A Data-Driven Sliding-window Pairwise Comparative Approach for the Estimation of Transmission Fitness of SARS-CoV-2 Variants and Construction of the Evolution Fitness Landscape

Estimating the transmission fitness of SARS-CoV-2 variants and understanding their evolutionary landscape is important for epidemiological forecasting. Existing methods are often constrained by their parametric natures and do not satisfactorily align with the observations during COVID-19. Here, we introduce a sliding-window data-driven pairwise comparison method, Differential Population Growth Rate (DPGR), that uses viral strains as internal controls to mitigate sampling biases. DPGR is applicable in time windows in which the logarithmic ratio of two variant subpopulations is approximately linear. We apply DPGR to genomic surveillance data and focus on Variants of Concern (VOCs) in multiple countries and regions. We found the log-linear assumption of DPGR can be found in appropriate time windows in many regions. We show that DPGR can provide estimates of transmission fitness and quantify the transmission advantage of key variants such as Omicron by location. We show that DPGR estimates agree with other methods for estimating pathogenic transmission. Furthermore, DPGR allowed us to construct viral fitness landscapes that capture the evolutionary trends of SARS-CoV-2, reflecting the relative changes of transmission traits for key genotypic changes represented by major variants. The straightforward log-linear regression approach of DPGR may also facilitate its easy adoption. This study shows that DPGR is a promising new tool in our repertoire for addressing future pandemics.