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Parametric inference for epidemic models

N G Becker1

  • 1Department of Statistics, La Trobe University, Bundoora, Australia.

Mathematical Biosciences
|September 1, 1993
PubMed
Summary
This summary is machine-generated.

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Martingale methods offer simpler statistical inferences for epidemic data compared to complex likelihood functions. These methods provide a robust estimate of disease infection potential, though precision varies with model assumptions.

Area of Science:

  • Epidemiology
  • Biostatistics
  • Statistical Inference

Background:

  • Likelihood functions for epidemic data are often complex and challenging for statistical inference.
  • Martingale methods offer an alternative approach to inference for epidemic models.
  • Previous research highlights the utility of martingale methods in analyzing epidemic data.

Purpose of the Study:

  • To compare likelihood inference with martingale methods for analyzing epidemic data.
  • To investigate the impact of varying model specifications on inferences about disease infection potential.
  • To assess the robustness and precision of martingale-based estimates.

Main Methods:

  • Utilized the Expectation-Maximization (EM) algorithm to simplify likelihood inferences.

Related Experiment Videos

  • Employed martingale methods based on estimating equations derived from martingale theory.
  • Contrasted statistical inferences derived from both likelihood and martingale approaches.

    • Main Results:

    • Martingale methods provide simpler inferences for epidemic data compared to complex likelihood functions.
    • The martingale-based estimate for disease infection potential is robust across various model specifications.
    • The precision of the martingale-based infection potential estimate is sensitive to changes in model assumptions.

    Conclusions:

    • Martingale methods are a valuable tool for simplifying statistical inference in the analysis of epidemic data.
    • Martingale-based estimates of infection potential offer stability under different model assumptions.
    • Researchers should consider the impact of model specification on the precision of martingale-based estimates.