预测索马里结核病发病率:单一和混合时间序列模型的比较分析
Hana Mahdi Dahir1, Ayan Husein Korse1, Farduus Ibrahim Mohamed1,2
1School of Postgraduate Studies and Research Amoud University Amoud Valley Borama Somalia.
Health science reports
|February 27, 2026
概括
与单个模型相比,混合时间序列模型,如ARIMA-ETS-TBATS,显著改善了索马里结核病 (TB) 发病率预测. 准确的结核病趋势预测对于有效的公共卫生战略至关重要.
科学领域:
- 流行病学 流行病学
- 公共卫生 公共卫生
- 时间序列分析时间序列分析
背景情况:
- 结核病 (TB) 是一个主要的全球健康问题.
- 准确的预测对于结核病控制和预防至关重要.
- 索马里面临着与结核病相关的重大公共卫生挑战.
研究的目的:
- 评估单一和混合时间序列模型,用于预测索马里结核病发病率.
- 为了比较各种预测方法的性能.
- 确定最准确的模型来预测未来的结核病趋势.
主要方法:
- 利用了世界银行的每年结核病发病率数据 (2000-2022年).
- 训练和评估了14个时间序列模型:5个单一 (ARIMA,ETS,TBATS,Theta,NNAR) 和9个混合组合.
- 通过使用Theil的U统计,MAPE,SMAPE和RMSE来评估模型性能.
主要成果:
- 在单个模型中,TBATS模型表现最好.
- 混合型ARIMA-ETS-TBATS模型表现出比其他配置更高的精度.
- 混合建模方法通常会提高预测精度.
结论:
- 混合时间序列模型为索马里结核病发病率预测提供了卓越的准确性.
- 准确的预测支持知情的公共卫生决策和有针对性的干预措施.
- 这项研究强调了对结核病控制努力的先进建模的价值.
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