Caroline Colijn

BREATH simultaneously infers phylogenetic and transmission trees for outbreaks using sequence data, improving resolution and origin timing estimates in a tuberculosis outbreak.

The paper develops a mathematical model to predict vaccine replacement dynamics in Streptococcus pneumoniae and identifies optimal genomic surveillance strategies.

This paper trains species-specific transformer models for bacterial genomic surveillance, showing they can cluster genomes, identify emerging lineages, and predict antibiotic resistance gene uptake.

The paper introduces an extended clade framework to better summarize annotated phylogenetic trees in phylogeography and transmission tree inference.

The paper identifies a rapidly expanding multidrug-resistant M. tuberculosis clade originating from Russia and circulating in Moldova, posing a threat to MDR-TB control.

The paper introduces e3SIM, a simulator that models epidemiological, ecological, and evolutionary processes in pathogens, demonstrating its use with SARS-CoV-2 and Mycobacterium tuberculosis.

This study introduces SAASI, a method that improves ancestral state inference by accounting for sampling bias, and demonstrates its effectiveness in reconstructing H5N1 virus transmission in the US.

The paper derives a closed-form expression for clade size distribution under the Kingman coalescent model and extends it to time-varying niche numbers.

MixInfect2 accurately detects mixed Mycobacterium tuberculosis infections and estimates strain proportions, improving upon previous methods.

This study shows that including indels improves transmission reconstruction in a large Mtb outbreak by reducing clonal clusters and altering transmission networks.

The paper introduces network science-based tree shape statistics for phylogenetic trees, showing they perform as well as or better than traditional measures.

This paper introduces a method to estimate genetic similarity matrices using phylogenetic trees, showing consistency with genotype-based matrices and enabling analysis without genotype data.

This study uses genomic data and mathematical modeling to reconstruct transmission events in a large TB outbreak, identifying key predictors of transmission.

This paper introduces a polynomial-based method to compare phylogenetic tree shapes and applies it to simulated and real data to estimate parameters and select models.

This study shows that different genomic variant calling methods lead to inconsistent transmission inferences and phylogenies in Mycobacterium tuberculosis outbreaks.

The paper introduces a transmission-based clustering method for identifying outbreak clusters using whole genome sequencing data, showing it outperforms SNP threshold methods in some scenarios.

The paper introduces methods to compare phylogenetic trees with categorical tip labels, applying them to analyze differences between Mycobacterium tuberculosis trees built from typing and sequence data.

The paper develops a Bayesian method to reconstruct transmission trees from WGS data by integrating unobserved processes, showing improved results on simulated and real datasets.

This paper develops a method to infer transmission trees from phylogenies in partially sampled and ongoing outbreaks by incorporating within-host genetic diversity and unsampled intermediates.

The paper introduces a method to map phylogenetic trees, revealing distinct evolutionary patterns in Ebolavirus and anole lizards, with implications for vaccine development.

The paper develops a method to infer transmission times from pathogen genomes and applies it to declare a tuberculosis outbreak over.

This study shows that competition between resistant and sensitive pathogens determines whether aggressive or moderate antibiotic treatment better minimizes resistance.

The paper uses phylogenetic tree shapes to predict disease transmission patterns from genome data, finding that different transmission dynamics produce distinct tree structures.

This paper develops a Bayesian method to reconstruct disease outbreaks from genomic data, finding that even with high substitution rates, uncertainty remains in transmission inference.