Social learning of prescribing behavior can promote population optimum of antibiotic use

TL;DR

This paper models how social learning influences physicians' prescribing behavior, showing that prompt feedback and adaptive norms can lead to optimal antibiotic use and mitigate resistance evolution.

Contribution

It introduces a behavior-disease interaction model demonstrating how social learning can promote population-level antibiotic prescribing optimality.

Findings

Fast social learning stabilizes antibiotic use at the population optimum.

Feedback on resistance evolution guides prescribing behavior effectively.

Overuse of antibiotics can be prevented through adaptive social norms.

Abstract

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.