Vol.130, No.1, 2022, pp.111-132, doi:10.32604/cmes.2022.016957
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ARTICLE
Near Future Perspective of ESBL-Producing Klebsiella pneumoniae Strains Using Mathematical Modeling
  • Cemile Bagkur1,*, Emrah Guler2,3, Bilgen Kaymakamzade4,5, Evren Hincal4,5, Kaya Suer6
1 Department of Medical Microbiology and Clinical Microbiology, Near East University, Nicosia, 99010, Cyprus
2 Department of Nutrition and Dietetics, Near East University, Nicosia, 99010, Cyprus
3 DESAM Institute, Near East University, Nicosia, 99010, Cyprus
4 Department of Mathematics, Near East University, Nicosia, 99010, Cyprus
5 Mathematics Research Center, Near East University, Nicosia, 99010, Cyprus
6 Department of Infectious Diseases and Clinical Microbiology, Near East University, Nicosia, 99010, Cyprus
* Corresponding Author: Cemile Bagkur. Email:
(This article belongs to this Special Issue: Mathematical Aspects of Computational Biology and Bioinformatics)
Received 14 April 2021; Accepted 12 July 2021; Issue published 29 November 2021
Abstract
While antibiotic resistance is becoming increasingly serious today, there is almost no doubt that more challenging times await us in the future. Resistant microorganisms have increased in the past decades leading to limited treatment options, along with higher morbidity and mortality. Klebsiella pneumoniae is one of the significant microorganisms causing major public health problems by acquiring resistance to antibiotics and acting as an opportunistic pathogen of healthcare-associated infections. The production of extended spectrum beta-lactamases (ESBL) is one of the resistance mechanisms of K. pneumoniae against antibiotics. In this study, the future clinical situation of ESBL-producing K. pneumoniae was investigated in order to reflect the future scenarios to understand the severity of the issue and to determine critical points to prevent the spread of the ESBL-producing strain. For evaluation purposes, SIS-type mathematical modeling was used with retrospective medical data from the period from 2016 to 2019. Stability of the disease-free equilibrium and basic reproduction ratios were calculated. Numerical simulation of the SIS model was conducted to describe the dynamics of non-ESBL and ESBL-producing K. pneumoniae. In order to determine the most impactful parameter on the basic reproduction ratio, sensitivity analysis was performed. A study on mathematical modeling using data on ESBL-producing K. pneumoniae strains has not previously been performed in any health institution in Northern Cyprus, and the efficiency of the parameters determining the spread of the relevant strains has not been investigated. Through this study, the spread of ESBL-producing K. pneumoniae in a hospital environment was evaluated using a different approach. According to the study, in approximately seventy months, ESBL-producing K. pneumoniae strains will exceed non-ESBL K. pneumoniae strains. As a result, the analyses showed that hospital admissions and people infected with non-ESBL or ESBL-producing K. pneumoniae have the highest rate of spreading the infections. In addition, it was observed that the use of antibiotics plays a major role in the spread of ESBL-producing K. pneumoniae compared to other risk factors such as in-hospital transmissions. As a matter of course, recoveries from Klebsiella infections were seen to be the most effective way of limiting the spread. It can be concluded from the results that although the use of antibiotics is one of the most effective factors in the development of the increasing ESBL-producing K. pneumoniae, regulation of antibiotic use policy could be a remedial step together with effective infection control measures. Such steps may hopefully lead to decreased morbidity and mortality rates as well as improved medical costs.
Keywords
Mathematical modeling; Klebsiella pneumoniae; extended spectrum beta lactamases (ESBL)
Cite This Article
Bagkur, C., Guler, E., Kaymakamzade, B., Hincal, E., Suer, K. (2022). Near Future Perspective of ESBL-Producing Klebsiella pneumoniae Strains Using Mathematical Modeling. CMES-Computer Modeling in Engineering & Sciences, 130(1), 111–132.
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