Behavior-induced oscillations in epidemic outbreaks with distributed memory: beyond the linear chain trick using numerical methods
Alessia and\`o, Simone De Reggi, Francesca Scarabel, Rossana Vermiglio, Jianhong Wu

TL;DR
This paper models how behavior-driven memory effects can induce oscillations in epidemic outbreaks, extending analysis beyond traditional methods using numerical techniques to understand long-term dynamics and stability.
Contribution
It introduces a numerical approach to analyze epidemic models with distributed memory kernels, surpassing the linear chain trick limitations and exploring complex oscillatory behaviors.
Findings
Behavior adaptation can cause sustained epidemic waves without demographic factors.
Memory kernel shape influences epidemic wave period and peak.
Minimal contact levels affect the stability of the disease-free equilibrium.
Abstract
We considered a model for an infectious disease outbreak, when the depletion of susceptible individuals is negligible, and assumed that individuals adapt their behavior according to the information they receive about new cases. In line with the information index approach, we supposed that individuals react to past information according to a memory kernel that is continuously distributed in the past. We analyzed equilibria and their stability, with analytical results for selected cases. Thanks to the recently developed pseudospectral approximation of delay equations, we studied numerically the long-term dynamics of the model for memory kernels defined by gamma distributions with a general non-integer shape parameter, extending the analysis beyond what is allowed by the linear chain trick. In agreement with previous studies, we showed that behavior adaptation alone can cause sustained…
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Taxonomy
TopicsMathematical and Theoretical Epidemiology and Ecology Models · COVID-19 epidemiological studies · Evolutionary Game Theory and Cooperation
