Preparing For the Next Pandemic: Learning Wild Mutational Patterns At Scale For Analyzing Sequence Divergence In Novel Pathogens

Abstract

AbstractAs we begin to recover from the COVID-19 pandemic, a key question is if we can avert such disasters in future. Current surveillance protocols generally focus on qualitative impact assessments of viral diversity 1. These efforts are primarliy aimed at ecosystem and human impact monitoring, and do not help to precisely quantify emergence. Currently, the similarity of biological strains is measured by the edit distance or the number of mutations that separate their genomic sequences 2–6, e.g. the number of mutations that make an avian flu strain human-adapted. However, ignoring the odds of those mutations in the wild keeps us blind to the true jump risk, and gives us little indication of which strains are more risky. In this study, we develop a more meaningful metric for comparison of genomic sequences. Our metric, the q-distance, precisely quantifies the probability of spontaneous jump by random chance. Learning from patterns of mutations from large sequence databases, the q-distance adapts to the specific organism, the background population, and realistic selection pressures; demonstrably improving inference of ancestral relationships and future trajectories. As important application, we show that the q-distance predicts future strains for seasonal Influenza, outperforming World Health Organization (WHO) recommended flu-shot composition almost consistently over two decades. Such performance is demonstrated separately for Northern and Southern hemisphere for different subtypes, and key capsidic proteins. Additionally, we investigate the SARS-CoV-2 origin problem, and precisely quantify the likelihood of different animal species that hosted an immediate progenitor, producing a list of related species of bats that have a quantifiably high likelihood of being the source. Additionally, we identify specific rodents with a credible likelihood of hosting a SARS-CoV-2 ancestor. Combining machine learning and large deviation theory, the analysis reported here may open the door to actionable predictions of future pandemics.