Abstract for: How do Human Behaviors impact the rate at which a disease becomes Resistant to an Antibiotic?

Antibiotic resistance continues to undermine one of modern medicine’s most vital protections, reducing our ability to treat infections and safely perform routine medical procedures. This is a complex issue shaped by interactions between human behavior and biological processes. While natural bacterial mutations make it unlikely that this issue can be fully resolved, human actions represent leverage points in determining how quickly resistance emerges and spreads. This study develops a system dynamics model to simulate resistance for a single pathogen-antibiotic pair in the United States from 1950-2030. The model captures key feedback loops, time delays, and the accumulation processes influencing resistance growth. It focuses on behavioral drivers of the resistance issue, including prescribing accuracy, patient treatment compliance, and public hygiene, while also incorporating adaptive learning by clinicians and the public. Simulation results indicate that resistance emerges across all scenarios, but the timing and rate of growth vary substantially depending on behavioral conditions. Improvements in prescribing accuracy, treatment adherence, and hygiene significantly delay the onset of rapid resistance growth, extending the effective lifespan of antibiotics. Findings highlight antibiotic resistance as a human-biological system governed by reinforcing feedback processes. Coordinated improvements in behavior can meaningfully slow resistance accumulation, though they cannot fully prevent it. AI tools were used for editing, clarity improvement, and language refinement.