Projecting the Development of Antimicrobial Resistance to Beta-lactam Antibiotics in Africa: A Mathematical Modelling Approach

Background: Despite significant advances in antibiotic and anti-infective therapy development, the global threat posed by AMR is undeniable, with millions of deaths attributed to bacterial AMR in recent years. The emergence and spread of resistance genes threaten the therapeutic effectiveness of beta-lactam antibiotics, which are widely used worldwide. The World Health Organization recommends the use of alternative antimicrobial treatment as empirical treatment when ≥25% of organisms are resistant. Susceptibility to beta-lactam antibiotics, one of the commonest treatment options has been decreasing in many countries. It is impossible to completely prevent the development and spread of resistance because the ability to adapt to changing environmental conditions is a fundamental property of all living things. Mathematical modeling is a powerful tool to investigate the dynamics of AMR; however, the extent of its use to predict population-level development of resistance to beta-lactam antibiotics is currently unclear. Aim: To use antimicrobial resistance surveillance data and develop a mathematical model to project the time to reach the 25% threshold for resistance to beta-lactam antibiotics. Model development and analysis: We shall use data from Antimicrobial Testing Leadership and Surveillance) Program ATLAS in Africa from 2004–2020 about minimum inhibitory concentrations (MIC) for penicillins, cephalosporins, and carbapenems. The data will be analyzed to assess the current prevalence and trends in beta-lactam resistance. Descriptive statistics and trend analysis will be employed to identify patterns and changes over time. We shall develop two susceptible-infected-susceptible models to fit the data and produce projections of the proportion of resistance until 2035. The single-step model will represent the situation in which a single mutation results in antimicrobial resistance. In the multi-step model, the sequential accumulation of resistance mutations will be reflected by changes in the MIC distribution.