IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v515y2019icp310-323.html
   My bibliography  Save this article

Population dynamics models based on the transmission mechanism of MCR-1

Author

Listed:
  • Qu, Leilei
  • Gao, Xubin
  • Kang, Baolin
  • He, Mingfeng
  • Pan, Qiuhui

Abstract

Antimicrobial resistance is now considered as one of the most serious global threats to human health in the 21st century. Recently, the discovery of a plasmid-borne colistin resistance gene, mcr-1, in China heralds the emergence of truly pan-drug-resistant bacteria. The gene has been found primarily in Escherichia coli but has also been identified in other members of the Enterobacteriaceae in animal, food, human, and environmental samples on every continent. Until now, the articles published have appeared to fairly use and interpret experimental studies. The aim of our work is to develop mathematic models that quantitatively describe the dynamic of the population of domestic animals and humans. We propose two ordinary differential equation models for the transmission dynamic of mcr-1 gene. The models are analyzed using stability theory of differential equations. Positive equilibrium points of the system are investigated and their stability analyses are carried out. Moreover, the numerical simulations of the proposed model are also represented Our results show that the fraction of animals used for human consumption is the most effective parameters of controlling the spread of mcr-1 gene in comparison with other parameters.

Suggested Citation

  • Qu, Leilei & Gao, Xubin & Kang, Baolin & He, Mingfeng & Pan, Qiuhui, 2019. "Population dynamics models based on the transmission mechanism of MCR-1," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 515(C), pages 310-323.
    • Handle: RePEc:eee:phsmap:v:515:y:2019:i:c:p:310-323
    DOI: 10.1016/j.physa.2018.09.141
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437118312780
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
    File URL: https://libkey.io/10.1016/j.physa.2018.09.141?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Zhang, Jiancheng & Sun, Jitao, 2014. "Stability analysis of an SIS epidemic model with feedback mechanism on networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 394(C), pages 24-32.
    2. Ahmed, E. & Agiza, H.N., 1998. "On modeling epidemics Including latency, incubation and variable susceptibility," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 253(1), pages 347-352.
    3. Petter Holme, 2013. "Extinction Times of Epidemic Outbreaks in Networks," PLOS ONE, Public Library of Science, vol. 8(12), pages 1-7, December.
    4. Christel Kamp & Mathieu Moslonka-Lefebvre & Samuel Alizon, 2013. "Epidemic Spread on Weighted Networks," PLOS Computational Biology, Public Library of Science, vol. 9(12), pages 1-10, December.
    5. James T. Murphy & Ray Walshe & Marc Devocelle, 2009. "Modeling The Population Dynamics Of Antibiotic-Resistant Bacteria: An Agent-Based Approach," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 20(03), pages 435-457.
    6. Sean Barnes & Bruce Golden & Edward Wasil, 2010. "MRSA Transmission Reduction Using Agent-Based Modeling and Simulation," INFORMS Journal on Computing, INFORMS, vol. 22(4), pages 635-646, November.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Wei, Xiaodan & Zhao, Xu & Zhou, Wenshu, 2022. "Global stability of a network-based SIS epidemic model with a saturated treatment function," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 597(C).
    2. Sean L. Barnes & Miranda Myers & Clare Rock & Daniel J. Morgan & Lisa Pineles & Kerri A. Thom & Anthony D. Harris, 2020. "Evaluating a Prediction-Driven Targeting Strategy for Reducing the Transmission of Multidrug-Resistant Organisms," INFORMS Journal on Computing, INFORMS, vol. 32(4), pages 912-929, October.
    3. Dongya Liu & Xinqi Zheng & Lei Zhang, 2021. "Simulation of Spatiotemporal Relationship between COVID-19 Propagation and Regional Economic Development in China," Land, MDPI, vol. 10(6), pages 1-15, June.
