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High_order_modelling

Abstract: The dynamics of SARS-CoV-2 transmission highlight the need to integrate human higher-order collective behavioral, spatial, and viral evolutionary factors into epidemic modeling. To capture social reinforcement phenomena, embedded in human higher-order interaction and often overlooked by standard models, we introduce a higher-order simplicial framework that combines human mobility, genomic diversity, and antigenic drift to examine how higher-order interactions and viral evolution shape transmission dynamics. During the pandemic, social reinforcement and cluster heterogeneity amplify infection risk, while higher-order interactions with multiple infectors increase transmissibility. Reconstructed networks reveal shifting critical locations, pathways, and evolving spatiotemporal cluster structures. Genetic diversity and antigenic drift correlate with increased susceptibility and transmissibility. These findings underscore the importance of data-driven approaches that account for higher-order interactions, human mobility and viral evolution, informing targeted interventions for SARS-CoV-2 and other respiratory pathogens. pathogens.

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Higher-order interactions, human mobility and viral evolution shaped the SARS-CoV-2 transmission in Mainland China Doi: https://doi.org/10.64898/2025.12.17.25342513

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The study establishes a data-driven, higher-order modelling framework that assimilates epidemiological, geographic, genomic data steams with complex network approaches to delineate the foundational role of higher-order interaction in shaping collective transmission dynamics and the spatiotemporal evolution of transmission networks across regions.

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