  The rise and spread of antibiotic resistance causes worsening medical cost
and mortality especially for life-threatening bacteria infections, thereby
posing a major threat to global health. Prescribing behavior of physicians is
one of the important factors impacting the underlying dynamics of resistance
evolution. It remains unclear when individual prescribing decisions can lead to
the overuse of antibiotics on the population level, and whether population
optimum of antibiotic use can be reached through an adaptive social learning
process that governs the evolution of prescribing norm. Here we study a
behavior-disease interaction model, specifically incorporating a feedback loop
between prescription behavior and resistance evolution. We identify the
conditions under which antibiotic resistance can evolve as a result of the
tragedy of the commons in antibiotic overuse. Furthermore, we show that fast
social learning that adjusts prescribing behavior in prompt response to
resistance evolution can steer out cyclic oscillations of antibiotic usage
quickly towards the stable population optimum of prescribing. Our work
demonstrates that provision of prompt feedback to prescribing behavior with the
collective consequences of treatment decisions and costs that are associated
with resistance helps curb the overuse of antibiotics.
