确定塞兰戈尔的白病热点:使用遥感发现气候联系,并开发预测模型
Muhammad Akram Ab Kadir1, Rosliza Abdul Manaf1, Siti Aisah Mokhtar1
1Department of Community Health, Universiti Putra Malaysia, Serdang, Selangor, Malaysia.
PeerJ
|March 10, 2025
概括
马来西亚的白病例显示空间聚类最小,但使用GIS和机器学习识别了热点. 降雨量和河流水位是预测疾病爆发的关键因素,有助于公共卫生干预.
科学领域:
- 环境流行病学环境流行病学
- 地理信息系统 (GIS) 是指地理信息系统.
- 机器学习 机器学习
背景情况:
- 在马来西亚,尤其是塞兰戈尔,白病的发病率显著增加.
- 气候变化的动态需要研究疾病热点和水气候因素.
研究的目的:
- 为了分析在塞兰戈尔的螺杆菌病的时空分布.
- 为了识别勒螺杆菌热点及其与水气候因素的关联.
- 通过使用机器学习,开发勒螺杆菌热点的预测模型.
主要方法:
- 使用GIS和遥感 (2011-2019) 的生态横截面研究.
- 在1045个实验室确定的病例中进行空间自相关 (莫兰的I) 和热点分析 (Getis-Ord Gi*).
- 机器学习模型 (SVM,RF,LGBM) 用卫星衍生的降雨量和陆地表面温度数据进行训练.
主要成果:
- 莱普托斯皮罗斯的分布主要是随机的,空间聚类有限.
- 热点地区分散,在塞兰戈尔的中部地区有着显著的集群.
- 光梯度增强机 (LGBM) 模型表现最好,以降雨量和河流水位作为关键预测指标.
结论:
- 在塞兰戈尔,白病的发生在很大程度上是零星的,空间聚类最小.
- 图形信息系统和机器学习,特别是LGBM,有效地预测了白病热点.
- 这种综合方法增强了疾病监测,并使有针对性的公共卫生干预成为可能.
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