Information from the abstract
Antimicrobial resistance represents a critical global healthcare emergency, with carbapenem resistant Klebsiella pneumoniae (CRKP) standing as a leading pathogen responsible for severe clinical infections. This study develops a fractional-order susceptible–resistant–persister (SRP) mathematical model under the Caputo fractional derivative framework to characterize CRKP dynamics across three distinct bacterial subpopulations. The formulation accounts for memory-dependent fractional orders, nonlinear transmission, saturated antibiotic efficacy, plasmid-mediated resistant dynamics, and environmental bacterial influx. Analytical proofs using Mittag-Leffler functions and the Banach fixed-point theorem establish the positivity, boundedness, existence, and uniqueness of the system solutions. The effective reproduction number is derived, governing the global stability thresholds between the resistant-free and endemic steady states. Numerical simulations using the Adams–Bashforth-Moulton predictor–corrector method illustrate how variations in the fractional order alter resistant distribution and system memory. Furthermore, an optimal control framework is established using two intervention variables: infection prevention and treatment enhancement. The optimization aims to minimize the resistant CRKP population while balancing operational expenses. The optimal control analysis reveals that lower fractional-order values yield a higher averted resistant bacterial burden. Due to inherent memory characteristics, the system requires more intensive control efforts during the initial phase of the intervention, with all cost-effective strategies maintaining a low incremental cost-effectiveness ratio.
Why this record is monitored
This record has an Impact Signal of 71/100 based on recency, source, collaboration, and bibliographic signals. It prioritizes monitoring and is not a judgment of research quality.
Related topics: Mathematical and Theoretical Epidemiology and Ecology Models · Fractional Differential Equations Solutions · Mathematical Biology Tumor Growth
Thai researcher and institutional participation
Mohamed Hafez · Shinawatra University
Data limitations
This page is a bibliographic record based on abstract-level information, not a full analysis or quality assessment. Verify the DOI and original article before citation.