Global seasonal influenza circulation involves a complex interplay between local (seasonality, demography, host immunity) and global factors (international mobility) shaping recurrent epidemic patterns. No studies so far have reconciled the two spatial levels, evaluating the coupling between national epidemics, considering heterogeneous coverage of epidemiological and virological data, integrating different data sources. We propose a novel combined approach based on a dynamical model of global influenza spread (GLEAM), integrating high-resolution demographic and mobility data, and a generalized linear model of phylogeographic diffusion that accounts for time-varying migration rates. Seasonal migration fluxes across global macro-regions simulated with GLEAM are tested as phylogeographic predictors to provide model validation and calibration based on genetic data. Seasonal fluxes obtained with a specific transmissibility peak time and recurrent travel outperformed the raw air-transportation predictor, previously considered as optimal indicator of global influenza migration. Influenza A subtypes supported autumn-winter reproductive number as high as 2.25 and an average immunity duration of 2 years. Similar dynamics were preferred by influenza B lineages, with a lower autumn-winter reproductive number. Comparing simulated epidemic profiles against FluNet data offered comparatively limited resolution power. The multiscale approach enables model selection yielding a novel computational framework for describing global influenza dynamics at different scales - local transmission and national epidemics vs. international coupling through mobility and imported cases. Our findings have important implications to improve preparedness against seasonal influenza epidemics. The approach can be generalized to other epidemic contexts, such as emerging disease out-breaks to improve the flexibility and predictive power of modeling.

The authors have declared no competing interest.

PL, VC, CP, ECGB and FP acknowledge funding from EU Horizon 2020 grants MOOD (H2020-874850, publication cataloged as MOOD 102). VC acknowledges support from Horizon Europe grant ESCAPE (101095619) and Agence Nationale de la Recherche projects DATAREDUX (ANR-19-CE46-0008-03). PL, MAS and AR acknowledge funding from the European Research Council under the European Union's Horizon 2020 research and innovation programme (grant agreement no. 725422-ReservoirDOCS) and from the Wellcome Trust Collaborative Award, 206298/Z/17/Z. MAS acknowledges support from US National Institutes of Health grants U19 AI135995, R01 AI153044 and R01 AI162611. PL acknowledges support by the Special Research Fund, KU Leuven (`Bijzonder Onderzoeksfonds', KU Leuven, OT/14/115), and the Research Foundation -- Flanders (`Fonds voor Wetenschappelijk Onderzoek -- Vlaanderen', G066215N, G0D5117N and G0B9317N). TB is a Howard Hughes Medical Institute Investigator and is supported by NIH NIGMS R35 GM119774.

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