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This study introduces a new diagnostic framework to identify errors in phylodynamic models, crucial for understanding pathogen transmission. The framework effectively detects model mis-specification, particularly during superspreading events and within-host diversity.

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Area of Science:

  • Epidemiology and Evolutionary Biology
  • Computational Biology and Bioinformatics
  • Statistical Modeling

Background:

  • Phylodynamic modeling integrates epidemiological and evolutionary processes, advancing pathogen transmission understanding with genomic data and statistical methods.
  • Existing phylodynamic models lack effective diagnostic tools for systematic mis-specification detection, hindering model refinement and calibration.
  • Latent residuals have been used for model assessment in spatio-temporal epidemiology, offering a basis for developing new diagnostic approaches.

Purpose of the Study:

  • To develop a novel model-diagnostic framework for phylodynamic models to systematically detect mis-specification.
  • To assess the framework's ability to identify specific model misspecifications, such as those arising from superspreading.
  • To demonstrate the framework's utility in real-world applications, including model calibration and evaluating within-host diversity assumptions.

Main Methods:

  • Extension of the latent residual concept from spatio-temporal epidemiology to phylodynamic modeling.
  • Development of non-centered re-parameterizations to construct latent residuals with known sampling distributions.
  • Application of the framework to simulated data to evaluate its performance in detecting mis-specification, including superspreading scenarios.
  • Testing the framework on a foot-and-mouth disease (FMD) outbreak dataset to assess within-host diversity assumptions.

Main Results:

  • The proposed framework effectively detects specific forms of phylodynamic model mis-specification, particularly in the presence of superspreading.
  • Application to an FMD outbreak dataset provided strong evidence against the assumption of no within-host diversity.
  • The framework facilitated model calibration, supporting a within-host diversity model over one assuming no diversity for FMD virus.

Conclusions:

  • The developed diagnostic framework offers a valuable tool for assessing the validity of phylodynamic models.
  • It enhances the reliability of phylodynamic analyses by enabling systematic detection and quantification of model mis-specification.
  • The framework's application highlights the importance of considering within-host diversity in pathogen outbreak modeling, improving model accuracy and calibration.