使用数据驱动方法分析疟疾的数学模型
Adithya Rajnarayanan1, Manoj Kumar1, Abdessamad Tridane2
1School of Engineering and Science, Indian Institute of Technology Madras Zanzibar, PO Box 394, Bweleo, Zanzibar, Urban West, 71215, Tanzania.
Scientific reports
|July 27, 2025
概括
这项研究使用温度和海拔高度等环境因素来模拟疟疾传播. 它引入了一种新的基于物理的机器学习方法,用于更好的预测和实时风险评估.
科学领域:
- 流行病学 流行病学
- 数学建模的数学建模
- 环境科学 环境科学
背景情况:
- 疟疾是全球主要的健康负担,每年导致数百万病例和死亡.
- 了解疾病传播动态对于有效的公共卫生干预至关重要.
研究的目的:
- 开发一种用于建模疟疾传播动态的新型框架.
- 将环境因素 (温度,高度) 整合到一个分区SIR-SI模型中.
- 为了提高疟疾传播模型的现实性和预测准确性.
主要方法:
- 开发了一个新的传输功能,结合了温度和高度的依赖性.
- 进行了稳定状态分析,以确定疾病平衡的稳定性标准.
- 使用人工神经网络 (ANN),循环神经网络 (RNN) 和物理信息神经网络 (PINN) 的比较学习框架进行参数估计.
- 实现了动态模式分解 (DMD),以创建数据驱动的传输风险指数.
主要成果:
- 建立了无病和特有平衡的稳定性标准.
- 通过嵌入流行病学动态,物理信息神经网络 (PINNs) 展示了优越的预测性能.
- 从感染数据中使用DMD推导出一种新的,可解释的传播风险指数.
结论:
- 新的框架通过整合环境因素来增强疟疾传播建模的现实性.
- 使用PINNs进行物理约束参数推断显著提高了预测准确性.
- 数据驱动的传播风险指数为实时疟疾风险评估提供了有价值的工具.
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