High-Resolution, Bias-Corrected Global and US SARS-CoV-2 Variant Surveillance Reveals Temporal Dynamics, Growth Rates, Co-Occurrence Networks, Variant Replacement Patterns, and Regional Disparities with Implications for Public Health

Abstract The continuous evolution of SARS-CoV-2 has led in the formation of numerous Variants of Concern (VOCs), Variants of Interest (VOIs), and Variants Under Monitoring (VUMs), each with distinct transmissibility, immunological escape potential, and regional prevalence patterns. Through the integration of bias-corrected prevalence estimates, temporal dynamics, growth rate comparisons, and co-occurrence patterns, we conducted a thorough, high-resolution comparative investigation of SARS-CoV-2 variations in populations in the US and around the world. We calculated the relative frequency of the most prevalent circulating variations using extensive genomic surveillance data, identifying significant distinctions between the global and US landscapes, such as the predominance of particular VOCs and VOIs in different geographical areas. variations having the most potential to outcompete others throughout time were highlighted by growth rate analyses, which also revealed fast expanding variations and possible regional replacement dynamics. Co-occurrence network analysis also revealed groups of variations that circulate often together, which may indicate selective benefits or epidemiological linkages. By making it easier to identify underrepresented variations, bias correction increased the precision of the dynamics and prevalence inferred. When taken as a whole, these analyses offer a quantitative framework for tracking the appearance, dissemination, and interactions of SARS-CoV-2 variations in different geographical areas. Our results provide practical information that public health officials may use to improve surveillance, lead targeted measures to reduce ongoing transmission, and advise immunization efforts. The importance of high-resolution, data-driven monitoring of SARS-CoV-2 evolution at both the national and international levels is highlighted by this integrated strategy that combines genomic prevalence, temporal, and network-based analysis.