Olav N. L. Aga

The paper uses evolutionary accumulation modelling to infer and forecast shared evolutionary pathways to multi-drug resistance across global pathogens.

The paper develops a method to identify the most parsimonious pathways of binary feature accumulation and convergent evolution, using a hypercubic transition network and cost function minimization.

This paper uses genomic data from 47k Klebsiella pneumoniae genomes to predict AMR evolutionary pathways across 102 countries, showing how resistance traits vary with geography and drug policy.

The paper introduces a clustering-based method to model dynamic accumulation processes in disease progression and antimicrobial resistance evolution using hypercubic hidden Markov models.

HyperTraPS-CT infers and predicts accumulation pathways in complex systems with flexible data and model structures, demonstrating its application to cancer and tuberculosis resistance.