通过使用先进的theta方法估计结核病发病率的时间流行病学趋势
Yongbin Wang1, Bingjie Zhang1, Chenlu Xue1
1Department of Epidemiology and Health Statistics, School of Public Health, Xinxiang Medical University, Xinxiang, People's Republic of China.
The American journal of tropical medicine and hygiene
|June 18, 2024
概括
优化的Theta模型 (OTM) 准确预测结核病 (TB) 发病率趋势,优于其他模型. 这种先进的方法有效地跟踪结核病流行病学,并为公共卫生战略提供信息.
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) 仍然是一个重大的全球卫生挑战,需要准确的流行病学趋势分析.
- 了解结核病发病率的时间模式对于有效的疾病控制和资源分配至关重要.
研究的目的:
- 评估先进的Theta方法,特别是优化Theta模型 (OTM) 在评估结核病的时间流行病学趋势方面的性能.
- 将OTM与其他已建立的时间序列模型进行结核病发病率预测的比较.
主要方法:
- 利用中国四个省份 (天津,黑龙江,湖北,广西) 2005年1月至2019年12月的结核病发病率数据.
- 采用并比较多种建模方法:季节性自回归集成移动平均 (SARIMA),Theta,标准Theta模型 (STM),动态优化Theta模型 (DOTM),动态标准Theta模型 (DSTM) 和优化Theta模型 (OTM).
- 使用诸如平均绝对偏差,平均绝对百分比误差和根平均平方误差等指标评估模型性能.
主要成果:
- 结核病发病率表现为季节性,春季和冬季达到顶峰,从2005年至2019年期间观察到的一般下降趋势.
- 优化的Theta模型 (OTM) 在预测天津和其他省份的结核病发病率方面,在各种统计指标上始终优于所有其他模型.
- 在捕捉结核病流行病学的时间动态方面,OTM表现出卓越的准确性.
结论:
- 优化的Theta模型 (OTM) 是一个高效和有益的工具,用于估计结核病的时间流行病学趋势.
- 这些发现支持使用OTM进行更精确的结核病监测和预测,帮助公共卫生干预.
- 准确预测结核病趋势对于积极的疾病管理和预防工作至关重要.
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