ViraFit: Tunable Fitness Model for Viral Evolution Within a Contact Network

Abstract

The spread of a virus can be modeled as the diffusion of virion populations along the edges of a host contact network. Most prior research assumes that the virus maintains the same genome throughout the diffusion process, and, consequently, the genome itself is of no modeling interest. Eletreby et al. do consider a model of multiple variants in which a virus can mutate within a host into another variant before transmission across an edge. Still, they fail to model the genome or any established fitness models. We incorporate three biological notions, fitness landscapes, viral quasispecies, and genome structure, to more accurately model the diffusion of a virus with mutations. We investigate established fitness landscape models and various simulated contact networks. We simulate the diffusion process across the contact network, incorporating co-occurring evolutionary and epidemiological processes. In our proof-of-concept simulations, while determining which variant should infect an exposed individual, we consider the fitness values of the variants. ViraFit tunes the ruggedness of the fitness landscape by varying several attributes: genome length, network size, mutation rate, infection probability, and infection time. Our simulation results demonstrate the importance of including the fitness value of each variant and how the fitter ones persist over time.