Affiliations: [a] Department of Family Medicine, Taichung Veterans General Hospital, Taichung City, Taiwan
| [b] Graduate Institute of Microbiology and Public Health, College of Veterinary Medicine, National Chung-Hsing University, Taichung, Taiwan
| [c] Computer & Communication Center, Taichung Veterans General Hospital
| [d] Department of Industrial Engineering and Enterprise Information, Tunghai University, Taichung City, Taiwan
| [e] School of Accounting, College of Business, University of Economics Ho Chi Minh City, Vietnam
| [f] Department of Information Management, Lunghwa University of Science and Technology, Taoyuan
Correspondence:
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Corresponding author. Kuo-Ping Lin, Department of Industrial Engineering and Enterprise Information, Box 985, Tunghai University, Taichung, Taiwan; School of Accounting, College of Business, University of Economics Ho Chi Minh city, Vietnam. E-mails: [email protected]; [email protected].
Abstract: 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.
