一个动态混合模型,用于有效预测结核病发病率
Jamilu Yahaya Maipan-Uku1,2,3, Nadire Cavus2,3
1Department of Computer Science, Ibrahim Badamasi Babangida University, Lapai, Nigeria.
本研究引入了一种创新的ARIMA-NARX模型,用于预测结核病 (TB) 发病率,其表现优于单个ARIMA和NARX模型. 这一进步有助于全球卫生组织规划有效的结核病控制战略.
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 公共卫生
背景情况:
- 结核病 (TB) 构成了严重的全球健康威胁,需要准确的发病率预测,以进行有效的公共卫生干预.
- 目前的预测方法可能缺乏最佳资源配置和结核病控制战略规划所需的精度.
研究的目的:
- 开发和评估一种先进的混合模型,用于预测结核病发病率.
- 将拟议的混合模型的预测精度与传统的时间序列模型进行比较.
主要方法:
- 一种混合自回归集成移动平均 (ARIMA) 和非线性自回归与外源输入 (NARX) 模型被开发用于结核病发病率预测.
- 使用标准统计指标严格评估模型性能,包括平均平方误差 (MSE),根平均平方误差 (RMSE),平均绝对误差 (MAE) 和平均绝对百分比误差 (MAPE).
主要成果:
- 结合的ARIMA-NARX模型显示出卓越的预测准确性,实现了最低的误差指标 (MSE: 0.0622,RMSE: 0.0851,MAE: 0.07520,MAPE: 0.05535).
- 单个NARX和ARIMA模型显示出明显更高的错误率,表明混合方法的效率提高.
结论:
- 开发的ARIMA-NARX模型为预测结核病发病率提供了一个更准确,更可靠的工具.
- 这种预测能力可以显著支持决策者和卫生组织,如世卫组织,在全球范围内实施积极的结核病控制和干预策略.
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