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预测马来西亚新结核病例:使用自回归集成移动平均 (ARIMA) 模型进行时间序列研究

Mohd Ariff Ab Rashid¹, Rafdzah Ahmad Zaki¹, Wan Rozita Wan Mahiyuddin²

¹Department of Social and Preventive Medicine, Faculty of Medicine, Universiti Malaya, Kuala Lumpur, MYS.

|October 9, 2023

概括

这项研究成功开发了一种自行回归集成移动平均 (ARIMA) 模型,用于预测马来西亚的结核病 (TB) 病例. 该模型为结核病流行病监测提供了一个可靠,低成本的早期预警系统.

科学领域:

流行病学流行病学
生物统计学生物统计学
公共卫生监督公共卫生监督

背景情况:

自行回归集成移动平均 (ARIMA) 模型是传染病预测和公共卫生监测的宝贵工具.
准确预测像结核病 (TB) 这样的传染病对于有效的公共卫生干预至关重要.

研究的目的:

开发和验证一个ARIMA模型,用于预测马来西亚的新结核病例.
根据历史数据,预测2019年的每月新结核病例.

主要方法:

利用马来西亚2013年1月至2018年12月新增结核病例的时间序列数据.
应用了Box-Jenkins ARIMA方法来确定最佳模型.
通过使用平均绝对百分比误差 (MAPE) 和静止R平方来评估模型有效性,并通过Ljung-Box测试进行验证.

主要成果:

确定ARIMA (2,1,1) ((0,1,0) ₁₂模型是最合适的,实现了6.762.0的低MAPE.
分析显示,结核病新发病例有明显的季节性模式,高峰期为3月和12月.
该模型解释了结核病例变异的55.8%,使用非显著的Ljung-Box测试 (p=0.356).

结论:

ARIMA模型提供了一种简单,准确和具有成本效益的预测工具,用于监测马来西亚的结核病流行.
该模型为公共卫生官员提供了宝贵的六个月的预警,以管理季节性结核病趋势.
这种方法增强了可预测季节性模式的传染病早期预警监测系统.

关键词:

早期预警监测监测的早期预警监测马来西亚马来西亚萨里玛模型模型时间序列时间序列结核病是一种肺结核病.

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