Integrating dynamical modeling and phylogeographic inference to characterize global influenza circulation

Abstract

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 countries 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 outbreaks to improve the flexibility and predictive power of modeling.

Keywords: Bayesian inference; influenza; metapopulation; phylogeography.

Fig. 1.

Marginal posterior inclusion probabilities associated with air travel data or with GLEAM-based fluxes comparing recurrent against Markovian travel, seasonal against annual fluxes and different peak times. For peak times, we performed an analysis comparing November 15 against December 15 and an analysis comparing December 15 to January 15, but we only show the latter for simplicity as December 15 outperformed November 15.

Fig. 2.

Marginal posterior inclusion probabilities associated with recurrent travel distribution of GLEAM fluxes for the three parameters $R_{max}$, $R_{min}$, and D, without and with residual predictor.

Fig. 3.

Annual epidemic profiles for 30 selected countries from FluNet H3N2 samples (colored) and simulations with the best-supported scenario (gray). For each seasonal region, selected countries are representative of the whole set for average correlation and its dispersion. Shaded areas show the 95% CI of the normalized incidence (Section 5 of the Supplementary Material). The * indicates countries for which no genetic data were available for calibration.

Fig. 4.

Dominant fluxes of cases for the two epochs. To enhance clarity, countries have been grouped into geographic areas. We have considered here the same region repartition as in (16). The plots show for each region the fluxes responsible for at least 60% of the importations. In the case of Europe, we show the top 20 countries in terms of importations, each featuring only the most significant importation flux. Fluxes between countries/areas are color-coded according to their country/area of origin.

Fig. 5.

Left: Number of imported cases (bars, left axis) for two consecutive years from a single stochastic realization—for the best parameterization obtained for H3N2. Right: ratio of imported vs. local cases (bars, left axis). In both plots, simulated incidence is reported with solid black line (right axis) for reference. On both left and right bars are assigned colors based on their respective area of origin (same repartition as in Fig. 4).