, and T. E. Zaoutis, “A novel heuristic algorithm for the modeling and risk assessment of the COVID-19 pandemic phenomenon,” CMES-Comp. Model. Eng. Sci., vol. 125, no. 2, pp. 815–828, 2020. doi: 10.32604/cmes.2020.013280 | en |
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