A mathematical modelling framework for the “stop when you feel better” approach to antibiotic prescribing

To prevent antimicrobial resistance (AMR), antibiotic courses are traditionally prescribed to completion, overlooking their collateral impact on the commensal microbiota. We developed a deterministic within-host model linking pathogen growth, immune responses, antibiotic action, and commensal dynamics to explore how treatment duration influences resistance development. Simulations across community-acquired infection parameters showed that 71% of cases successfully treated with a standard 7-day regimen could also be cured with shorter “Stop When Better” (SWB) courses, where treatment cessation was triggered by a predefined threshold in pathogen abundance, and 97% by SWB plus 1 day. Compared with standard treatment, SWB+1 approaches reduced overall antibiotic exposure and resistance emergence in commensal niches while preserving efficacy. While this threshold-based definition of “feeling better” offers a clear theoretical framework, it would require translation into clinical cues. Therefore, although these findings suggest that response-based antibiotic stopping rules could inform future adaptive prescribing strategies, further empirical validation is essential.