The outcome of an epidemic outbreak can be critically shaped by the collective behavioural response of the population. Likewise, individual decision-making is highly influenced by the overwhelming pressure of epidemic spreading. However , existing models lack the ability to capture this complex interdependence over the entire course of the epidemic. We introduce a novel parsimonious network model , grounded in evolutionary game theory, in which decision-making and epidemics co-evolve, shaped by an interplay of factors mapped onto a minimal set of model parameters ---including government-mandated interventions, socio-economic costs, perceived infection risks and social influences. This interplay gives rise to a range of characteristic phenomenathat can be captured within this general framework, such as sustained periodic outbreaks, multiple epidemic waves, or prompt behavioural response ensuring a successful eradication of the disease. The model's potentialities are demonstrated by three case studiesbased on real-world gonorrhoea, 1918--19 Spanish flu and COVID-19 .
We introduce a parsimonious game-theoretic behavioral--epidemic model, in which an interplay of realistic factors shapes the co-evolution of individual decision-making and epidemics on a network. Although such a co-evolution is deeply intertwined in the real-world , existing models schematize population behavior as instantaneously reactive, thus being unable to capture human behavior in the long term. Our model offers a unified framework to model and predict complex emergent phenomena, including successful collective responses, periodic oscillations, and resurgent epidemic outbreaks, as illustrated through two real-world case studies .
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The outcome of an epidemic outbreak can be critically shaped by the collective behavioural response of the population. Likewise,
We introduce a parsimonious game-theoretic behavioral--epidemic model, in which an interplay of realistic factors shapes the co-evolution of
R
is highly influenced by the overwhelming pressure of epidemic spreading. However
and epidemics on a network. Although such a co-evolution is deeply intertwined in the real-world
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lack the ability to capture this complex interdependence over the entire course of the epidemic. We introduce a novel parsimonious network model , grounded in evolutionary game theory, in which decision-making and epidemics co-evolve, shaped by an interplay of factors mapped onto a minimal set of model parameters ---including government-mandated interventions, socio-economic costs, perceived infection risks and social influences. This interplay gives rise to a range of characteristic phenomenathat can be captured within this general framework, such as sustained periodic
schematize population behavior as instantaneously reactive, thus being unable to capture human behavior in the long term. Our model offers a unified framework to model and predict complex emergent phenomena, including successful collective responses, periodic oscillations, and resurgent epidemic
R
multiple epidemic waves, or prompt behavioural response ensuring a successful eradication of the disease. The model's potentialities are demonstrated by three case studiesbased on real-world gonorrhoea, 1918--19 Spanish flu and COVID-19
as illustrated through two real-world case studies