Evolutionary trajectory and co-infection dynamics of human influenza A(H1N1) virus (2000-2025): an integrated...

Influenza A (H1N1) remains an important seasonal respiratory pathogen, but evidence on its evolutionary dynamics, reported co-detections, and surveillance priorities remains fragmented.

We conducted an evidence-mapping synthesis (2000-2025) integrating bibliometric analysis, expert-guided curation, and sequence/structure-informed interpretation. A total of 15,028 records were retrieved from PubMed, Web of Science, and Scopus, and 11,848 unique publications were retained after deduplication. GenBank-derived hemagglutinin (HA) sequences and Swiss-Model homology models were used to characterize mutational patterns and structural features. Literature-derived co-detection records were extracted from eligible publications and interpreted using a method-aware framework.

A post-2010 shift in the HA mutational landscape was observed, with recurrent substitutions at sites including S13, S146, S160, and S202. Structure-informed comparison of representative HA models identified a conformationally flexible segment spanning residues aa190-aa226, suggesting potential relevance to the receptor-binding microenvironment. Mapping of literature-derived co-detection records showed that RSV and SARS-CoV-2 were among the most frequently reported co-pathogens; however, these proportions reflected reporting composition across heterogeneous studies rather than population-level co-infection prevalence. In a China-focused module, G219A in Eurasian avian-like (EA) H1N1 strains was prioritized through protocol-constrained expert annotation requiring isolate-level evidence and was interpreted as a hypothesis-generating site of interest within the receptor-binding region rather than an algorithm-derived global bibliometric signal.

This study provides an integrated overview of H1N1 research evolution, HA mutational change, and reported co-detection patterns over the past 25 years. The findings support a tiered, method-aware multi-pathogen surveillance framework for preparedness, while underscoring that heterogeneous literature-derived co-detection data require standardized definitions, assay-aware interpretation, and local calibration before translation into clinical or public health decision-making.