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 and response. FluNet provides data on influenza specimens by (sub)type for more than one hundred fifty countries. However, due to surveillance variations across countries, global analyses usually focus on (sub)type compositions, a kind of data which is 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 2022. 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 the composition trajectories of countries between 2010 and 2019 clustered in two macroregions characterized by (sub)type alternation vs. persistent mixing. Finally, we defined five algorithms for forecasting the next-year composition and we found that taking into account 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.