A kernelized data-driven approach for analyzing and predicting brucellosis in Inner Mongolia

Public summary

* A hybrid kernel-based data-driven framework is proposed for high-dimensional linearized modeling.

* Elastic net regularization balances model sparsity and accuracy, enabling the selection of key Koopman modes.

* Combining with LSTM, merging Koopman interpretability with deep learning’s nonlinear predictive capability.

* Empirical analysis of Inner Mongolia brucellosis data validates the framework's practicality.

Abstract

Brucellosis remains a persistent public health concern in Inner Mongolia, exhibiting complex spatiotemporal transmission, pronounced periodicity, and strong nonlinear dynamics. To address these challenges, a novel hybrid modeling framework—kernelized Hankel dynamic mode decomposition with sparsity promotion- long short-term memory (KHDMDsp-LSTM) —is proposed for analysis and prediction of epidemic dynamics. The framework’s core innovation is the KHDMDsp, which linearizes nonlinear epidemic processes by embedding low-dimensional system states into a high-dimensional feature space via a hybrid Mercer kernel, enabling extraction of diverse dynamical modes. Kernel parameters are adaptively optimized using Bayesian optimization, and an elastic net regularization strategy promotes sparsity and interpretable mode selection. An integrated LSTM network captures nonlinear temporal dependencies across multiple cities, enhancing long-term prediction. Applied to 11 years of monthly brucellosis data from 12 cities, the framework reconstructs and predicts epidemic dynamics with high fidelity, revealing stable, gradually decaying transmission concentrated in central and eastern Inner Mongolia. Compared with baseline models, KHDMDsp-LSTM demonstrates superior reconstruction and long-horizon prediction, highlighting the potential of combining kernel-based Koopman spectral analysis with deep learning as a robust, data-driven approach for infectious disease modeling and prediction.