Characterization and forecast of global influenza (sub)type dynamics

Abstract The (sub)type composition of seasonal influenza waves varies in space and time. (Sub)types tend to have different impacts on population groups; therefore, understanding the drivers of their co-circulation and anticipating their composition is important for epidemic preparedness. FluNet provides data on influenza specimens by (sub)type for more than 150 countries. However, due to surveillance variations across countries, global analyses usually focus on (sub)type compositions, a kind of data difficult to treat with advanced statistical methods. We used Compositional Data Analysis to circumvent the problem and study trajectories of annual (sub)type compositions of countries. First, we examined global trends from 2000 to 2023. We identified a few seasons which stood out for the strong within-country (sub)type dominance due to either a new virus/clade taking over (2003/2004 season, A/H1N1pdm pandemic) or (sub)types’ spatial segregation (COVID-19 pandemic). Second, we showed that geographical factors, most notably international mobility, concurred in shaping countries’ composition trajectories between 2010 and 2019. Trajectories clustered in two macroregions characterized by (sub)type alternation vs. persistent mixing. Finally, we defined five algorithms for forecasting the next year’s composition and found that incorporating the global history of (sub)type composition in a Bayesian Hierarchical Vector AutoRegressive model improved predictions compared with naive methods. The joint analysis of spatiotemporal dynamics of influenza (sub)types worldwide revealed a hidden structure in (sub)type circulation that can be used to improve predictions of the (sub)type composition of next year’s epidemic according to place. Competing Interest Statement The authors have declared no competing interest. Funding Statement This study was partially funded by EU grant 874850 MOOD to V.C. and is catalogued as MOOD 136. The contents of this publication are the sole responsibility of the authors and don't necessarily reflect the views of the European Commission. Additional supporting funding was provided by the ANR project DATAREDUX(ANR-19-CE46-0008-03) to V.C.; the Municipality of Paris through the programme Emergence(s) to C.P. and F.B.; the BEHAVE-MOD project funded through the Cascade Open Call 2023-01 for Spoke 4 of the PNRR INF-ACT Grant to F.B.; Cariparo Foundation through the program Starting Package to C.P.; Department of Molecular Medicine through the program SID from BIRD funding to C.P. Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: Data are available in the FluNet database of W.H.O.: https://www.who.int/tools/flunet I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes Footnotes An in-depth investigation of the factors influencing similarities in (sub)type compositions across countries has been added. - More extensive sensitivity analyses regarding the forecast of next year's (sub)type compositions have been included. - Robustness analyses considering an alternative definition of the influenza year (from autumn to autumn instead of spring to spring) have been added. Data Availability Code and data for reproducible analyses are available at https://github.com/FrancescoBonacina/coupled-dynamics-flu-subtypes/ . https://github.com/FrancescoBonacina/coupled-dynamics-flu-subtypes/