Speaker
Description
Human behaviour can profoundly alter disease dynamics and the impact of public health interventions. Recognising this socio-epidemiological interplay, recent modelling efforts have increasingly incorporated behavioural responses triggered by perceived risk, disease outcomes, policy recommendations, or conformity pressure. We present a mathematical model that integrates two types of behavioural responses---reversible and irreversible--- to investigate the effect of risk-based and imitation-driven mechanisms on the proportion of population infected (i.e., the attack rate). Reversible behaviours---such as adopting non-pharmaceutical measures---are represented using threshold-like functional responses that capture sharp increases in protective behaviour once disease prevalence exceeds a critical level. Irreversible behaviours---such as vaccination---are modelled through a dynamic imitation process influenced by population-level adoption. Extending previous approaches, we demonstrate that behavioural feedbacks can generate non-monotonic relationships between the attack rate and disease transmissibility for a broad range of parameters representing behavioural effectiveness or adoption strength. We observed that, for sufficiently high imitation rates, distinct intrinsic transmission rates can yield the same attack rate, even though the corresponding epidemic durations and peak prevalence levels may differ substantially.
