Graduate Institute of Business Administration, Fu Jen Catholic University, New Taipei City 242062, Taiwan.
Artificial Intelligence Development Center, Fu Jen Catholic University, New Taipei City 242062, Taiwan.
Int J Environ Res Public Health. 2020 Jul 1;17(13):4743. doi: 10.3390/ijerph17134743.
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.
流感是一个严重的公共卫生问题，因为它可能导致急性痛苦甚至死亡、社会混乱和经济损失。有效预测流感门诊量有助于预测和预防医疗资源短缺。本研究使用流感门诊量的区域数据，提出了一种二维分层决策树方案来预测流感门诊量。台湾每周流感门诊量数据来自国家传染病统计系统，并用于实证示例。788 个数据点从 2005 年第一周开始，到 2020 年第二周结束。实证结果表明，所提出的预测方案优于五个竞争模型，能够预测一到四周的预期流感门诊量。该方案可能是一种有效且有前途的替代方案，可用于预测台湾全国流感门诊量一到四周的情况。我们的结果还表明，对于预测台湾全国流感门诊量，台北、北部和南部地区的一个和两个时间滞后信息以及区域信息具有重要意义。
