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A Temporal-Dynamic Neural-SIR Approach based on LSTM for Stochastic Infectious Disease Forecasting
Summary
Neural-SIR enhances infectious disease forecasting by integrating stochastic dynamics into Susceptible-Infected-Recovered (SIR) models. This framework rigorously evaluates machine learning models for reliability and robustness in uncertain epidemic scenarios.
Area of Science:
- Epidemiology and Public Health
- Computational Biology and Machine Learning
Background:
- Accurate infectious disease forecasting is vital for public health interventions and resource allocation.
- Traditional epidemiological models like Susceptible-Infected-Recovered (SIR) struggle with real-world variability and uncertainty.
- Hybrid models combining mathematical modeling and machine learning (ML) show promise but require evaluation for generalization and interpretability.
Purpose of the Study:
- To present Neural-SIR, a simulation-based framework for evaluating ML model generalization in epidemic forecasting.
- To assess the reliability, robustness, and epidemiological interpretability of ML models under uncertain scenarios.
- To incorporate stochastic dynamics to better reflect real-world disease variability.
Main Methods:
- Developed Neural-SIR, a temporal-dynamic simulation framework using SIR differential equations (DEs).
- Generated synthetic data capturing inter-regional heterogeneity and intra-population variability using two uncertainty modeling approaches.
- Trained and evaluated Long Short-Term Memory (LSTM) models using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R-squared.
Main Results:
- The Neural-SIR framework successfully integrates stochastic dynamics, mimicking real-world uncertainty.
- Performance evaluation of various LSTM architectures demonstrated their generalization ability in novel, uncertain conditions.
- Findings highlight the importance of stochastic dynamics for robust epidemic forecasting model evaluation.
Conclusions:
- Neural-SIR provides a rigorous and interpretable framework for evaluating epidemic forecasting models under uncertainty.
- The framework's ability to incorporate stochastic dynamics enhances the reliability and robustness of forecasting approaches.
- This supports better decision-making for public health professionals and clinicians in managing disease outbreaks.
