A Hybrid Model based on mBA-ANFIS for COVID-19 Confirmed Cases Prediction and Forecast

• Published in: Journal of the Institution of Engineers (India). Series B, Electrical Engineering, Electronics and telecommunication engineering, Computer engineering Vol. 102; no. 6; pp. 1123 - 1136
• Main Authors: Saif, Sohail, Das, Priya, Biswas, Suparna
• Format: Journal Article
• Language: English
• Published: New Delhi Springer India 01.12.2021; Springer Nature B.V
• Subjects: Adaptive systems; Algorithms; Artificial neural networks; Communications Engineering; Coronaviruses; COVID-19; Engineering; Fuzzy logic; Heuristic methods; Mathematical models; Mean square errors; Mutation; Networks; Original Contribution; Root-mean-square errors; Search algorithms; Viral diseases; United States > US; India; COVID-19; MAE; Adaptive neuro-fuzzy inference system (ANFIS); Forecast; MAPE; NRMSE; Mutation-based Bees Algorithm (mBA); RMSE; India
• ISSN: 2250-2106; 2250-2114; 2250-2114
• DOI: 10.1007/s40031-021-00538-0

In India, the first confirmed case of novel corona virus (COVID-19) was discovered on January 30, 2020. The number of confirmed cases is increasing day by day, and it crossed 21,53,010 on August 9, 2020. In this paper, a hybrid forecasting model has been proposed to determine the number of confirmed cases for upcoming 10 days based on the earlier confirmed cases found in India. The proposed model is based on adaptive neuro-fuzzy inference system (ANFIS) and mutation-based Bees Algorithm (mBA). The meta-heuristic Bees Algorithm (BA) has been modified applying 4 types of mutation, and mutation-based Bees Algorithm (mBA) is applied to enhance the performance of ANFIS by optimizing its parameters. Proposed mBA-ANFIS model has been assessed using COVID-19 outbreak dataset for India and USA, and the number of confirmed cases in the next 10 days in India has been forecasted. Proposed mBA-ANFIS model has been compared to standard ANFIS model as well as other hybrid models such as GA-ANFIS, DE-ANFIS, HS-ANFIS, TLBO-ANFIS, FF-ANFIS, PSO-ANFIS and BA-ANFIS. All these models have been implemented using Matlab 2015 with 10 iterations each. Experimental results show that the proposed model has achieved better performance in terms of Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), Mean absolute error (MAE) and Normalized Root Mean Square Error (NRMSE). It has obtained RMSE of 1280.24, MAE of 685.68, MAPE of 6.24 and NRMSE of 0.000673 for India Data. Similarly, for USA the values are 4468.72, 3082.07, 6.1, and 0.000952 for RMSE, MAE, MAPE, and NRMSE, respectively.