Deep Learning for Disease Outbreak Prediction: A Robust Early Warning Signal for Transcritical Bifurcations

2025-01-14

AI Summary

Early warning of emerging infectious disease outbreaks remains challenging due to limited data, high noise, and unknown pathogen dynamics. Method: This paper proposes a robust prediction framework integrating bifurcation theory from dynamical systems with deep temporal classification. Specifically, it formulates transcritical bifurcation precursor detection as a time-series classification task and develops an optimal temporal neural network trained on stochastic polynomial dynamical system simulations under strong noise perturbations, validated on real-world influenza and COVID-19 incidence time series. Contribution/Results: The approach breaks from conventional statistical or purely data-driven paradigms by enabling interpretable modeling of unknown pathogen dynamics. It demonstrates strong generalization under high-noise and small-sample conditions. Experiments show significant performance gains over existing early-warning models across multiple simulation scenarios and real epidemic datasets, achieving high accuracy, low latency, and cross-pathogen applicability.

Technology Category

• Machine Learning: Other Foundations of Machine Learning
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Data Mining & Knowledge Management: Anomaly/Outlier Detection

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Social Networks and Social Media: Influence propagation, information diffusion, and the prediction on networks
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

Early Warning Signals (EWSs) are vital for implementing preventive measures before a disease turns into a pandemic. While new diseases exhibit unique behaviors, they often share fundamental characteristics from a dynamical systems perspective. Moreover, measurements during disease outbreaks are often corrupted by different noise sources, posing challenges for Time Series Classification (TSC) tasks. In this study, we address the problem of having a robust EWS for disease outbreak prediction using a best-performing deep learning model in the domain of TSC. We employed two simulated datasets to train the model: one representing generated dynamical systems with randomly selected polynomial terms to model new disease behaviors, and another simulating noise-induced disease dynamics to account for noisy measurements. The model's performance was analyzed using both simulated data from different disease models and real-world data, including influenza and COVID-19. Results demonstrate that the proposed model outperforms previous models, effectively providing EWSs of impending outbreaks across various scenarios. This study bridges advancements in deep learning with the ability to provide robust early warning signals in noisy environments, making it highly applicable to real-world crises involving emerging disease outbreaks.

Problem

• Disease Outbreak Prediction
• Early Warning Signal
• Epidemic Prevention

Innovation

• Deep Learning Model
• Disease Prediction
• Epidemic Prevention