MATHEMATICAL MODELS FOR PREDICTING THE SPREAD OF ANTIBIOTIC-RESISTANT BACTERIA

Abstract

Antimicrobial resistance is a worldwide health issue that requires the use of all available methods to manage it. Mathematical modeling is an important tool that allows us to better understand how antimicrobial resistance (AMR) develops and spreads. It also helps us to explore and suggest new ways to manage AMR. Ensuring the wide applicability of mathematical models is crucial, and this may be achieved by adhering to appropriate modeling practices. The aim of this work was to conduct a thorough systematic evaluation of existing models that examine the development and spread of antimicrobial resistance (AMR). Moreover, the research sought to uncover deficiencies in the information necessary for the development of practical models. The review conducted an extensive literature search and included 38 carefully chosen research. The chosen articles were analyzed using a modified version of established frameworks, and their quality was assessed according to the TRACE good modeling practice recommendations. None of the chosen articles met the TRACE standards. Our suggestion for future mathematical models is to: a) combine mechanistic modeling of biological processes, b) use stochastic modeling to account for uncertainty and unpredictability in the system, c) do sensitivity analysis and validate the model externally and internally. There are several mathematical models that describe the development and spread of antimicrobial resistance (AMR). Insufficient understanding of antibiotic resistance hinders the creation of effective mathematical models.

Keywords: Mathematical models, antimicrobial resistance, antibiotic bacterial, resistance, review.