Over the past several years, monitoring the transmission fitness of emerging SARS-CoV-2 variants has been critical for pandemic forecasting and public health response. However, existing methods, such as basic reproductive number estimation or phylogenetic analysis, often face limitations in directly comparing variants that dominate at different times or in quantifying their relative advantages with robustness to sampling biases. Thus, developing a data-driven, comparative framework to estimate relative variant fitness and visualize evolutionary trends across time and geography is of paramount importance.

Recently, a research team led by Prof. Hong Qin from Old Dominion University and the University of Tennessee at Chattanooga published an article titled “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” in Quantitative Biology. The team introduced the Differential Population Growth Rate (DPGR), a novel pairwise comparison method that uses viral strains as internal controls within sliding time windows to mitigate surveillance biases. They applied DPGR to global genomic data from GISAID, focusing on Variants of Concern (VOCs) like Alpha, Delta, and Omicron across multiple countries and continents. Based on the pairwise fitness estimates, the team successfully constructed an evolutionary fitness landscape, visually capturing the shifting competitive advantages among key SARS-CoV-2 variants.

Figure 1 illustrates the constructed fitness landscape for major VOCs in the United States. The three-dimensional surface maps the relative transmission fitness between variant pairs, where the height represents the fitness advantage. The landscape clearly shows Omicron occupying the highest “peak,” indicating its superior transmission fitness relative to all earlier variants, with Delta and others positioned on the slopes below. This visualization provides an intuitive macro-view of the virus’s adaptive evolution throughout the pandemic.
DOI10.1002/qub2.70003