预测斯里兰卡每周登革热发病率:修改自回归集成移动平均线建模方法
Nilantha Karasinghe1, Sarath Peiris2, Ruwan Jayathilaka3
1Teaching Hospital, Nagoda, Kalutara, Sri Lanka.
PloS one
|March 8, 2024
概括
在斯里兰卡准确的登革热预测对公共卫生至关重要. 一个自回归集成移动平均 (ARIMA) 模型准确地预测了科伦坡每周的登革热病例,帮助疾病控制工作.
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:
- 登革热在斯里兰卡是一个重大的公共卫生挑战,需要有效的监测和控制策略.
- 准确的发病率预测对于积极的疾病管理和资源分配至关重要.
研究的目的:
- 开发和验证一个统计模型,用于预测斯里兰卡科伦坡地区每周发生的登革热病例.
- 评估自回归集成移动平均线 (ARIMA) 模型对于登革热发病率预测的适用性.
主要方法:
- 从2015年1月到2020年8月的每周登革热数据用于模型开发.
- 选择并校准了一种ARIMA (2,1,0) 模型,其自回归元件为16级.
- 使用独立数据验证了模型,并评估了随机性,恒定方差和正常性的剩余诊断.
主要成果:
- 选择的ARIMA模型显示了预测和观察到的登革热发病率之间的密切匹配.
- 模型性能使用参数显著性,AIC和SBIC标准进行评估.
- 剩余分析证实了该模型的统计假设,表明其适合预测.
结论:
- 开发的ARIMA模型为科伦坡登革热监测和控制的公共卫生决策者提供了有价值的工具.
- 外部因素,如COVID-19的干扰,可能会影响预测的准确性,突出了对自适应式监控系统的需求.
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