Deciphering Viral Antigenic Relationships Through Antigenic Cartography: A Narrative Review

Background: Antigenic cartography is a computational approach that quantitatively visualizes antigenic relationships among viral strains using serological data, including hemagglutination inhibition and virus-neutralization assays. Since its introduction in the early 2000s, it has substantially advanced the study of viral antigenic evolution by transforming complex serological measurements into interpretable antigenic maps, thereby facilitating assessment of antigenic diversity, immune escape, cross-reactivity, and vaccine–virus relationships. Objective: This review examines the historical development, methodological principles, applications, limitations, and emerging directions of antigenic cartography across clinically relevant viruses, with particular emphasis on its relevance to viral surveillance, antigenic drift monitoring and vaccine-strain selection. Methods: The literature was synthesized comparatively across major applications of antigenic cartography, encompassing human seasonal influenza and other viruses, including dengue, lyssaviruses, foot-and-mouth disease virus, Enterovirus A71, norovirus, rotavirus, SARS-CoV-2, and HIV. Particular attention was given to real-world applications, methodological advances, sources of uncertainty, and integration with genomic, structural, evolutionary, and computational approaches. Findings: Influenza remains the most extensively studied viral pathogen, where antigenic cartography has supported recognition of epidemiologically relevant antigenic transitions and informed vaccine decisions. Examples include the emergence of A/Fujian/411/2002-like H3N2 viruses during 2004–2005 and assessment of A/California/07/2009 during 2010–2011. Applications to other viruses demonstrate broader utility for characterizing antigenic landscapes and evaluating cross-protective immunity and vaccine performance. However, map reliability is influenced by serological assay variability, serum-panel composition, measurement noise, incomplete and geographically biased datasets. Limited inter-operability among serological, genomic, and structural datasets further restricts reproducibility and cross-study comparability. Future Directions and Conclusions: The field is transitioning from retrospective visualization toward predictive and mechanistically informed antigenic surveillance. Integration of artificial intelligence, protein language models, evolutionary modelling, structural vaccinology, and real-time genomic–serological surveillance could enable earlier prediction of antigenic drift, identification of emerging vaccine mismatches, and prioritization of candidate vaccine strains. Future platforms should incorporate standardized and automated serological measurements, inter-laboratory calibration, harmonized data standards, geographically representative datasets, experimental validation, and interpretable continuously updated models. Such integration could establish antigenic cartography as a scalable predictive framework for vaccine design and for strengthening preparedness against emerging viral threats.