Age-Structured Epidemic Model with Spatial Heterogeneity

The COVID-19 pandemic underscored the profound influence of demographic and geographical factors on the trajectory and impact of infectious diseases. Understanding how variations in age, susceptibility, contact behaviour, immune response, and healthcare access contribute to disease spread is crucial for designing effective public health interventions. The paper addresses this need by developing and applying a spatially heterogeneous, age-structured Susceptible-Vaccinated-Infected-Recovered-Deceased (SVIRD) epidemic model. Governed by a system of partial differential equations (PDEs), this model accounts for the temporal evolution of epidemiological compartments, age progression, and spatial movement. By integrating an age and location-dependent transmission kernel, the framework provides a robust tool for assessing the impact of age-specific behaviours and geographic factors on COVID-19 transmission dynamics. The normalization of the population using a demographic steady-state distribution enhances the model's analytical tractability, facilitating theoretical analyses such as the derivation of equilibrium states and the basic reproduction number. Ultimately, this framework aims to inform targeted control strategies by identifying high-risk age groups and geographic hotspots, thereby contributing to more precise pandemic response planning and resource allocation.