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Related Experiment Video

Updated: Jan 9, 2026

A Data-Driven Approach to Quantifying Immune States in Sepsis
07:42

A Data-Driven Approach to Quantifying Immune States in Sepsis

Published on: February 7, 2025

463

A Temporal-Dynamic Neural-SIR Approach based on LSTM for Stochastic Infectious Disease Forecasting.

Sofia Zahri, Nikesh Bajaj, Jesus Requena Carrion

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |December 3, 2025
    PubMed
    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.

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    In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
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    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:

    Related Experiment Videos

    Last Updated: Jan 9, 2026

    A Data-Driven Approach to Quantifying Immune States in Sepsis
    07:42

    A Data-Driven Approach to Quantifying Immune States in Sepsis

    Published on: February 7, 2025

    463
  • 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.