一个结合的统计和决定模型,用于预测马来西亚塞兰戈尔地区气候驱动的登革热发病率
Xinyi Lu1, Su Yean Teh2, Hock Lye Koh3
1School of Mathematical Sciences, Universiti Sains Malaysia, 11800, USM, Pulau Pinang, Malaysia.
Bulletin of mathematical biology
|May 28, 2024
概括
一个新的合模型通过将气候数据与流行病学模型相结合,准确地预测马来西亚的登革热病例. 这种方法改善了早期检测,并有助于制定有效的登革热缓解策略.
科学领域:
- 流行病学 流行病学
- 数学建模的数学建模
- 公共卫生 公共卫生
背景情况:
- 登革热病毒在马来西亚构成了重大公共卫生挑战,尽管有控制努力,病例仍在增加.
- 目前的战略重点是早期检测和媒介控制,但不足以制登革热发病率的上升.
- 众所周知,温度,湿度和降雨等气候变量通过影响蚊子载体来影响登革热传播动态.
研究的目的:
- 开发和验证一种用于预测马来西亚登革热病例的新型合数学模型.
- 将气候变量纳入登革热发病率预测模型.
- 与现有方法相比,评估模型的预测准确性和稳定性.
主要方法:
- 开发了一个合模型,将统计ARIMAX模型与决定性SI-SIR模型集成在一起.
- 该模型使用马来西亚塞兰戈尔 (2014-2019) 的每周登革热数据进行了验证.
- 气候变量 (温度,降雨量,湿度) 与通过蚊子咬率传播登革热有关.
主要成果:
- 结合模型表现出强大的预测能力,平均误差低于6%的1-2周的预测.
- 预测准确度在较长的交付时间中仍然很高,在3周内有7.06%的误差,在4周内有8.01%.
- 合型号的表现优于独立的ARIMAX型号,特别是在疫情爆发期间.
结论:
- 开发的合模型为马来西亚的登革热病例实时预测提供了一个强大的工具.
- 这种模型可以通过结合气候数据,有效预测登革热爆发,帮助公共卫生干预.
- 该模型的适应性允许在不断变化的气候条件下评估登革热缓解策略.
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