Optimal adaptive neuro-fuzzy inference system with biogeography-based optimization for numbers of COVID-19 vaccination prediction

• Published in: Journal of intelligent & fuzzy systems 2023-08, Vol.45 (3), p.4639-4650
• Main Authors: Hsu, Pi-Shan, Huang, Chien-Chung, Sung, Wei-Ying, Tsai, Han-Ying, Wu, Zih-Xin, Lin, Ting-Yu, Lin, Kuo-Ping, Liu, Gia-Shie
• Format: Article
• Language: English
• Subjects: Adaptive systems; Artificial neural networks; Biogeography; COVID-19 vaccines; Forecasting; Fuzzy logic; General regression neural networks; Health care facilities; Immunization; Inference; Mathematical models; Optimization; Prediction models; Support vector machines
• ISSN: 1064-1246; 1875-8967
• DOI: 10.3233/JIFS-231165

This study attempts to develop the adaptive neuro-fuzzy inference system (ANFIS) with biogeography-based optimization (BBO) (ANFIS-BBO) for a case study of the actual number of COVID-19 vaccinations in a medical center, considering the variables of the date and time of vaccination, the brand of vaccine, and the number of open appointments on the government network platform in Taiwan. The COVID-19 has brought about a great burden on the health and economy of the world since the end of 2019. Many scholars have proposed a prediction model for the number of confirmed cases and deaths. However, there is still a lack of research in the prediction model for mass vaccination. In this study, ANFIS-BBO is developed to predict the number of COVID-19 vaccination, and three other forecasting models, support vector machines (SVM), least-square support vector machines (LSSVM) and general regression neural network (GRNN) are employed for forecasting the same data sets. Empirical results show that the ANFIS-BBO with trapezoidal membership function model can achieve better performance than other methods and provide robust predictions for the actual number of COVID-19 mass vaccination.