    4. Matt J Keeling & Thomas House & Alison J Cooper & Lorenzo Pellis, 2016. "Systematic Approximations to Susceptible-Infectious-Susceptible Dynamics on Networks," PLOS Computational Biology, Public Library of Science, vol. 12(12), pages 1-18, December.
    5. Md Sayeed Anwar & Dibakar Ghosh & Nikita Frolov, 2021. "Relay Synchronization in a Weighted Triplex Network," Mathematics, MDPI, vol. 9(17), pages 1-10, September.
    6. Ramos, A.B.M. & Schimit, P.H.T., 2019. "Disease spreading on populations structured by groups," Applied Mathematics and Computation, Elsevier, vol. 353(C), pages 265-273.
    7. Wang, Jinling & Jiang, Haijun & Hu, Cheng & Yu, Zhiyong & Li, Jiarong, 2021. "Stability and Hopf bifurcation analysis of multi-lingual rumor spreading model with nonlinear inhibition mechanism," Chaos, Solitons & Fractals, Elsevier, vol. 153(P2).
    8. Liu, Qiming & Li, Hua, 2019. "Global dynamics analysis of an SEIR epidemic model with discrete delay on complex network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 524(C), pages 289-296.
    9. Anwar, Md Sayeed & Kundu, Srilena & Ghosh, Dibakar, 2021. "Enhancing synchrony in asymmetrically weighted multiplex networks," Chaos, Solitons & Fractals, Elsevier, vol. 142(C).
    10. Liu, Lijun & Wei, Xiaodan & Zhang, Naimin, 2019. "Global stability of a network-based SIRS epidemic model with nonmonotone incidence rate," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 515(C), pages 587-599.
    11. Meliksah Turker & Haluk O. Bingol, 2023. "Multi-layer network approach in modeling epidemics in an urban town," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 96(2), pages 1-13, February.
    12. Zhan, Xiu-Xiu & Liu, Chuang & Zhang, Zi-Ke & Sun, Gui-Quan, 2016. "Roles of edge weights on epidemic spreading dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 456(C), pages 228-234.
    13. Wei, Xiaodan & Xu, Gaochao & Zhou, Wenshu, 2018. "Global stability of endemic equilibrium for a SIQRS epidemic model on complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 512(C), pages 203-214.
    14. Jing, Wenjun & Li, Yi & Zhang, Xiaoqin & Zhang, Juping & Jin, Zhen, 2022. "A rumor spreading pairwise model on weighted networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 585(C).
    15. Mehdi Najafi & Marek Laskowski & Pieter T. de Boer & Evelyn Williams & Ayman Chit & Seyed M. Moghadas, 2017. "The Effect of Individual Movements and Interventions on the Spread of Influenza in Long-Term Care Facilities," Medical Decision Making, , vol. 37(8), pages 871-881, November.
    16. Pan, Qiuhui & Liu, Rui & He, Mingfeng, 2014. "An epidemic model based on individuals with movement characteristics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 399(C), pages 157-162.
    17. Emanuele Borgonovo & Marco Pangallo & Jan Rivkin & Leonardo Rizzo & Nicolaj Siggelkow, 2022. "Sensitivity analysis of agent-based models: a new protocol," Computational and Mathematical Organization Theory, Springer, vol. 28(1), pages 52-94, March.
    18. Huo, Jingjing & Zhao, Hongyong, 2016. "Dynamical analysis of a fractional SIR model with birth and death on heterogeneous complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 448(C), pages 41-56.
    19. Zhu, Linhe & Liu, Wenshan & Zhang, Zhengdi, 2020. "Delay differential equations modeling of rumor propagation in both homogeneous and heterogeneous networks with a forced silence function," Applied Mathematics and Computation, Elsevier, vol. 370(C).
    20. Ullah, Farman & Lee, Sungchang, 2017. "Identification of influential nodes based on temporal-aware modeling of multi-hop neighbor interactions for influence spread maximization," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 486(C), pages 968-985.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:phsmap:v:515:y:2019:i:c:p:310-323. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.