title: Inferring epidemiological parameters from phylogenies using regression-ABC: A comparative study.
abstract: Inferring epidemiological parameters such as the R0 from time-scaled phylogenies is a timely challenge. Most current approaches rely on likelihood functions, which raise specific issues that range from computing these functions to finding their maxima numerically. Here, we present a new regression-based Approximate Bayesian Computation (ABC) approach, which we base on a large variety of summary statistics intended to capture the information contained in the phylogeny and its corresponding lineage-through-time plot. The regression step involves the Least Absolute Shrinkage and Selection Operator (LASSO) method, which is a robust machine learning technique. It allows us to readily deal with the large number of summary statistics, while avoiding resorting to Markov Chain Monte Carlo (MCMC) techniques. To compare our approach to existing ones, we simulated target trees under a variety of epidemiological models and settings, and inferred parameters of interest using the same priors. We found that, for large phylogenies, the accuracy of our regression-ABC is comparable to that of likelihood-based approaches involving birth-death processes implemented in BEAST2. Our approach even outperformed these when inferring the host population size with a Susceptible-Infected-Removed epidemiological model. It also clearly outperformed a recent kernel-ABC approach when assuming a Susceptible-Infected epidemiological model with two host types. Lastly, by re-analyzing data from the early stages of the recent Ebola epidemic in Sierra Leone, we showed that regression-ABC provides more realistic estimates for the duration parameters (latency and infectiousness) than the likelihood-based method. Overall, ABC based on a large variety of summary statistics and a regression method able to perform variable selection and avoid overfitting is a promising approach to analyze large phylogenies.
authors.affiliation: Laboratoire Maladies Infectieuses et Vecteurs: Ecologie, Génétique, Evolution et Contrôle - UMR CNRS 5290, IRD 224 et UM, Montpellier, France.|Institut de Biologie Computationnelle (IBC) and Laboratoire d'Informatique, de Robotique et de Microélectronique de Montpellier (LIRMM) - UMR 5506, CNRS et UM, Montpellier, France.
authors.forename: Emma
authors.identifier: 0000-0002-8392-0624
authors.initials: E
authors.lastname: Saulnier
authors.affiliation: Institut de Biologie Computationnelle (IBC) and Laboratoire d'Informatique, de Robotique et de Microélectronique de Montpellier (LIRMM) - UMR 5506, CNRS et UM, Montpellier, France.|Unité de Bioinformatique Evolutive, C3BI - USR 3756, Institut Pasteur et CNRS, Paris, France.
authors.forename: Olivier
authors.initials: O
authors.lastname: Gascuel
authors.affiliation: Laboratoire Maladies Infectieuses et Vecteurs: Ecologie, Génétique, Evolution et Contrôle - UMR CNRS 5290, IRD 224 et UM, Montpellier, France.
authors.forename: Samuel
authors.identifier: 0000-0002-0779-9543
authors.initials: S
authors.lastname: Alizon
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D003198:Computer Simulation; D004196:Disease Outbreaks / Q000706:statistics & numerical data*; D029043:Ebolavirus / Q000145:classification / Q000235:genetics* / Q000302:isolation & purification; D019142:Hemorrhagic Fever, Ebola / Q000453:epidemiology* / Q000821:virology*; D006801:Humans; D015994:Incidence; D015233:Models, Statistical; D010802:Phylogeny; D012044:Regression Analysis; D012307:Risk Factors; D012807:Sierra Leone / Q000453:epidemiology; D017735:Virus Latency / Q000235:genetics