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A Data-Driven Approach to Quantifying Immune States in Sepsis
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Published on: February 7, 2025

A comparative study of simulation-based inference methods for epidemic models with identifiability considerations.

Geunsoo Jang1, K Selçuk Candan1, Gerardo Chowell2,3

  • 1School of Computing and Augmented Intelligence, Arizona State University, Tempe, Arizona, United States of America.

Plos Computational Biology
|June 2, 2026
PubMed
Summary

Simulation-based inference methods like Approximate Bayesian Computation (ABC) and neural approaches offer alternatives when epidemic models lack computable likelihood functions. Neural methods generally enhance accuracy under limited simulations, with Preconditioned Neural Posterior Estimation (PNPE) showing strong performance.

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Area of Science:

  • Computational epidemiology and statistical inference.
  • Development and application of advanced simulation-based inference techniques.

Background:

  • Epidemic models are crucial for understanding disease transmission and informing public health.
  • Traditional Bayesian inference methods struggle with complex epidemic models due to intractable likelihood functions.
  • Simulation-based inference (SBI) offers a viable alternative by bypassing explicit likelihood evaluation.

Purpose of the Study:

  • To systematically compare four SBI methods for calibrating epidemic models.
  • To evaluate performance under varying model complexity, simulation budgets, and observational noise.
  • To assess structural and practical identifiability challenges in epidemic modeling.

Main Methods:

  • Comparison of Approximate Bayesian Computation (ABC), Neural Posterior Estimation (NPE), temporal embedding neural methods, and Preconditioned Neural Posterior Estimation (PNPE).
  • Evaluation across a range of epidemic models with increasing complexity.
  • Assessment under fixed simulation budgets and different levels of observational noise.

Main Results:

  • Neural methods, particularly NPE and PNPE, generally yield improved posterior accuracy and predictive performance over ABC, especially under constrained simulation budgets.
  • PNPE demonstrated strong performance, while temporal embeddings enhanced inference for complex dynamics by capturing sequential dependencies.
  • ABC remained computationally efficient, providing conservative yet reasonable posterior estimates.

Conclusions:

  • Method selection involves trade-offs between computational efficiency, posterior accuracy, uncertainty calibration, and reusability.
  • Neural methods offer advantages in accuracy but may require more computational resources and re-adaptation.
  • The choice of inference method should be guided by epidemic model complexity, data characteristics, identifiability, and available computational power.