Integrating PSO-GA with ANFIS for predictive analytics of confirmed cases of COVID-19 in Iran

Document Type : IIIEC 2021


Department of Industrial Engineering, K.N Toosi University of Technology, Tehran, Iran


The first case of the unknown coronavirus, referred to as COVID-19, was detected in Wuhan, China, in late December 2019, and spread throughout China and globally. The total confirmed cases globally are rising day by day. This study proposes a novel prediction model to estimate and predict the total confirmed cases of COVID-19 in the next two days, according to Iran’s confirmed cases reported before. The proposed model is an improved adaptive neuro-fuzzy inference system (ANFIS) using a co-evolutionary PSO-GA algorithm. PSO-GA is generally used to strike a balance between exploration and exploitation capabilities enhanced further by integrating the genetic operators, i.e., mutation and crossover in the PSO algorithm. The proposed model (i.e., PSO-GA-ANFIS) thus aims to enhance the efficiency of the ANFIS model by determining ANFIS parameters using PSO-GA. The model is assessed by utilizing epidemiological data provided by John Hopkins University to forecast the COVID-19 epidemic prevalence trend of Iran in 02.20.2020-06.10.2020-time window. A comparison was also made between the proposed model and a couple of available models. The results indicated that the proposed model outperforms the other models regarding MSE, RMSE, MAPE, and R2.


