title: Modeling the effects of introducing a new antibiotic in a hospital setting: A case study.
abstract: The increase in antibiotic resistance continues to pose a public health risk as very few new antibiotics are being produced, and bacteria resistant to currently prescribed antibiotics is growing. Within a typical hospital setting, one may find patients colonized with bacteria resistant to a single antibiotic, or, of a more emergent threat, patients may be colonized with bacteria resistant to multiple antibiotics. Precautions have been implemented to try to prevent the growth and spread of antimicrobial resistance such as a reduction in the distribution of antibiotics and increased hand washing and barrier preventions; however, the rise of this resistance is still evident. As a result, there is a new movement to try to re-examine the need for the development of new antibiotics. In this paper, we use mathematical models to study the possible benefits of implementing a new antibiotic in this setting; through these models, we examine the use of a new antibiotic that is distributed in various ways and how this could reduce total resistance in the hospital. We compare several different models in which patients colonized with both single and dual-resistant bacteria are present, including a model with no additional treatment protocols for the population colonized with dual-resistant bacteria as well as models including isolation and/or treatment with a new antibiotic. We examine the benefits and limitations of each scenario in the simulations presented.
authors.affiliation: Department of Mathematics and Statistics and Institute for Quantitative Biology, East Tennessee State University, Johnson City, TN, United States. joynerm@etsu.edu
authors.forename: Michele L
authors.initials: ML
authors.lastname: Joyner
authors.forename: Cammey C
authors.initials: CC
authors.lastname: Manning
authors.forename: Brandi N
authors.initials: BN
authors.lastname: Canter
mesh_terms: D000900:Anti-Bacterial Agents / Q000627:therapeutic use*; D003198:Computer Simulation / Q000706:statistics & numerical data; D004352:Drug Resistance, Microbial; D006761:Hospitals / Q000706:statistics & numerical data*; D006801:Humans; D008954:Models, Biological
chemical_list: D000900:Anti-Bacterial Agents