Forecasting Weekly Influenza Outpatient Visits Using a Two-Dimensional Hierarchical Decision Tree Scheme

Abstract

Influenza is a serious public health issue, as it can cause acute suffering and even death, social disruption, and economic loss. Effective forecasting of influenza outpatient visits is beneficial to anticipate and prevent medical resource shortages. This study uses regional data on influenza outpatient visits to propose a two-dimensional hierarchical decision tree scheme for forecasting influenza outpatient visits. The Taiwan weekly influenza outpatient visit data were collected from the national infectious disease statistics system and used for an empirical example. The 788 data points start in the first week of 2005 and end in the second week of 2020. The empirical results revealed that the proposed forecasting scheme outperformed five competing models and was able to forecast one to four weeks of anticipated influenza outpatient visits. The scheme may be an effective and promising alternative for forecasting one to four steps (weeks) ahead of nationwide influenza outpatient visits in Taiwan. Our results also suggest that, for forecasting nationwide influenza outpatient visits in Taiwan, one- and two-time lag information and regional information from the Taipei, North, and South regions are significant.