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Inferring epidemiological parameters from phylogenies using regression-ABC: A comparative study.

Emma Saulnier1,2, Olivier Gascuel2,3, Samuel Alizon1

  • 1Laboratoire Maladies Infectieuses et Vecteurs: Ecologie, Génétique, Evolution et Contrôle - UMR CNRS 5290, IRD 224 et UM, Montpellier, France.

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Summary
This summary is machine-generated.

A new regression-based Approximate Bayesian Computation (ABC) method accurately infers epidemiological parameters from phylogenies. This approach, using machine learning, offers a robust alternative to traditional methods for analyzing large phylogenetic datasets.

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Area of Science:

  • Epidemiology
  • Phylogenetics
  • Computational Biology

Background:

  • Inferring epidemiological parameters like R0 from time-scaled phylogenies is crucial but challenging.
  • Current likelihood-based methods face computational and numerical optimization issues.
  • Existing techniques may struggle with large datasets and complex epidemiological models.

Purpose of the Study:

  • To introduce a novel regression-based Approximate Bayesian Computation (ABC) approach for inferring epidemiological parameters.
  • To provide a robust and computationally efficient alternative to existing phylogenetic inference methods.
  • To assess the performance of the new method against established techniques using simulated and real-world data.

Main Methods:

  • Developed a regression-based Approximate Bayesian Computation (ABC) framework utilizing numerous summary statistics from phylogenies and lineage-through-time plots.
  • Employed the Least Absolute Shrinkage and Selection Operator (LASSO) for variable selection in the regression step, avoiding Markov Chain Monte Carlo (MCMC).
  • Validated the approach through simulations of various epidemiological models and re-analysis of Ebola epidemic data.

Main Results:

  • Regression-ABC demonstrated accuracy comparable to likelihood-based methods (BEAST2) for large phylogenies.
  • Outperformed existing methods in inferring host population size for Susceptible-Infected-Removed models.
  • Provided more realistic estimates for latency and infectiousness duration parameters in Ebola epidemic data compared to likelihood-based methods.

Conclusions:

  • Regression-based ABC with extensive summary statistics and LASSO offers a promising, robust approach for analyzing large phylogenies.
  • This method effectively handles variable selection and avoids overfitting, providing reliable epidemiological parameter estimates.
  • The developed approach presents a significant advancement for epidemiological research using phylogenetic data.