Modeling the effects of introducing a new antibiotic in a hospital setting: A case study

  • Received: 01 August 2011 Accepted: 29 June 2018 Published: 01 July 2012
  • MSC : Primary: 92B99.

  • 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.

    Citation: Michele L. Joyner, Cammey C. Manning, Brandi N. Canter. Modeling the effects of introducing a new antibiotic in a hospital setting: A case study[J]. Mathematical Biosciences and Engineering, 2012, 9(3): 601-625. doi: 10.3934/mbe.2012.9.601

    Related Papers:

    [1] Xiaxia Kang, Jie Yan, Fan Huang, Ling Yang . On the mechanism of antibiotic resistance and fecal microbiota transplantation. Mathematical Biosciences and Engineering, 2019, 16(6): 7057-7084. doi: 10.3934/mbe.2019354
    [2] Hermann Mena, Lena-Maria Pfurtscheller, Jhoana P. Romero-Leiton . Random perturbations in a mathematical model of bacterial resistance: Analysis and optimal control. Mathematical Biosciences and Engineering, 2020, 17(5): 4477-4499. doi: 10.3934/mbe.2020247
    [3] Avner Friedman, Najat Ziyadi, Khalid Boushaba . A model of drug resistance with infection by health care workers. Mathematical Biosciences and Engineering, 2010, 7(4): 779-792. doi: 10.3934/mbe.2010.7.779
    [4] Jing Jia, Yanfeng Zhao, Zhong Zhao, Bing Liu, Xinyu Song, Yuanxian Hui . Dynamics of a within-host drug resistance model with impulsive state feedback control. Mathematical Biosciences and Engineering, 2023, 20(2): 2219-2231. doi: 10.3934/mbe.2023103
    [5] Rujing Zhao, Xiulan Lai . Evolutionary analysis of replicator dynamics about anti-cancer combination therapy. Mathematical Biosciences and Engineering, 2023, 20(1): 656-682. doi: 10.3934/mbe.2023030
    [6] Edgar Alberto Vega Noguera, Simeón Casanova Trujillo, Eduardo Ibargüen-Mondragón . A within-host model on the interactions of sensitive and resistant Helicobacter pylori to antibiotic therapy considering immune response. Mathematical Biosciences and Engineering, 2025, 22(1): 185-224. doi: 10.3934/mbe.2025009
    [7] Jhoana P. Romero-Leiton, Kernel Prieto, Daniela Reyes-Gonzalez, Ayari Fuentes-Hernandez . Optimal control and Bayes inference applied to complex microbial communities. Mathematical Biosciences and Engineering, 2022, 19(7): 6860-6882. doi: 10.3934/mbe.2022323
    [8] Hamdy M. Youssef, Najat A. Alghamdi, Magdy A. Ezzat, Alaa A. El-Bary, Ahmed M. Shawky . A new dynamical modeling SEIR with global analysis applied to the real data of spreading COVID-19 in Saudi Arabia. Mathematical Biosciences and Engineering, 2020, 17(6): 7018-7044. doi: 10.3934/mbe.2020362
    [9] Qimin Huang, Mary Ann Horn, Shigui Ruan . Modeling the effect of antibiotic exposure on the transmission of methicillin-resistant Staphylococcus aureus in hospitals with environmental contamination. Mathematical Biosciences and Engineering, 2019, 16(5): 3641-3673. doi: 10.3934/mbe.2019181
    [10] Urszula Ledzewicz, Shuo Wang, Heinz Schättler, Nicolas André, Marie Amélie Heng, Eddy Pasquier . On drug resistance and metronomic chemotherapy: A mathematical modeling and optimal control approach. Mathematical Biosciences and Engineering, 2017, 14(1): 217-235. doi: 10.3934/mbe.2017014
  • 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.


  • This article has been cited by:

    1. Anna Camilla Birkegård, Tariq Halasa, Nils Toft, Anders Folkesson, Kaare Græsbøll, Send more data: a systematic review of mathematical models of antimicrobial resistance, 2018, 7, 2047-2994, 10.1186/s13756-018-0406-1
    2. Ashit Trivedi, Richard E Lee, Bernd Meibohm, Applications of pharmacometrics in the clinical development and pharmacotherapy of anti-infectives, 2013, 6, 1751-2433, 159, 10.1586/ecp.13.6
    3. Uri Obolski, Gideon Y. Stein, Lilach Hadany, Mark M. Tanaka, Antibiotic Restriction Might Facilitate the Emergence of Multi-drug Resistance, 2015, 11, 1553-7358, e1004340, 10.1371/journal.pcbi.1004340
    4. D. E. Ramsay, J. Invik, S. L. Checkley, S. P. Gow, N. D. Osgood, C. L. Waldner, Application of dynamic modelling techniques to the problem of antibacterial use and resistance: a scoping review, 2018, 146, 0950-2688, 2014, 10.1017/S0950268818002091
    5. Josephine N.A. Tetteh, Franziska Matthäus, Esteban A. Hernandez-Vargas, A survey of within-host and between-hosts modelling for antibiotic resistance, 2020, 196, 03032647, 104182, 10.1016/j.biosystems.2020.104182
    6. Emily Reichert, Reza Yaesoubi, Minttu M Rönn, Thomas L Gift, Joshua A Salomon, Yonatan H Grad, Resistance-minimising strategies for introducing a novel antibiotic for gonorrhoea treatment: a mathematical modelling study, 2023, 26665247, 10.1016/S2666-5247(23)00145-3
    7. Josiah Mushanyu, Mathematical modelling of community acquired antibiotic resistant infections, 2024, 23529148, 101452, 10.1016/j.imu.2024.101452
  • Reader Comments
  • © 2012 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(4038) PDF downloads(639) Cited by(7)

Article outline

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog