神经参数校准用于登革热爆发预测
Hoang Viet Pham1, Khuong Trung Dang Nguyen1, Thirumalaisamy P Velavan2,3,4
1Faculty of Engineering, Vietnamese-German University, Ho Chi Minh City, Vietnam.
PloS one
|February 25, 2026
概括
神经参数校准 (NPC) 提供了一个更快,更准确的方法来估计登革热传播模型中的参数. 这种计算方法有助于及时响应公共卫生问题,特别是在资源有限的地区.
科学领域:
- 流行病学 流行病学
- 计算生物学 计算生物学
- 数学建模的数学建模
背景情况:
- 登革热是热带和亚热带地区的重大公共卫生问题.
- 病毒和宿主因素之间的复杂相互作用驱动着登革热传播动态.
- 计算模型,通常使用普通微分方程 (ODEs),对于理解这些动态至关重要.
研究的目的:
- 评估神经参数校准 (NPC) 用于估计登革热传播的扩展分区模型 (ECM) 中的参数.
- 将NPC的计算效率和准确性与传统的马尔科夫链蒙特卡洛 (MCMC) 方法进行比较.
- 通过使用来自南美和东南亚的真实世界登革热监测数据来验证ECM-NPC方法.
主要方法:
- 开发了一个包括七个ODE的扩展隔间模型 (ECM) 来描述人类和蚊子登革热的传播.
- 采用神经参数校准 (NPC),利用神经网络来学习模型参数后部分布.
- 分析了来自三个南美城市和三个东南亚国家的六个登革热监测数据集.
主要成果:
- 与MCMC相比,NPC表现出明显更快的计算时间 (例如,国家级数据的368s与2998s).
- 对于城市和国家数据集,NPC实现了与MCMC可比的准确性,平均平方误差 (MSE) 值较低.
- 结合ECM和NPC的方法在登革热爆发预测方面被证明是有效的.
结论:
- 扩展隔间模型与神经参数校准的整合提供了准确的登革热爆发预测.
- 这种方法可以大幅降低计算成本,使其成为公共卫生的实用工具.
- 对于在资源有限的环境中支持及时干预,ECM-NPC方法特别有价值.
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