A theoretical framework for agent-based modelling of infectious disease dynamics under misinformation and vaccine hesitancy

The relevance of this study stems from the growing importance of modelling not only the biological transmission of infectious diseases but also the behavioural and informational factors that shape real-world epidemic dynamics. The subject of the research is the development of an agent-based simulation framework capable of capturing the complex interactions between epidemiological processes, vaccination behaviour, and misinformation propagation. The study aims to propose and evaluate a modular, theoretically grounded model that simulates the spread of infection while accounting for belief-driven decision-making and dynamic social influence. To achieve this, the tasks included analyzing the current state of agent-based epidemic models, formalizing a system architecture with cognitive and logistical subsystems, and conducting scenario-based simulations to explore the effects of misinformation and behavioural resistance on vaccination uptake and epidemic outcomes. The methodology is based on a discrete-time SEIRDV structure extended with agent-level belief states, social influence mechanisms, and dynamic vaccination decisions. The model was implemented in Python and tested through a case study simulating a COVID-like outbreak in a synthetic population. The results demonstrate that even modest behavioural resistance can significantly increase mortality and delay epidemic control, while counter-misinformation interventions if applied early and at sufficient intensity, can improve vaccine coverage and reduce disease burden. The study concludes that integrating behavioural and informational dynamics into epidemic models provides a more realistic and policy-relevant tool for analyzing communication strategies, vaccine rollout scenarios, and public health interventions under uncertainty.