一个自行回归的集成移动平均模型用于预测小疫情 - 中国,2019年
Miaomiao Wang1, Zhuojun Jiang2, Meiying You1
1Office of Epidemiology, Chinese Center for Disease Control and Prevention, Beijing, China.
China CDC weekly
|August 18, 2023
概括
这项研究开发了一种自回归集成移动平均 (ARIMA) 模型,用于预测中国的水疫情. 该模型准确预测未来的趋势,有助于预防和控制这一不断升级的公共卫生问题的战略.
科学领域:
- 流行病学 流行病学
- 公共卫生 公共卫生
- 生物统计学 生物统计学
背景情况:
- 麻疹是中国日益严重的公共卫生问题,特别是在儿童中.
- 有效的监测和早期预警系统对于缓解和控制小疫情至关重要.
- 预测建模为公共卫生干预提供了科学基础.
研究的目的:
- 开发和验证一个自回归集成移动平均线 (ARIMA) 模型,用于预测中国的水疫情.
- 根据历史数据,预测2019年每月的水病例.
主要方法:
- 在ARIMA模型开发中利用了2005年至2018年中国每月的水疫情数据.
- 应用参数和Ljung-Box测试以确保模型的统计学意义.
- 选择了基于R平方和贝叶斯信息标准 (BIC) 值的最佳ARIMA模型.
主要成果:
- 确定ARIMA (1, 1, 1) ×(0, 1, 1) 12模型是最优的预测模型.
- 该模型显示,它很适合2019年观察到的小病例,平均相对误差为15.2%.
结论:
- 开发的ARIMA模型有效地预测了中国未来的水疫情趋势.
- 这种预测能力为提高水预防和控制策略提供了科学基准.
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