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动态变化,影响因素,和预测在新疆的菌病由ARIMA-随机森林混合模型

Fenghan Wang¹, Xuedong Yang¹, Qianqian Zhang²

¹Shanghai 411 Hospital, China RongTong Medical Healthcare Group Co.Ltd./411 Hospital, Shanghai University, Shanghai, China.

|August 19, 2025

概括

在新疆,球菌病例在2004年至2020年间呈现出显著的增长趋势,受温度和药物价格的影响. 结合ARIMA-RF模型准确地预测了这些疾病变异.

科学领域:

流行病学流行病学
公共卫生公共卫生
环境科学环境科学

背景情况:

新疆是中国主要的牧场地区,在控制球菌病方面面临着重大挑战.
新疆的干旱和半干旱条件有助于这种寄生虫疾病的流行.

研究的目的:

从2004年到2020年,调查新疆内球菌病的时间变异.
为了确定球菌病与环境/社会经济因素之间的关系.
开发一种预测 echinococcosis 发病率的模型.

主要方法:

对球菌病时间变异 (病例和发病率) 的全面分析.
统计分析菌病与温度 (Tmp),降水 (Pre),相对湿度 (RH),阳光持续时间 (SD) 和药物速率 (MR) 之间的相关性.
开发和应用混合自回归集成移动平均线 (ARIMA) 和随机森林 (RF) 模型进行预测.

主要成果:

球菌病呈现出显著的增长趋势,每年病例增加94.48例,每10万人口中新增病例为0.339例.
对于确诊病例和发病率,观察到具有多个周期性的非线性时间特征.
温度 (Tmp) 和药物速率 (MR) 与球菌病正相关,而阳光持续时间 (SD) 显示出负相关性.

结论:

在2004年至2020年期间,新疆的球菌病发病率显著增加.
温度和药物速率是积极影响球菌患病率的关键因素.
ARIMA-RF混合模型在预测球菌变异方面表现出很高的准确性,有助于疾病控制工作.

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