Science Daily  July 21, 2023 An international team of researchers (USA – Scripps Research Institute, China) developed a machine learning approach using Gaussian process-based spatial covariance (SCV) to track the impact of spatial-temporal mutational events driving host-pathogen balance in biology. They showed how SCV could be applied to understanding the response of evolving covariant relationships linking the variant pattern of virus spread to pathology for the entire SARS-CoV-2 genome on a daily basis. The GP-based SCV relationships in conjunction with genome-wide co-occurrence analysis provided an early warning anomaly detection (EWAD) system for the emergence of variants of concern (VOCs). EWAD […]