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Structural and practical identifiability analysis of outbreak models.

Necibe Tuncer1, Trang T Le2

  • 1Department of Mathematical Sciences, Florida Atlantic University, Science Building, Room 234, 777 Glades Road, Boca Raton, FL 33431, USA.

Mathematical Biosciences
|February 26, 2018
PubMed
Summary
This summary is machine-generated.

Estimating infectious disease spread requires identifiable epidemic models. This study finds the SIR model is identifiable using prevalence data, but not cumulative incidence data, suggesting prevalence reporting is preferable.

Keywords:
Cumulative incidenceEmerging infectious diseaseOutbreak modelsPractical identifiabilityStructural identifiability

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Area of Science:

  • Epidemiology
  • Mathematical Modeling
  • Infectious Disease Dynamics

Background:

  • Accurate estimation of disease reproduction numbers is crucial for outbreak assessment.
  • Epidemic models require structural identifiability for reliable parameter estimation from data.
  • Current studies often lack this fundamental prerequisite, impacting outbreak evaluation.

Purpose of the Study:

  • To perform structural and practical identifiability analysis on classical epidemic models (SIR, SEIR, SITR).
  • To investigate the well-posedness of parameter estimation problems using different data types.
  • To determine the suitability of common epidemiological data for model parameterization.

Main Methods:

  • Structural identifiability analysis using differential algebra.
  • Practical identifiability analysis employing Monte Carlo simulations and Fisher's Information Matrix.
  • Estimation of model parameters from prevalence, cumulative incidences, and treated individuals data.

Main Results:

  • The SIR model is both structurally and practically identifiable from prevalence data.
  • The SIR model is structurally identifiable but practically unidentifiable from cumulative incidence data due to parameter correlations.
  • None of the simple epidemic models analyzed were practically identifiable from cumulative incidence data.

Conclusions:

  • Prevalence data offers better identifiability for simple epidemic models compared to cumulative incidence data.
  • Health agencies should prioritize reporting prevalence data for more robust outbreak analysis.
  • Limitations in current data reporting practices hinder accurate epidemic modeling and parameter estimation.