Main Subjects

Ahmed, K., Ewees, A. A., El Aziz, M. A., Hassanien, A. E., Gaber, T., Tsai, P. W., & Pan, J. S. (2016, October). A hybrid krill-ANFIS model for wind speed forecasting. In International Conference on Advanced Intelligent Systems and Informatics (pp. 365-372). Springer, Cham.
Alameer, Z., Elaziz, M. A., Ewees, A. A., Ye, H., & Jianhua, Z. (2019). Forecasting copper prices using hybrid adaptive neuro-fuzzy inference system and genetic algorithms. Natural Resources Research, 28(4), 1385-1401.
Al-qaness, M. A., Ewees, A. A., Fan, H., & Abd El Aziz, M. (2020). Optimization method for forecasting confirmed cases of COVID-19 in China. Journal of Clinical Medicine, 9(3), 674.
Al-Qaness, M. A., Elaziz, M. A., & Ewees, A. A. (2018). Oil consumption forecasting using optimized adaptive neuro-fuzzy inference system based on sine cosine algorithm. IEEE Access, 6, 68394-68402.
Bagheri, A., Peyhani, H. M., & Akbari, M. (2014). Financial forecasting using ANFIS networks with quantum-behaved particle swarm optimization. Expert Systems with Applications, 41(14), 6235-6250.
Benvenuto, D., Giovanetti, M., Vassallo, L., Angeletti, S., & Ciccozzi, M. (2020). Application of the ARIMA model on the COVID-2019 epidemic dataset. Data in brief, 105340.
Catalão, J. P. D. S., Pousinho, H. M. I., & Mendes, V. M. F. (2010). Hybrid wavelet-PSO-ANFIS approach for short-term electricity prices forecasting. IEEE Transactions on Power Systems, 26(1), 137-144.
Cauchemez, S., Van Kerkhove, M. D., Riley, S., Donnelly, C. A., Fraser, C., & Ferguson, N. M. (2013). Transmission scenarios for Middle East Respiratory Syndrome Coronavirus (MERS-CoV) and how to tell them apart. Euro surveillance: bulletin European sur les maladies transmissible= European communicable disease bulletin, 18(24).
Chen, Y., Liu, Q., & Guo, D. (2020). Emerging coronaviruses: genome structure, replication, and pathogenesis. Journal of medical virology, 92(4), 418-423.
Cheng, C. H., Wei, L. Y., Liu, J. W., & Chen, T. L. (2013). OWA-based ANFIS model for TAIEX forecasting. Economic Modelling, 30, 442-448.
Cheng, C. H., & Wei, L. Y. (2010). One step-ahead ANFIS time series model for forecasting electricity loads. Optimization and Engineering, 11(2), 303-317.
Dehesh, T., Mardani-Fard, H. A., & Dehesh, P. (2020). Forecasting of covid-19 confirmed cases in different countries with arima models. medRxiv.
Eberhart, R., & Kennedy, J. (1999). A new optimizer using particle swarm optimization. In Proceedings of the 1995 Sixth International Symposium on Micro Machine and Human Science (pp. 39-43).
Ekici, B. B., & Aksoy, U. T. (2011). Prediction of building energy needs in early stage of design by using ANFIS. Expert Systems with Applications, 38(5), 5352-5358.
Elmousalami, H. H., & Hassanien, A. E. (2020). Day level forecasting for Coronavirus Disease (COVID-19) spread: analysis, modeling and recommendations. arXiv preprint arXiv:2003.07778.
Elaziz, M. A., Ewees, A. A., & Alameer, Z. (2019). Improving adaptive neuro-fuzzy inference system based on a modified salp swarm algorithm using genetic algorithm to forecast crude oil price. Natural Resources Research, 1-16.
El Aziz, M. A., Hemdan, A. M., Ewees, A. A., Elhoseny, M., Shehab, A., Hassanien, A. E., & Xiong, S. (2017, June). Prediction of biochar yield using adaptive neuro-fuzzy inference system with particle swarm optimization. In 2017 IEEE PES Power Africa (pp. 115-120). IEEE.
Ewees, A. A., El Aziz, M. A., & Elhoseny, M. (2017, July). Social-spider optimization algorithm for improving ANFIS to predict biochar yield. In 2017 8th international conference on computing, communication and networking technologies (ICCCNT) (pp. 1-6). IEEE.
Fanelli, D., & Piazza, F. (2020). Analysis and forecast of COVID-19 spreading in China, Italy and France. Chaos, Solitons & Fractals, 134, 109761.
Garg, H. (2016). A hybrid PSO-GA algorithm for constrained optimization problems. Applied Mathematics and Computation, 274, 292-305.
Ge, X. Y., Li, J. L., Yang, X. L., Chmura, A. A., Zhu, G., Epstein, J. H., ... & Zhang, Y. J. (2013). Isolation and characterization of a bat SARS-like coronavirus that uses the ACE2 receptor. Nature, 503(7477), 535-538.
Ghaffarzadegan, N., & Rahmandad, H. (2020). Simulation-based Estimation of the Spread of COVID-19 in Iran. medRxiv.
Goldberg D, E. (1989). Genetic Algorithm in Search, Optimization and Machine Learning. Reading: Addison2wes2 ley.
Grasselli, G., Pesenti, A., & Cecconi, M. (2020). Critical care utilization for the COVID-19 outbreak in Lombardy, Italy: early experience and forecast during an emergency response. Jama.
Guan, W. J., Ni, Z. Y., Hu, Y., Liang, W. H., Ou, C. Q., He, J. X., ... & Du, B. (2020). Clinical characteristics of 2019 novel coronavirus infection in China. MedRxiv.
Ho, Y. C., & Tsai, C. T. (2011). Comparing ANFIS and SEM in linear and nonlinear forecasting of new product development performance. Expert Systems with Applications, 38(6), 6498-6507.
Hu, Z., Ge, Q., Jin, L., & Xiong, M. (2020). Artificial intelligence forecasting of covid-19 in china. arXiv preprint arXiv:2002.07112.
Jang, J. S. (1993). ANFIS: adaptive-network-based fuzzy inference system. IEEE transactions on systems, man, and cybernetics, 23(3), 665-685.
Jung, S. M., Akhmetzhanov, A. R., Hayashi, K., Linton, N. M., Yang, Y., Yuan, B., ... & Nishiura, H. (2020). Real-time estimation of the risk of death from novel coronavirus (COVID-19) infection: inference using exported cases. Journal of clinical medicine, 9(2), 523.
