Modeling antimicrobial cycling and mixing: Differences arising from an individual-based versus a population-based perspective

In order to manage bacterial infections in hospitals in the face of antibiotic resistance, the two treatment protocols “mixing” and “cycling” have received considerable attention both from modelers and clinicians. However, the terms are not used in exactly the same way by both groups. This comes because the standard modeling approach disregards the perspective of individual patients. In this article, we investigate a model that comes closer to clinical practice and compare the predictions to the standard model. We set up two deterministic models, implemented as a set of differential equations, for the spread of bacterial infections in a hospital. Following the traditional approach, the first model takes a population-based perspective. The second model, in contrast, takes the drug use of individual patients into account. The alternative model can indeed lead to different predictions than the standard model. We provide examples for which in the new model, the opposite strategy maximizes the number of uninfected patients or minimizes the rate of spread of double resistance. While the traditional models provide valuable insight, care is hence needed in the interpretation of results