Samuel Alizon

Resource landscape shapes the composition and stability of the human vaginal microbiota

This study introduces a mathematical model to explore how nutritional resources shape the composition and stability of the human vaginal microbiota, showing that exclusive access to certain nutrients correlates with bacterial vaginosis dominance.

Teddy: neural inference of epidemiological parameters from viral sequences

The paper introduces a deep learning framework for estimating epidemiological parameters from viral sequences, achieving high accuracy and speed compared to traditional methods.

Genome wide association study of vaginal microbiota genetic diversity in French women

The paper conducts a GWAS to identify genetic polymorphisms associated with vaginal microbiota diversity in French women using data from the PAPCLEAR cohort.

COVFlow: phylodynamics analyses of viruses from selected SARS-CoV-2 genome sequences

COVFlow enables phylodynamic analysis of SARS-CoV-2 sequences by selecting data from GISAID, performing phylogenetic analysis, and generating input for Beast2.

TiPS: rapidly simulating trajectories and phylogenies from compartmental models

TiPS generates trajectories and phylogenetic trees from compartmental models using stochastic simulations and coalescent methods, with an R package for flexible and efficient modeling.

COUSIN (COdon Usage Similarity INdex): a normalized index of Codon Usage Preferences.

COUSIN is a normalized index for measuring codon usage preferences, revealing a bimodal distribution in human and chicken genes.

Epithelial stratification shapes infection dynamics

This paper develops a mathematical model to simulate infections in stratified epithelia, finding that infection outcomes depend on pathogen interactions with epithelial layers and cell responses.

Why multiple infections favour virulent parasites

The paper shows that within-host growth traits are under strong selective pressure, leading to a selection bias towards higher virulence in multiple infections.

Assessing the accuracy of Approximate Bayesian Computation approaches to infer epidemiological parameters from phylogenies

This study evaluates ABC methods for inferring epidemiological parameters from phylogenies, finding comparable or better accuracy than likelihood-based methods for complex models and large datasets.