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Pair-based likelihood approximations for stochastic epidemic models.

Jessica E Stockdale1, Theodore Kypraios2, Philip D O'Neill2

  • 1Department of Mathematics, Simon Fraser University, 8888 University Drive, Burnaby, British Columbia V5A 1S6, Canada.

Biostatistics (Oxford, England)
|December 7, 2019
PubMed
Summary
This summary is machine-generated.

Fitting complex epidemic models to data is challenging due to intractability. A novel likelihood approximation method offers a computationally efficient alternative to data-augmented Markov chain Monte Carlo (MCMC) for disease transmission modeling.

Keywords:
Epidemic modelsLikelihood approximationMarkov chain Monte Carlo methodsStochastic epidemic models

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Area of Science:

  • Epidemiology
  • Computational Biology
  • Statistical Modeling

Background:

  • Direct observation of infection processes in stochastic epidemic models is rare, leading to computationally intractable likelihoods.
  • Existing data-augmented Markov chain Monte Carlo (MCMC) methods, while providing a solution, suffer from poor performance in large populations.

Purpose of the Study:

  • To develop a new computational approach for fitting stochastic epidemic models to observed data.
  • To address the challenge of intractable likelihoods in epidemiological modeling.

Main Methods:

  • Proposed a novel method to approximate the likelihood of observed data by exploiting the inherent structure of epidemic models.
  • Evaluated the approach through simulation studies.

Main Results:

  • The new likelihood approximation method demonstrated competitive performance against data-augmented MCMC methods.
  • The approach proved effective in simulation studies, offering a viable alternative for epidemiological data analysis.

Conclusions:

  • The developed method provides an efficient and scalable approach for fitting stochastic epidemic models.
  • This technique is broadly applicable to various disease transmission models, including those for the common cold, Ebola, and foot-and-mouth disease.