Memory mechanisms for behavioural change in Bayesian individual-level spatial epidemic models

Accurate modelling of infectious disease transmission often requires capturing how individuals adjust their behaviours in response to evolving epidemic conditions. While recently developed behavioural change epidemic models attempt to acknowledge such dynamics, the role of memory in shaping perceived risk has been treated in an ad hoc fashion. This study develops a data-driven framework of memory enhanced behavioural change individual-level models (MEBC-ILMs) that incorporate four distinct memory specifications: memoryless, sliding window, power-law decay, and exponential decay. These models allow behavioural responses to reflect varying assumptions about how past epidemiological information informs risk perception, with memory features inferred from epidemic data. Simulation experiments show that MEBC-ILMs can reliably recover key transmission and behavioural parameters under different memory settings and exhibit robust predictive performance even when the assumed memory structure differs from the true process. In contrast, the basic BC-ILM can perform poorly when memory affects present behaviour but is not accounted for in the model. Applying our framework to data from the 2001 U.K. foot and mouth disease epidemic illustrates how it can represent behavioural effects and explore plausible memory structures when fitted to real-world data.