A Computational Intelligence Approach to Detect Future Trends of COVID-19 in France by Analyzing Chinese Data

Document Type : Original Research

Authors
Z. Sazvar
M. Tanhaeean
S.S. Aria
A. Akbari
S.F. Ghaderi
S.H. Iranmanesh
Department of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran
Abstract
Aims: Due to the terrible effects of 2019 novel coronavirus (COVID-19) on health systems and the global economy, the necessity to study future trends of the virus outbreaks around the world is seriously felt. Since geographical mobility is a risk factor of the disease, it has spread to most of the countries recently. It, therefore, necessitates to design a decision support model to 1) identify the spread pattern of coronavirus and, 2) provide reliable information for the detection of future trends of the virus outbreaks.

Materials & Methods: The present study adopts a computational intelligence approach to detect the possible trends in the spread of 2019-nCoV in China for a one-month period. Then, a validated model for detecting future trends in the spread of the virus in France is proposed. It uses ANN (Artificial Neural Network) and a combination of ANN and GA (Genetic Algorithm), PSO (Particle Swarm Optimization), and ICA (Imperialist Competitive Algorithm) as predictive models.

Findings: The models work on the basis of data released from the past and the present days from WHO (World Health Organization). By comparing four proposed models, ANN and GA-ANN achieve a high degree of accuracy in terms of performance indicators.

Conclusion: The models proposed in the present study can be used as decision support tools for managing and controlling of 2019-nCoV outbreaks.

Keywords

Subjects

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Health Education and Health Promotion
Volume 8, Issue 3
Autumn 2020
Pages 107-113