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应用多重线性回归模型和长短期记忆与分区模型,以基于气候变量预测马来西亚塞兰戈的登革热病例

Xinyi Lu¹, Su Yean Teh¹, Chai Jian Tay²

¹School of Mathematical Sciences, Universiti Sains Malaysia, 11800, USM, Pulau Pinang, Malaysia.

Infectious Disease Modelling

|November 19, 2024

概括

这项研究为马来西亚的塞兰戈尔开发了一种先进的登革热预测模型,整合了气候数据,以预测病例的时间长达60周. 这种新的方法增强了公众健康的准备,以对抗这种重要的蚊子传播疾病.

科学领域:

流行病学和公共卫生.
环境科学与气候变化
数学建模和计算科学数学建模和计算科学

背景情况:

登革热仍然是马来西亚的主要公共卫生挑战,由于缺乏特定治疗方法,需要改进预警系统.
有效的登革热控制依赖于公共卫生干预措施的及时和战略部署,强调需要准确的预测模型.

研究的目的:

通过将气候变量整合到预测模型中,开发一个全面和新的框架来预测马来西亚塞兰戈州的登革热病例.
建立关键气候变量 (温度,湿度,降雨) 和蚊虫咬伤率之间的强有力的关系,以改善登革热发病率预测.

主要方法:

结合多线性回归 (MLR),长短期记忆 (LSTM) 和易受感染-感染-恢复 (SI-SIR) 人类宿主模型的整体建模方法被采用.
气候变量被用来预测蚊子咬率,然后向SI-SIR模型提供预测登革热发病率的信息.
拟议模型的性能与其他方法进行了评估,并在不同的时间段进行了验证,包括运动后控制命令 (MCO).

主要成果:

与三种替代方法相比,开发的整体模型显示出优异的预测性能,在60周的预测时间范围内,平均绝对百分比误差 (MAPE) 为13.97%.
扩展验证显示,预测准确度始终令人满意,MAPE在随后的时期从13.12%到17.09%不等.
该方法成功地扩大了塞兰戈尔登革热预测的时间范围,为公共卫生规划提供了有价值的工具.

结论:

这种新的框架整合了气候变量和先进的建模技术,为塞兰哥尔的登革热病例提供了强大和扩展的时间预测能力.
这项研究为该领域做出了重大贡献,通过提高登革热爆发的预测准确度和预测,帮助积极的公共卫生战略.
在不同时期的验证表现表明该模型的稳定性和在登革热监测和控制计划中广泛应用的潜力.

关键词:

气候气候气候气候气候深度学习是一种深度学习.登革热是因为登革热.回归是一种回归.这里是SIRS,SIRS.

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