Abstract
Genomics is fundamentally changing epidemiological research. However, systematically exploring hypotheses in pathogen evolution requires new modeling tools. Models intertwining pathogen epidemiology and genomic evolution can help understand processes such as the emergence of novel pathogen genotypes with higher transmissibility or resistance to treatment. In this work, we present Opqua, a flexible simulation framework that explicitly links epidemiology to sequence evolution and selection. We use Opqua to study determinants of evolution across fitness valleys. We confirm that competition can limit evolution in high transmission environments and find that low transmission, host mobility, and complex pathogen life cycles facilitate reaching new adaptive peaks through population bottlenecks and decoupling of selective pressures. The results show the potential of genomic epidemiological modeling as a tool in infectious disease research.
Competing Interest Statement
The authors have declared no competing interest.
Footnotes
Vladimir Corredor is added as an author, given he was instrumental in making this work. He has seen and discussed the results of the project as it developed, and has now edited and approved of the manuscript. All authors approve of the change. Additionally, supplementary figures S7, S8 are included based on peer review feedback. Aesthetic edits to Figs 1, 5 and clarifications throughout the text.