Volume 8, Issue 3 (2020)
Health Educ Health Promot 2020, 8(3): 107-113 |
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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-5-42118-en.html
URL: http://hehp.modares.ac.ir/article-5-42118-en.html
1- Department of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran , sazvar@ut.ac.ir
2- Department of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran
2- Department of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran
Abstract: (1835 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.
Article Type: Original Research |
Subject:
Health Care
Received: 2020/04/16 | Accepted: 2020/07/4 | Published: 2020/09/20
Received: 2020/04/16 | Accepted: 2020/07/4 | Published: 2020/09/20
References
1. Akhmetzhanov AR, Mizumoto K, Jung SM, Linton NM, Omori R, Nishiura H. Epidemiological characteristics of novel coronavirus infection: A statistical analysis of publicly available case data. medRxiv. 2020. [Link]
2. European Centre for Disease Prevention and Control data. Geographical distribution of 2019-nCov cases [Internet]. Unknown Publisher city: Unknown Publisher; Unknown Year [cited 2020 January 2]. Available from: Not Found. [Link]
3. Huang C, Wang Y, Li X, Ren L, Zhao J, Hu Y, et al. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet. 2020;395(10223):497-506. [Link] [DOI:10.1016/S0140-6736(20)30183-5]
4. Mühlenbein H, Mahnig T. FDA-A scalable evolutionary algorithm for the optimization of additively decomposed functions. Evol Comput. 1999;7(4):353-76. [Link] [DOI:10.1162/evco.1999.7.4.353]
5. Kennedy J, Eberhart R. Particle swarm optimization. Proceedings of ICNN'95-International Conference on Neural Networks, 1995 27 November-1 December, Perth, Australia. Piscataway: IEEE; 1995. [Link]
6. Atashpaz-Gargari E, Lucas C. Imperialist competitive algorithm: An algorithm for optimization inspired by imperialistic competition. IEEE Congress On Evolutionary Computation, 2007 September 25-28, Singapore, Singapore. Piscataway: IEEE; 2007. [Link] [DOI:10.1109/CEC.2007.4425083]
7. Santosh KC. AI-driven tools for coronavirus outbreak: Need of active learning and cross-population train/test models on multitudinal/multimodal data. J Med Syst. 2020;44(5):93. [Link] [DOI:10.1007/s10916-020-01562-1]
8. Long JB, Ehrenfeld JM. The role of augmented intelligence (AI) in detecting and preventing the spread of novel coronavirus. J Med Syst. 2020;44:59. [Link] [DOI:10.1007/s10916-020-1536-6]
9. Hwang RC, Huang HC, Hsieh JG. Short-term power load forecasting by neural network with stochastic back-propagation learning algorithm. IEEE Power Engineering Society Winter Meeting, 2000 January 23-27, Singapore, Singapore. Piscataway: IEEE; 2000. [Link]
10. Jhee WC, Lee JK. Performance of neural networks in managerial forecasting. Intell Syst Account Financ Manag. 1993;2(1):55-71. [Link] [DOI:10.1002/j.1099-1174.1993.tb00034.x]
11. Hwarng HB. Insights into neural-network forecasting of time series corresponding to ARMA (p, q) structures. Omega. 2001;29(3):273-89. [Link] [DOI:10.1016/S0305-0483(01)00022-6]
12. Kohzadi N, Boyd MS, Kermanshahi B, Kaastra I. A comparison of artificial neural network and time series models for forecasting commodity prices. Neurocomputing. 1996;10(2):169-81. [Link] [DOI:10.1016/0925-2312(95)00020-8]
13. Tang Z, De Almeida Ch, Fishwick PA. Time series forecasting using neural networks vs. Box-Jenkins methodology. Simulation. 1991;57(5):303-10. [Link] [DOI:10.1177/003754979105700508]
14. Sibanda W. Artificial neural networks-a review of applications of neural networks in the modeling of HIV epidemic. Int J Comput Appl. 2012;44(16):1-9. [Link]
15. Keltch B, Lin Y, Bayrak C. Comparison of AI techniques for prediction of liver fibrosis in hepatitis patients. J Med Syst. 2014;38(8):60. [Link] [DOI:10.1007/s10916-014-0060-y]
16. Aburas HM, Cetiner BG, Sari M. Dengue confirmed-cases prediction: A neural network model. Expert Syst Appl. 2010;37(6):4256-60. [Link] [DOI:10.1016/j.eswa.2009.11.077]
17. Mustaffa Z, Yusof Y. A comparison of normalization techniques in predicting dengue outbreak. International Conference on Business and Economics Research, 2011 October 21-23, Kuala Lumpur, Malaysia. IACSIT Press; 2011. [Link]
18. Nishanthi PH, Perera AA, Wijekoon HP. Prediction of dengue outbreaks in Sri Lanka using artificial neural networks. Int J Comput Appl. 2014;101(15):1. [Link] [DOI:10.5120/17760-8862]
19. Faisal T, Taib MN, Ibrahim F. Neural network diagnostic system for dengue patients risk classification. J Med Syst. 2012;36(2):661-76. [Link] [DOI:10.1007/s10916-010-9532-x]
20. Majumdar A, Debnath T, Sood SK, Baishnab KL. Kyasanur forest disease classification framework using novel extremal optimization tuned neural network in fog computing environment. J Med Syst. 2018;42(10):187. [Link] [DOI:10.1007/s10916-018-1041-3]
21. Saadah LM, Chedid FD, Sohail MR, Nazzal YM, Al Kaabi MR, Rahmani AY. Palivizumab prophylaxis during nosocomial outbreaks of respiratory syncytial virus in a neonatal intensive care unit: Predicting effectiveness with an artificial neural network model. Pharmacother J Hum Pharmacol Drug Ther. 2014;34(3):251-9. [Link] [DOI:10.1002/phar.1333]
22. Bashir ZA, El-Hawary ME. Applying wavelets to short-term load forecasting using PSO-based neural networks. IEEE Trans Power Syst. 2009;24(1):20-7. [Link] [DOI:10.1109/TPWRS.2008.2008606]
23. Whitley D. A genetic algorithm tutorial. Stat Comput. 1994;4(2):65-85. [Link] [DOI:10.1007/BF00175354]
24. Hassan MR. A combination of hidden Markov model and fuzzy model for stock market forecasting. Neurocomputing. 2009;72(16-18):3439-46. [Link] [DOI:10.1016/j.neucom.2008.09.029]
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