Kennedy, J., & Eberhart, R. (1995, November). Particle swarm optimization. In Proceedings of ICNN'95-International Conference on Neural Networks (Vol. 4, pp. 1942-1948). IEEE.
Kim, S. K. (2020). AAEDM: Theoretical Dynamic Epidemic Diffusion Model and Covid-19 Korea Pandemic Cases. medRxiv.
Kumar, D. T., Soleimani, H., & Kannan, G. (2014). Forecasting return products in an integrated forward/reverse supply chain utilizing an ANFIS. International Journal of Applied Mathematics and Computer Science, 24(3), 669-682.
Li, Q., Feng, W., & Quan, Y. H. (2020). Trend and forecasting of the COVID-19 outbreak in China. Journal of Infection, 80(4), 469-496.
Liu, P., Beeler, P., & Chakrabarty, R. K. (2020). COVID-19 Progression Timeline and Effectiveness of Response-to-Spread Interventions across the United States. medRxiv.
Liu, Z., Magal, P., Seydi, O., & Webb, G. (2020). Predicting the cumulative number of cases for the COVID-19 epidemic in China from early data. arXiv preprint arXiv:2002.12298.
Lover, A. A., & McAndrew, T. (2020). Sentinel Event Surveillance to Estimate Total SARS-CoV-2 Infections, United States. medRxiv.
Lu, R., Zhao, X., Li, J., Niu, P., Yang, B., Wu, H., ... & Bi, Y. (2020). Genomic characterization and epidemiology of 2019 novel coronavirus: implications for virus origins and receptor binding. The Lancet, 395(10224), 565-574.
Mahase, E. (2020). China coronavirus: what do we know so far? BMJ 2020;368:m308 doi: 10.1136/bmj.m308
Mandal, S., Bhatnagar, T., Arinaminpathy, N., Agarwal, A., Chowdhury, A., Murhekar, M., ... & Sarkar, S. (2020). Prudent public health intervention strategies to control the coronavirus disease 2019 transmission in India: A mathematical model-based approach. Indian Journal of Medical Research, 151(2), 190.
Massonnaud, C., Roux, J., & Crépey, P. (2020). COVID-19: Forecasting short term hospital needs in France. medRxiv.
Nishiura, H., Kobayashi, T., Yang, Y., Hayashi, K., Miyama, T., Kinoshita, R., ... & Akhmetzhanov, A. R. (2020). The rate of under ascertainment of novel coronavirus (2019-nCoV) infection: estimation using Japanese passengers' data on evacuation flights.
Peng, L., Yang, W., Zhang, D., Zhuge, C., & Hong, L. (2020). Epidemic analysis of COVID-19 in China by dynamical modeling. arXiv preprint arXiv:2002.06563.
Pousinho, H. M. I., Mendes, V. M. F., & Catalão, J. P. D. S. (2012). Short-term electricity prices forecasting in a competitive market by a hybrid PSO–ANFIS approach. International Journal of Electrical Power & Energy Systems, 39(1), 29-35.
Roosa, K., Lee, Y., Luo, R., Kirpich, A., Rothenberg, R., Hyman, J. M., ... & Chowell, G. (2020). Real-time forecasts of the COVID-19 epidemic in China from February 5th to February 24th, 2020. Infectious Disease Modelling, 5, 256-263.
Russo, L., Anastassopoulou, C., Tsakris, A., Bifulco, G. N., Campana, E. F., Toraldo, G., & Siettos, C. (2020). Tracing DAY-ZERO and Forecasting the Fade out of the COVID-19 Outbreak in Lombardy, Italy: A Compartmental Modelling and Numerical Optimization Approach. medRxiv.
Shi, Z., & Fang, Y. (2020). Temporal relationship between outbound traffic from Wuhan and the 2019 coronavirus disease (COVID-19) incidence in China. medRxiv.
Sohrabi, C., Alsafi, Z., O’Neill, N., Khan, M., Kerwan, A., Al-Jabir, A., ... & Agha, R. (2020). World Health Organization declares global emergency: A review of the 2019 novel coronavirus (COVID-19). International Journal of Surgery.
Svalina, I., Galzina, V., Lujić, R., & ŠImunović, G. (2013). An adaptive network-based fuzzy inference system (ANFIS) for the forecasting: The case of close price indices. Expert systems with applications, 40(15), 6055-6063.
Tang, B., Wang, X., Li, Q., Bragazzi, N. L., Tang, S., Xiao, Y., & Wu, J. (2020). Estimation of the transmission risk of the 2019-nCoV and its implication for public health interventions. Journal of clinical medicine, 9(2), 462.
Thompson, R. N. (2020). Novel coronavirus outbreak in Wuhan, China, 2020: Intense surveillance Is vital for preventing sustained transmission in new locations. Journal of clinical medicine, 9(2), 498.
Wang, L. F., Shi, Z., Zhang, S., Field, H., Daszak, P., & Eaton, B. T. (2006). Review of bats and SARS. Emerging infectious diseases, 12(12), 1834.
Wangping, J., Ke, H., Yang, S., Wenzhe, C., Shengshu, W., Shanshan, Y., ... & Miao, L. (2020). Extended SIR prediction of the epidemics trend of COVID-19 in Italy and compared with Hunan, China. Frontiers in Medicine, 7, 169.
Wei, L. Y. (2016). A hybrid ANFIS model based on empirical mode decomposition for stock time series forecasting. Applied Soft Computing, 42, 368-376.
Wise, T., Zbozinek, T. D., Michelini, G., & Hagan, C. C. (2020). Changes in risk perception and protective behavior during the first week of the COVID-19 pandemic in the United States.
Wu, T., Ge, X., Yu, G., & Hu, E. (2020). Open-source analytics tools for studying the COVID-19 coronavirus outbreak. medRxiv.
Zhang, G., & Hu, M. Y. (1998). Neural network forecasting of the British pound/US dollar exchange rate. Omega, 26(4), 495-506.
Zhao, S., Musa, S. S., Lin, Q., Ran, J., Yang, G., Wang, W., ... & Wang, M. H. (2020). Estimating the unreported number of novel coronavirus (2019-nCoV) cases in China in the first half of January 2020: a data-driven modelling analysis of the early outbreak. Journal of clinical medicine, 9(2), 388.
Zheng, Z., Wu, K., Yao, Z., Zheng, J., & Chen, J. (2020). The prediction for development of COVID-19 in global major epidemic areas through empirical trends in China by utilizing state transition matrix model. Available at SSRN 3552835.