用数据增强提升神经网络对疫情的预测能力
A data augmentation strategy for deep neural networks with application to epidemic modelling
- 结合流行病学模型与深度学习，设计数据增强策略
- 在意大利和西班牙疫情数据上，预测准确率显著提升
- 适合需要高精度预测的公共卫生决策者使用
本文将经典传染病动力学模型与机器学习相结合，探索复杂高维数据中的隐藏模式。以包含社会因素和饱和感染率的SIR模型为例，提出一种数据驱动的数据增强方法，用于提升前馈神经网络与非线性自回归网络的预测可靠性。该方法在意大利和西班牙新冠疫情期间的封城及解封阶段的数值模拟中得到验证，证明其能有效增强非线性动态建模能力，为疫情预测提供可扩展、高精度的数据驱动解决方案，尤其适用于物理约束模型难以覆盖的场景。该策略可作为物理信息神经网络的补充手段。
原文摘要 · Abstract (English)
In this work, we integrate the predictive capabilities of compartmental disease dynamics models with machine learning ability to analyze complex, high-dimensional data and uncover patterns that conventional models may overlook. Specifically, we present a proof of concept demonstrating the application of data-driven methods and deep neural networks to a recently introduced Susceptible-Infected-Recovered type model with social features, including a saturated incidence rate, to improve epidemic prediction and forecasting. Our results show that a robust data augmentation strategy trough suitable data-driven models can improve the reliability of Feed-Forward Neural Networks and Nonlinear Autoregressive Networks, providing a complementary strategy to Physics-Informed Neural Networks, particularly in settings where data augmentation from mechanistic models can enhance learning. This approach enhances the ability to handle nonlinear dynamics and offers scalable, data-driven solutions for epidemic forecasting, prioritizing predictive accuracy over the constraints of physics-based models. Numerical simulations of the lockdown and post-lockdown phase of the COVID-19 epidemic in Italy and Spain validate our methodology.
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