| Abstract: |
| \begin{abstract}
During disease outbreaks, individuals adopt preventive measures based on perceived infection risk and the associated costs. These behavioral responses, in turn, influence disease transmission dynamics. In this talk, we propose a co-evolutionary model that integrates adaptive behavioral changes into a classical epidemic framework to investigate their interplay. We show that such adaptive behavior can give rise to complex dynamical phenomena, including Hopf bifurcations, sustained periodic oscillations, and even some counterintuitive outcomes. Furthermore, by incorporating real-world behavioral data, we validate the proposed model and demonstrate its ability to capture key features of observed epidemic patterns.
\end{abstract} |
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