Open Access

ARTICLE

A Novel Heuristic Algorithm for the Modeling and Risk Assessment of the COVID-19 Pandemic Phenomenon

Panagiotis G. Asteris^1,*, Maria G. Douvika¹, Chrysoula A. Karamani¹, Athanasia D. Skentou¹, Katerina Chlichlia², Liborio Cavaleri³, Tryfon Daras⁴, Danial J. Armaghani⁵, Theoklis E. Zaoutis⁶

1 Computational Mechanics Laboratory, School of Pedagogical and Technological Education, Athens, 15122, Greece
2 Laboratory of Molecular Immunology, Department of Molecular Biology and Genetics, Democritus University of Thrace, Alexandroupolis, Greece
3 Department of Civil, Environmental, Aerospace and Materials Engineering, University of Palermo, Palermo, Italy
4 School of Environmental Engineering, Technical University of Crete, Chania, Greece
5 Department of Civil Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur, Malaysia
6 Division of Infectious Diseases, The Children’s Hospital of Philadelphia, Perelman School of Medicine, University of Pennsylvania, PHL, USA

* Corresponding Authors: Panagiotis G. Asteris. Email: ;

(This article belongs to the Special Issue: Soft Computing Techniques in Materials Science and Engineering)

Computer Modeling in Engineering & Sciences 2020, 125(2), 815-828. https://doi.org/10.32604/cmes.2020.013280

Received 31 July 2020; Accepted 17 August 2020; Issue published 12 October 2020

Abstract

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.

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