Genomic epidemiological models describe pathogen evolution across fitness valleys

Abstract

Genomics is fundamentally changing epidemiological research. However, exploring hypotheses about pathogen evolution in different epidemiological contexts poses new challenges. Models intertwining pathogen epidemiology and genomic evolution can help understand processes such as the emergence of novel pathogen genotypes with higher transmission or resistance to treatment. In this work, we present Opqua, a computational framework for flexible simulation of pathogen epidemiology and evolution. 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 selective pressures. The results show the potential of genomic epidemiological modeling as a tool in infectious disease research.

Measurably recombining malaria parasites

“…settings [59]. These models are used to simulate data under arbitrarily complex scenarios whose parameters are known.…”

“…the R package SIMPLEGEN, https://mrc-ide.github.io/SIMPLEGEN/ ). Agent-based models linked to genomic processes have been used to estimate R 0 and changes in transmission intensity [ 57 , 4 ], to investigate the relationship between different descriptive statistics of parasite genetic data and transmission intensity [ 58 , 55 ], to study the effect of heterogeneity on the spatial distribution of multiclonal infections and on the stability of transmission [ 38 ], and to study the effect of selective pressures on evolution under different transmission settings [ 59 ]. These models are used to simulate data under arbitrarily complex scenarios whose parameters are known.…”