Collective Interactions, Human Mobility and Viral Evolution Shaped the SARS-CoV-2 Transmission in Mainland China

Preprint

📄 Read Preprint

Abstract

This work proposes a spatial higher-order mathematical modeling framework combined with complex network analysis, leveraging high-resolution surveillance data on epidemiology, human mobility, and viral evolution across mainland China. The framework accounts for collective interactions—including social reinforcement within group settings—and integrates genomic surveillance to disentangle how mobility patterns, NPI stringency, and viral genetic dynamics jointly shaped provincial and national transmission trajectories.

Key findings show that social reinforcement accounted for 5.3%–14.4% of infections across provinces, while cluster heterogeneity contributed a 17%–71% increase in susceptibility. The study demonstrates that standard compartmental models systematically underestimate transmission risk in high-density collective settings.

Key Contributions

• Proposed a higher-order epidemic modeling framework capturing collective interactions beyond pairwise contacts
• Integrated human mobility, viral phylogenetics, and NPI data into a unified spatiotemporal model
• Quantified the role of social reinforcement and population heterogeneity in driving provincial-level variation

Technologies

Python   Mathematical Modeling (Higher-order ODE)   Complex Network Analysis   Phylogenetics   Spatiotemporal Statistics

Recommended citation: Wang, D., Wang, Y., Gressani, O., Chen, J., Tao, Y., Wang, H., Li, S., Chen, D., Lau, E. H. Y., Zhao, Y., Wu, P., Zhang, Q., Cowling, B. J., & Ali, S. T. (2025). "Collective Interactions, Human Mobility and Viral Evolution Shaped the SARS-CoV-2 Transmission in Mainland China." medRxiv preprint.
Download Paper