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Massimo Ciccozzi

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Open access Sep 2026

Genetic Variability and Evolutionary Dynamics of A(H1N1)pdm09 in Italy Before, During, and After the COVID-19 Pandemic

Influenza A(H1N1)pdm09 remains one of the predominant seasonal influenza viruses worldwide and continues to evolve under the combined effects of host immunity, vaccination, and changing epidemiological conditions. However, the long-term impact of the COVID-19 pandemic on its evolutionary dynamics remains poorly understood. We investigated the genetic variability and phylodynamic evolution of the hemagglutinin (HA) and neuraminidase (NA) genes of Italian A(H1N1)pdm09 viruses collected between 2009 and 2026. Time-calibrated phylodynamic analyses, Bayesian Skyline Plots (BSPs), Lineages Through Time (LTT) graphs, principal component analysis (PCA), and codon-based selection analyses were used to characterize long-term evolutionary patterns. Both HA and NA followed continuous evolutionary trajectories, with a marked post-2020 genetic shift associated with the emergence of 6B.1A.5a.2-related clades. HA showed greater evolutionary variability than NA, whereas selection analyses identified only one positively selected site in HA and none in NA, consistent with reduced adaptive diversification in recent strains. Phylodynamic analyses revealed a marked decline in effective population size and lineage accumulation during the COVID-19 pandemic, followed by renewed expansion after 2022. Overall, Italian A(H1N1)pdm09 viruses exhibited reduced genetic diversity, limited evidence of positive selection, and coordinated genomic restructuring following the pandemic. These findings provide new insights into the long-term evolutionary dynamics of A(H1N1)pdm09 in Italy and reinforce the importance of sustained genomic surveillance for anticipating evolutionary changes and informing evidence-based public health strategies.

Maria Perra, Ilaria Deplano, I. Azzena et al. · 0 citations
Jul 2026

Comparing Mpox epidemic controls through simulations and explainable graph convolution networks: A case study in the Republic of the Congo and Nigeria

The study supports the potential usefulness of combining contact-based models with explainable graph neural networks for scenario-based epidemic analysis and suggests that the k-GCN model captures relevant temporal and structural dependencies in the simulated graph-organized data.

Francesco Branda, G. Ceccarelli, Massimo Ciccozzi et al. · 0 citations

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