  The course of an epidemic can be drastically altered by changes in human
behavior. Incorporating the dynamics of individual decision-making during an
outbreak represents a key challenge of epidemiology, faced by several modeling
approaches siloed by different disciplines. Here, we propose an epi-economic
model including adaptive, forward-looking behavioral response on a
heterogeneous networked substrate, where individuals tune their social activity
based on future health expectations. Under basic assumptions, we show that it
is possible to derive an analytical expression of the optimal value of the
social activity that matches the traditional assumptions of classic epidemic
models. Through numerical simulations, we contrast the settings of global
awareness -- individuals only know the prevalence of the disease in the
population -- with local awareness, where individuals explicitly know which of
their contacts are infected. We show that behavior change can flatten the
epidemic curve by lowering the peak prevalence, but local awareness is much
more effective in curbing the disease early with respect to global awareness.
Our work bridges classical epidemic modeling with the epi-economic approach,
and sheds light on the effects of heterogeneous behavioral responses in curbing
the epidemic spread.
