A natural history of AMR in Klebsiella pneumoniae: Global diversity, predictors, and predictions of evolutionary pathways

Antimicrobial resistance (AMR) is a substantial and growing global health burden. Understanding, and predicting, its evolution in specific pathogens will help responses across scales from individual patient cases to large-scale policy. Data-driven approaches to this question often focus more on genomes and less on the evolutionary dynamics generating these genomes -- or on what factors influence these dynamics. Here, we use global data on AMR features in Klebsiella pneumoniae with hypercubic transition path sampling (HyperTraPS), a machine learning approach, for Bayesian inference of the evolutionary pathways of AMR in K. pneumoniae (KpAMR) in 102 different countries, territories and areas. We identify "globally consistent" evolutionary behaviours that hold across countries, and "globally divergent" behaviours including carbapenem and fluoroquinolone resistance that vary across countries. We show how these divergent dynamics covary both with public health superregion and drug use policy, and reveal competing evolutionary pathways within and between countries. Using newly-sequenced data across several decades from sub-Saharan Africa, we show that this inferred global roadmap of KpAMR evolution successfully predicts prospective evolutionary dynamics. Together, we hope that the ability to characterize and predict evolutionary dynamics of AMR acquisition, connected to socio-economic and drug policy predictors, will help strengthen our understanding of AMR evolution worldwide.