An Artificial Neural Network Method to Predict the COVID-19 Cases in Iran
الموضوعات :Meisam Shamsi 1 , Reza Babazadeh 2 , Mohsen Varmazyar 3
1 - Faculty of Engineering, Urmia University, Urmia, West Azerbaijan Province, Iran
2 - Faculty of Engineering, Urmia University, Urmia, West Azerbaijan Province, Iran
3 - Department of Industrial Engineering, Sharif University of Technology, Tehran, Iran
الکلمات المفتاحية: predict, Artificial Neural Network, Radial Basis Function, COVID-19, Adaptive-network-based fuzzy inference system,
ملخص المقالة :
The sudden emergence of a Coronavirus and its rapid spread due to the globalization factors, especially the airline network, provoked the reaction of countries. Governments attempt to use all available means, including prediction methods, to control the spread of the Coronavirus. In this article, we have developed various models based on artificial neural networks, including multi-layer perceptron, radial basis function, and adaptive-network-based fuzzy inference system with different learning algorithms, transfer functions, membership functions, hidden layers, hidden neurons, and kernels. We have identified five factors influencing the Coronavirus outbreak based on the Pearson correlation coefficient approach. These factors are gasoline consumption, internet pressure, number of wedding ceremonies, online transactions, and mask consumption. The accuracy of the developed models is identified by calculating three types of statistical errors, including root mean square error, mean absolute error, and mean absolute percentage error. The results show that the radial basis function model predicts the number of Covid-19 cases for the one month (mid-term) with an accuracy of over 97%. This study provides an efficient approach to predict the number of COVID-19 cases which help policymakers to make strategic decisions, including closing borders, designing supply chains for medical and health equipment, and enacting new laws.
Abbas, A. K., Al-haideri, N. A., & Bashikh, A. A. (2019). Implementing artificial neural networks and support vector machines to predict lost circulation. Egyptian Journal of Petroleum, 28(4), 339–347.
Acheampong, A. O., & Boateng, E. B. (2019). Modelling carbon emission intensity: Application of artificial neural network. Journal of Cleaner Production, 225, 833–856.
Alakus, T. B., & Turkoglu, I. (2020). Comparison of deep learning approaches to predict COVID-19 infection. Chaos, Solitons & Fractals, 140, 110120.
Al-Shawwa, M. O., & Abu-Naser, S. S. (2019). Predicting birth weight using artificial neural network.
Barto, A. G. (1984). Neuron-like adaptive elements that can solve difficult learning control-problems. Behavioural Processes, 9(1).
Barua, S. (2021). Understanding coronanomics: The economic implications of the COVID-19 pandemic. The Journal of Developing Areas, 55(3), 435–450.
Bickley, S. J., Chan, H. F., Skali, A., Stadelmann, D., & Torgler, B. (2021). How does globalization affect COVID-19 responses? Globalization and Health, 17(1), 1–19.
Chen, Y., Yu, G., Long, Y., Teng, J., You, X., Liao, B.-Q., & Lin, H. (2019). Application of radial basis function artificial neural network to quantify interfacial energies related to membrane fouling in a membrane bioreactor. Bioresource Technology, 293, 122103.
Emamgholizadeh, S., Moslemi, K., & Karami, G. (2014). Prediction the groundwater level of bastam plain (Iran) by artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS). Water Resources Management, 28(15), 5433–5446.
Guhathakurata, S., Saha, S., Kundu, S., Chakraborty, A., & Banerjee, J. S. (2022). A new approach to predict COVID-19 using artificial neural networks. In Cyber-Physical Systems (pp. 139–160). Elsevier.
Hartman, E. J., Keeler, J. D., & Kowalski, J. M. (1990). Layered neural networks with Gaussian hidden units as universal approximations. Neural Computation, 2(2), 210–215.
Jackson, J. K. (2021). Global economic effects of COVID-19. Congressional Research Service.
Jang, J.-S., & Sun, C.-T. (1995). Neuro-fuzzy modeling and control. Proceedings of the IEEE, 83(3), 378–406.
Kong, F. G., Li, Q., & Zhang, W. J. (1999). An artificial neural network approach to mechanism kinematic chain isomorphism identification. Mechanism and Machine Theory, 34(2), 271–283.
Kouser, R. R., Manikandan, T., & Kumar, V. V. (2018). Heart disease prediction system using artificial neural network, radial basis function and case based reasoning. Journal of Computational and Theoretical Nanoscience, 15(9–10), 2810–2817.
Li, Y., Wang, X., Sun, S., Ma, X., & Lu, G. (2017). Forecasting short-term subway passenger flow under special events scenarios using multiscale radial basis function networks. Transportation Research Part C: Emerging Technologies, 77, 306–328.
Mirrashid, M. (2014). Earthquake magnitude prediction by adaptive neuro-fuzzy inference system (ANFIS) based on fuzzy C-means algorithm. Natural Hazards, 74(3), 1577–1593.
Mitchell, T. M., & Mitchell, T. M. (1997). Machine learning (Vol. 1, Issue 9). McGraw-hill New York.
Mohandes, M., Rehman, S., & Rahman, S. M. (2011). Estimation of wind speed profile using adaptive neuro-fuzzy inference system (ANFIS). Applied Energy, 88(11), 4024–4032.
Nayak, P. C., Sudheer, K. P., Rangan, D. M., & Ramasastri, K. S. (2004). A neuro-fuzzy computing technique for modeling hydrological time series. Journal of Hydrology, 291(1–2), 52–66.
Sadek, R. M., Mohammed, S. A., Abunbehan, A. R. K., Ghattas, A. K. H. A., Badawi, M. R., Mortaja, M. N., Abu-Nasser, B. S., & Abu-Naser, S. S. (2019). Parkinson’s disease prediction using artificial neural network.
Takagi, T., & Sugeno, M. (1983). Derivation of fuzzy control rules from human operator’s control actions. IFAC Proceedings Volumes, 16(13), 55–60.
Tatar, A., Nasery, S., Bahadori, A., Bahadori, M., Najafi-Marghmaleki, A., & Barati-Harooni, A. (2016). Implementing radial basis function neural network for prediction of surfactant retention in petroleum production and processing industries. Petroleum Science and Technology, 34(11–12), 992–999.
