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Updated: Jun 17, 2025

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Stochastic EM algorithm for partially observed stochastic epidemics with individual heterogeneity.

Fan Bu1, Allison E Aiello2, Alexander Volfovsky3

  • 1Department of Biostatistics, University of Michigan, 1415 Washington Heights, Ann Arbor, MI 48109, USA.

Biostatistics (Oxford, England)
|August 8, 2024
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Summary

We created a new model for tracking infectious disease spread through changing social networks, accounting for individual differences. Our method accurately estimates disease dynamics even with incomplete data, improving epidemic analysis.

Keywords:
SEIR modelscontact tracingdata-augmented inferencestochastic EMstochastic epidemic models

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Area of Science:

  • Epidemiology
  • Network Science
  • Computational Biology

Background:

  • Epidemic modeling often simplifies contact networks.
  • Real-world networks are dynamic and infection rates vary.
  • Incomplete observation of disease cases is common.

Purpose of the Study:

  • To develop a flexible stochastic model for epidemics on dynamic networks.
  • To create an inference method for partially observed epidemic data.
  • To accurately estimate model parameters and understand disease spread.

Main Methods:

  • Modeled joint dynamics as a continuous-time Markov chain.
  • Incorporated heterogeneous infection rates and individual covariates.
  • Developed a stochastic Expectation-Maximization (EM) algorithm with efficient samplers for imputing missing data.

Main Results:

  • The proposed EM algorithm accurately recovers model parameters.
  • The method effectively handles dynamic networks and partial observations.
  • Demonstrated performance on synthetic and real-world epidemic datasets.

Conclusions:

  • Our approach provides a robust framework for epidemic modeling on dynamic networks.
  • The inference method offers valuable insights despite unobserved disease episodes.
  • This work enhances the analysis of complex infectious disease dynamics.