Lorenzo Cappello

This paper develops a Bayesian framework to infer population dynamics with dormancy using molecular sequence data under the strong seedbank coalescent model.

The paper integrates multi-omic data to identify genetic and microbiome factors associated with severe COVID-19, independent of ancestry and social factors.

This paper introduces a Bayesian model for inferring dependent population dynamics using Markov random fields and applies it to study SARS-CoV-2 variants.

This paper develops a high-throughput method to generate host and pathogen genomic data from single nasopharyngeal swabs of COVID-19 patients.

This paper introduces a Bayesian method called BESTT for inferring population size changes using Tajima’s coalescent, showing it is computationally efficient and accurate for both simulated and human data.