在非洲结核病的整数时间序列模型.
Oluwadare O Ojo1, Saralees Nadarajah2, Malick Kebe3
1Department of Statistics, Federal University of Technology, Akure, Nigeria.
Scientific reports
|July 15, 2023
概括
这项研究使用时间序列分析对非洲结核病 (TB) 病例进行建模. 最适合的模型预测了未来的结核病趋势,有助于该地区的公共卫生战略.
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) 是全球领先的空气传染性传染病.
- 非洲地区面临着高结核病病例负担.
- 准确预测结核病发病率对于有效控制至关重要.
研究的目的:
- 在非洲国家建模和预测新的结核病例.
- 为结核病时间序列数据确定最适合的统计模型.
- 提供2022年至2031年结核病发病率的预测.
主要方法:
- 整数时间序列分析应用于2000-2021年结核病例数据.
- 采用了20种不同的统计模型,主要是波桑分布和负二项式分布.
- 模型的选择是基于每个非洲国家最适合的数据.
主要成果:
- 该研究确定了各种非洲国家结核病发病率的最佳时间序列模型.
- 配套的模型包含了随着时间的推移的线性或二次性趋势.
- 这些选定的模型作为未来结核病例预测的基础.
结论:
- 选择的模型为了解和预测非洲结核病动态提供了一个强大的框架.
- 这些预测可以为有针对性的公共卫生干预和资源分配提供信息.
- 持续监测和建模对于该地区的结核病控制至关重要.
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