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Updated: Jul 10, 2026

Diagnosis and Surgical Treatment of Human Brucellar Spondylodiscitis

Diagnosis and Surgical Treatment of Human Brucellar Spondylodiscitis

Published on: May 23, 2021

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

Ying-Ping Liu^1,2, Yong Li^3,2, Gui-Quan Sun^4,5

¹School of Mathematics and Statistics, Northeast Normal University, Changchun 130024, China.

Innovation (Cambridge (Mass.))

|July 9, 2026

Summary

This summary is machine-generated.

Inner Mongolia faces ongoing brucellosis challenges. A new hybrid model (KHDMDsp-LSTM) accurately predicts epidemic dynamics, revealing transmission patterns and offering improved forecasting for public health.

Keywords:

data-driven human brucellosis kernelized Hankel DMD long short-term memory network sparsity

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Diagnosis and Surgical Treatment of Human Brucellar Spondylodiscitis

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Published on: May 23, 2021

Chronic Unpredictable Mild Stress in Rats based on the Mongolian medicine

Chronic Unpredictable Mild Stress in Rats based on the Mongolian medicine

Published on: October 27, 2023

Area of Science:

Epidemiology
Computational Biology
Data Science

Background:

Brucellosis poses a significant public health threat in Inner Mongolia.
Epidemic dynamics exhibit complex spatiotemporal transmission, periodicity, and nonlinear behavior.

Purpose of the Study:

To develop and validate a novel hybrid modeling framework for analyzing and predicting brucellosis epidemic dynamics.
To improve the accuracy and long-term forecasting capabilities of infectious disease models.

Main Methods:

Proposed a hybrid framework: Kernelized Hankel Dynamic Mode Decomposition with Sparsity promotion (KHDMDsp) integrated with Long Short-Term Memory (LSTM) networks.
KHDMDsp linearizes nonlinear dynamics using a hybrid Mercer kernel and Bayesian optimization for adaptive parameter tuning.
LSTM network captures multi-city temporal dependencies; elastic net regularization promotes interpretable mode selection.

Main Results:

The KHDMDsp-LSTM framework accurately reconstructed and predicted brucellosis epidemic dynamics over 11 years across 12 cities.
Identified stable, gradually decaying transmission patterns concentrated in central and eastern Inner Mongolia.
Demonstrated superior performance in reconstruction and long-horizon prediction compared to baseline models.

Conclusions:

The KHDMDsp-LSTM framework offers a robust, data-driven approach for infectious disease modeling and prediction.
Combining kernel-based Koopman spectral analysis with deep learning shows significant potential for public health surveillance.
Highlights the effectiveness of advanced computational methods in understanding and managing endemic diseases.