Speaker
Description
Accurately capturing epidemic dynamics requires accounting for how individuals adjust behaviour in response to perceived infection risk. Recently models have been developed to incorporate behavioural feedback \cite{ward_bayesian_2023}, but they currently rely on simplified, ad hoc representations of memory. For instance, many emphasize only recent case counts, neglecting the lasting influence of earlier epidemic experiences on current risk perception.
Here, we introduce a framework of Memory Mechanism Enhanced Behavioural Change (MEBC) models within a Bayesian SIR setting. Five memory formulations -- memoryless, sliding window, power-law, exponential, and reciprocal -- are considered, each reflecting a distinct way past epidemic information shapes present behaviour. A fully Bayesian data-augmented MCMC approach jointly estimates transmission and behavioural parameters while accounting for uncertainty in infectious periods.
Simulation results show that the MEBC framework provides accurate parameter recovery and remains robust under misspecified memory assumptions. Applications to the early COVID-19 outbreak in Miami-Dade County and the 2023–2024 influenza season in Manitoba demonstrate that incorporating an interpretable memory mechanism significantly improves model fit, underscoring the importance of collective memory in behavioural adaptation and disease transmission.
Bibliography
@article{ward_bayesian_2023,
title = {Bayesian modeling of dynamic behavioral change during an epidemic},
volume = {8},
issn = {24680427},
url = {https://linkinghub.elsevier.com/retrieve/pii/S2468042723000787},
doi = {10.1016/j.idm.2023.08.002},
language = {en},
number = {4},
urldate = {2026-03-20},
journal = {Infectious Disease Modelling},
author = {Ward, Caitlin and Deardon, Rob and Schmidt, Alexandra M.},
month = dec,
year = {2023},
pages = {947--963},
}
