Phylodynamics

Inferring epidemiological parameters from genomic data

With the advent of affordable sequencing techniques, phylogenies are routinely generated in epidemiological studies on viruses. The question is, can these really tell us more about epidemiological processes than classical incidence data?

Works in the field of phylodynamics over the last 20 years led to the development of epidemiological parameter inference by maximum likelihood methods. But these methods tend to be based on simple epidemiological models. Furthermore, the computational time can be elevated for detailed models or large datasets.

We developped a likelihood-free method involving Approximate Bayesian Computation (ABC). The idea is to simulate thousands of phylogenies for known sets of parameters and then to compare them to the target phylogeny. BY selecting the parameter sets that simulate the most resembling phhylogenies, we can infer a posterior distribution.

To this end, we created a flexible simulator in a software package, TiPS, to generate phylogenies from any epidemiological compartmental model. Our method has been compared to some implemented in BEAST and applied to the case of an HCV epidemics spreading in Lyon (France).

We are now extending this work to perform the parameter inference directly from the sequence alignments, therefore bypassing the need to infer a phylogeny. This is done using recently-developped machine learning algorithms.

We also apply these phylodynamics methods to analyse the spread of the HIV epidemic in France at the national and at the local level through a collaboration with the CHU of Montpellier.

Publications

Danesh G, Saulnier E, Gascuel O, Choisy M, Alizon S (2023) TiPS: rapidly simulating trajectories and phylogenies from compartmental models. Methods in Ecology and Evolution. 14:487-495

Alizon S (2022) Phylogénies d'infections et phylodynamique. In: Étude de l'évolution par l'approche mathématique et informatique. (Didier G, Guindon S, eds.) ISTE, London, UK

Danesh G, Virlogeux V, Ramière C, Charre C, Cotte L, Alizon S (2021) Quantifying transmission dynamics of acute hepatitis C virus infections in a heterogeneous population using sequence data. PLoS Pathogens 17(9):e1009916

Saulnier E, Gascuel O, Alizon S (2017) Inferring epidemiological parameters from phylogenies using regression-ABC: a comparative study. PLoS Comput Biol 13(3):e1005416