The effect of vaccination on COVID-19 transmission dynamics with comorbidity using reaction-diffusion model

The global emergence of COVID-19 and its widespread transmission posed a formidable challenge for the global medical community. While vaccinations succeeded in mitigating the severity and fatality of the infection, a new challenge emerged: addressing transmission in the presence of comorbidities. A comprehensive mathematical model has been developed to address this issue, incorporating elements such as nonpharmaceutical interventions, vaccination strategies, comorbidity factors, limited healthcare resources, and the impact of nosocomial transmission. This updated model is formulated as a set of nonlinear partial differential equations under the category of reaction-diffusion models, aiming to provide a more accurate representation of the dynamics and interactions involved in spreading infectious diseases in a given population. The methodology employed involves a comprehensive analysis of the master model system's qualitative characteristics, focussing on the stability of its constituent subsystems. The model's dynamical system is subjected to numerical solutions, enabling a detailed exploration of its behaviour under various conditions. A rigorous parametric variation is carried out to understand the model's response to different parameter values. The novelty of this research is rooted in its pioneering approach to bridging the gap between theory and real-world observations. By rigorously validating theoretical results against empirical experimental data, the research aims to provide valuable insights into the dynamics of the ongoing pandemic. The outcomes generated by the present model system are expected to offer a deeper and more comprehensive understanding of the pandemic's behaviour and transmission patterns, playing a pivotal role in advancing the field of theoretical modelling.