使用气候变量预测哥伦比亚科尔多瓦登革热发病率的规范性时间建模方法
Ever Medina1, Myladis R Cogollo1, Gilberto González-Parra2
1Departamento de Matematicas y Estadistica, Universidad de Cordoba, Monteria 230002, Colombia.
Mathematical biosciences and engineering : MBE
|January 14, 2025
概括
通过使用降水和湿度等气候数据,改善了哥伦比亚登革热发病率的预测. 将时间序列模型与这些气候因素调整,可以提高预测的准确性.
科学领域:
- 环境科学 环境科学
- 流行病学 流行病学
- 时间序列分析时间序列分析
背景情况:
- 登革热对公众健康构成重大挑战,特别是在热带地区.
- 众所周知,降水和湿度等气候变量会影响登革热传播动态.
- 准确预测登革热发病率对于有效的公共卫生干预至关重要.
研究的目的:
- 开发和评估用于预测哥伦比亚科尔多瓦登革热发病率的建模策略.
- 评估气候变量 (降水和相对湿度) 对登革热预测模型的影响.
- 为了确定最佳的模型配置来预测登革热率.
主要方法:
- 使用了一个带有异源变量的季节性自回归集成移动平均线 (SARIMAX) 模型.
- 采用交叉验证方法,使用三个训练集-测试集配置 (182-13,189-6,192-3).
- 2007年至2021年的登革热病例,降水和相对湿度的历史数据.
主要成果:
- 证实了降水,相对湿度和登革热发病率之间的关系.
- 证明当时间序列模型被调整为外源气候变量时,模型性能会得到改善.
- 确定了189-6和192-3的配置,为训练和测试数据提供了最一致的结果.
结论:
- 气候变量大大提高了登革热发病率预测模型的准确性.
- 调整模型以预测的外源变量来改善未来登革热率的预测.
- 特定的模型配置 (189-6和192-3) 为研究区域的登革热预测提供了强大的性能.
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