  The spreading dynamics of an epidemic and the collective behavioral pattern
of the population over which it spreads are deeply intertwined and the latter
can critically shape the outcome of the former. Motivated by this, we design 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. The framework also allows to
assess the effectiveness of different policy interventions on ensuring a
collective response that successfully eradicates the outbreak. Two case
studies, inspired by real-world diseases, are presented to illustrate the
potentialities of the proposed model.
