  The last decade saw the advent of increasingly realistic epidemic models that
leverage on the availability of highly detailed census and human mobility data.
Data-driven models aim at a granularity down to the level of households or
single individuals. However, relatively little systematic work has been done to
provide coupled behavior-disease models able to close the feedback loop between
behavioral changes triggered in the population by an individual's perception of
the disease spread and the actual disease spread itself. While models lacking
this coupling can be extremely successful in mild epidemics, they obviously
will be of limited use in situations where social disruption or behavioral
alterations are induced in the population by knowledge of the disease. Here we
propose a characterization of a set of prototypical mechanisms for
self-initiated social distancing induced by local and non-local
prevalence-based information available to individuals in the population. We
characterize the effects of these mechanisms in the framework of a
compartmental scheme that enlarges the basic SIR model by considering separate
behavioral classes within the population. The transition of individuals in/out
of behavioral classes is coupled with the spreading of the disease and provides
a rich phase space with multiple epidemic peaks and tipping points. The class
of models presented here can be used in the case of data-driven computational
approaches to analyze scenarios of social adaptation and behavioral change.
