Abstract
Predicting phenotypic properties of a virus directly from its sequence data is an attractive goal for viral epidemiology. Here, we focus narrowly on the Orthocoronavirinae clade and demonstrate models that are powerfully predictive for a human-pathogen phenotype with 76.74% average precision and 85.96% average recall on the withheld test set groups, using only Orf1ab codon frequencies. We show alternative examples for other viral coding sequences and feature representations that do not perform well and discuss what distinguishes the models that are performant. These models point to a small subset of features, specifically 5 codons, that are critical to the success of the models. We discuss and contextualize how this observation may fit within a larger model for the role of translation in virus-host agreement.