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Extended models for nosocomial infection: parameter estimation and model selection.

Alun Thomas1, Karim Khader2,3, Andrew Redd2,3

  • 1Division of Genetic Epidemiology, School of Medicine, University of Utah, Salt Lake, UT, USA.

Mathematical Medicine and Biology : a Journal of the IMA
|October 18, 2017
PubMed
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This study enhances patient-level nosocomial infection models by incorporating latent periods, decolonization, and multiple facility/unit transfers. These advanced models improve tracking and understanding of infection transmission dynamics in healthcare settings.

Area of Science:

  • Epidemiology
  • Biostatistics
  • Infectious Disease Modeling

Background:

  • Nosocomial infections pose a significant threat in healthcare settings.
  • Existing models often lack detailed patient movement and pathogen transmission dynamics.
  • Accurate modeling is crucial for effective infection control strategies.

Purpose of the Study:

  • To extend existing susceptible-infected models for nosocomial infections.
  • To develop a flexible modeling framework accounting for patient transfers and varied transmission rates.
  • To provide computational tools for parameter estimation and model selection.

Main Methods:

  • Developed extended susceptible-infected models with latent periods and decolonization.
  • Incorporated multiple facilities, units, and patient transfer tracking.

Related Experiment Videos

  • Utilized Bayesian inference with Markov chain Monte Carlo (MCMC) methods.
  • Implemented cross-validation, Deviance Information Criterion (DIC), and Widely Applicable Information Criterion (WAIC) for model choice.

    • Main Results:

    • The enhanced models provide a robust framework for analyzing complex infection transmission.
    • Parameter estimates and model choice statistics were obtained using MCMC.
    • Demonstrated the application of the methods using methicillin-resistant Staphylococcus aureus surveillance data from a Veterans Administration hospital.

    Conclusions:

    • The developed methods offer a comprehensive approach to modeling nosocomial infections.
    • The framework allows for detailed analysis of transmission dynamics influenced by patient movement and facility structure.
    • This work provides valuable tools for infection control and public health surveillance.