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Volume 8, Issue 3 (2020)
Health Educ Health Promot 2020, 8(3): 107-113 |
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Received: 2020/04/16 | Accepted: 2020/07/4 | Published: 2020/09/20
Received: 2020/04/16 | Accepted: 2020/07/4 | Published: 2020/09/20
How to cite this article
Sazvar Z, Tanhaeean M, Aria S, Akbari A, Ghaderi S, Iranmanesh S. A Computational Intelligence Approach to Detect Future Trends of COVID-19 in France by Analyzing Chinese Data. Health Educ Health Promot 2020; 8 (3) :107-113
URL: http://hehp.modares.ac.ir/article-4-42118-en.html
URL: http://hehp.modares.ac.ir/article-4-42118-en.html
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1- Department of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran
Abstract (3040 Views)
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.
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.
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