From detection to prediction: asymptomatic shedding-informed mass-balance modeling predicts travel-borne viral importation at airports.

Aircraft wastewater surveillance (AWS) has emerged as a non-invasive tool for travel-mediated pathogen detection, yet its utility remains largely descriptive without capacity to translate environmental concentrations into quantitative infection dynamics. This study aimed to advance AWS from passive pathogen detection to an active predictive framework by integrating mass balance back-calculation with hybrid-capture genomic surveillance. To resolve a critical parameter gap in mass-balance modeling-namely, the fecal shedding kinetics of asymptomatic influenza carriers, we prospectively enrolled 238 non-respiratory outpatients at Nanchang People's Hospital and quantified fecal influenza A/B virus loads (IAV /IBV) by RT-qPCR. Asymptomatic IAV and IBV shedding was detected across all age strata, with median viral loads of 5.9 and 5.0log₁₀ copies/g, respectively, and positivity rates ranging from 41.2% (adolescents) to 62.1% (children <10 years), thereby establishing the first empirical fecal shedding parameter set for carriage-inclusive mass-balance back-calculation. Between January and July 2025, we prospectively collected 240 aircraft wastewater samples from six Asia-Pacific flight routes and 60 longitudinal sewage samples from an international airport in Nanjing, China. RT-qPCR was conducted to quantify norovirus, influenza virus, mpox, and dengue virus. A mass balance back-calculation model was established to convert wastewater viral concentrations into estimated weekly numbers of infected inbound passengers. Viral genomes were enriched via hybrid-capture panels and subjected to high-resolution phylogenetic analysis. The predictive framework estimated weekly flight-imported infection cases for norovirus GII, influenza A virus and dengue virus, revealing distinct regional importation curves synchronized with epidemiological trends in countries of origin (95% concordance). Phylogenetic analysis resolved multiple independent viral introductions. Further employing a Susceptible-Exposed-Infectious-Recovered model, we forecasted 14-day community infection burden and validated predictions against clinical surveillance data, demonstrating significant concordance with IAV (r=0.71) and norovirus clinical visits (r=0.84). By bridging quantitative infection dynamics with lineage-resolution genomics, this study establishes a paradigm shift from qualitative pathogen detection to predictive outbreak intelligence.