Collective interactions, human mobility and viral evolution shaped the SARS-CoV-2 transmission in Mainland China
Year: 2025
Type: preprint
Abstract: Collective interaction of individuals in various settings is crucial for exposure to infections, encompassing complex viral interplay and amplifying infectious risk through phenomena such as social reinforcement, clustering and superspreading events, during the COVID-19 pandemic. However, standard epidemic models often inadequately capture such heterogeneity, overlooking the higher-order social structural. Spatiotemporal variation in transmission, an essential feature of the pandemic, remains poorly quantified at various scales, particularly in integrating high-resolution data streams and complex network approaches. We introduced a higher-order simplicial model that unifies human mobility data, genetic diversity and antigenic drift to systematically investigate the role of social reinforcement, spatiotemporal variation and genetic mutations in SARS-CoV-2 transmission. We found a median of 5.3% to 14.4% of infections across provinces were attributed to social reinforcement, while cluster heterogeneity contributed to a median of 17%–71% increase in susceptibility. Multiple viral interactions elevated transmissibility by 68%–70% across the periods of dominant variants. The reconstructed transmission networks underscored distinct spatiotemporal variation, with dynamic critical locations, varying mobility patterns, and evolving geographic cluster structures, by assessing complex networks. The influence of human mobility was found to be positive on transmission, effective distance was negatively associated with infection risks, while greater genetic diversity and antigenic drift were linked to higher susceptibility and transmissibility. Our proposed data-driven higher-order framework could help us to understand epidemics better by accounting the role of collective interactions, population mobility, and genetic mutation in transmission, which could inform the targeted interventions to mitigate SARS-CoV-2 and other respiratory pathogens.
Source: medRxiv
Authors: Dong Wang, Yi Wang, Oswaldo Gressani, Jiaqi Chen, Yuxin Tao, Hao Wang, Shanshan Li, Dongxuan Chen, Eric H. Y. Lau, Yi Zhao, Peng Wu, Qingpeng Zhang, Benjamin J. Cowling, Sheikh Taslim Ali
Subfield: Modeling and Simulation
Field: Mathematics
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7116044147