Viral mutations and waning immunity play a key role in the spread of infectious diseases. Traditional compartmental models often assume that individuals acquire complete but temporary immunity after their infection, overlooking the complex dynamics of immune escape by emerging variants. To address this oversight, we built an agent-based model using empirical data from COVID-19 to investigate how viral evolution, mutation, and immune escape reinfections shape transmission dynamics. For the pathogen characteristics examined here, simulations with and without immune escape reinfections revealed that immune escape reinfections cause approximately 30% of infections and accelerate the accumulation of viral mutations. Sensitivity analyses using varying infection rates and evolutionary distances revealed that increasing the evolutionary distance required for immune escape reinfections caused a reduction in mutation counts. In addition, restricting agent interactions to localized connections in a ring-lattice network reduced infections by 47% and mutations by 95%. These findings demonstrate the importance of immune escape reinfections and agent-interaction networks in disease transmission, and offer insights to improve future epidemiological modelling, particularly in addressing mutations and their influence on transmission dynamics.