  Meta-population networks are effective tools for capturing population
movement across distinct regions, but the assumption of well-mixed regions
fails to capture the reality of population higher-order interactions. As a
multidimensional system capturing mobility characteristics, meta-population
networks are inherently complex and difficult to interpret when subjected to
resilience analysis based on N-dimensional equations. We propose a higher-order
meta-population model that captures large-scale global cross-regional mobility
and small-scale higher-order interactions within regions. Remarkably, we extend
the dimension-reduction approach, simplifying the N-dimensional higher-order
meta-population system into a one-dimensional equation by decomposing different
network behaviours into a single universal resilience function, thereby
allowing for convenient and accurate prediction of the system resilience. The
network structure and human mobility parameters can clearly and simply express
the epidemic threshold. Numerical experimental results on both real networks
and star networks confirm the accuracy of the proposed dimension-reduction
framework in predicting the evolution of epidemic dynamics on higher-order
meta-population networks. Additionally, higher-order interactions among
populations are shown to lead to explosive growth in the epidemic infection
size potentially. Population mobility causes changes in the spatial
distribution of infectious diseases across different regions.
