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.

Comorbidity—the co-occurrence of multiple diseases in a patient—complicates diagnosis, treatment, and prognosis. Understanding how diseases connect at a molecular level is crucial, especially in aging populations and complex conditions like COVID-19, where comorbidities worsen outcomes. While network medicine has used the human interactome—a map of protein-protein interactions—to study these links, accurately predicting comorbid pairs remains challenging.