Mathematical modelling for antibiotic resistance control policy: do we know enough?

Abstract

BackgroundAntibiotics remain the cornerstone of modern medicine. Yet there exists an inherent dilemma in their use: we are able to prevent harm by administering antibiotic treatment as necessary to both humans and animals, but we must be mindful of limiting the spread of resistance and safeguarding the efficacy of antibiotics for current and future generations. Policies that strike the right balance must be informed by a transparent rationale that relies on a robust evidence base.Main textOne way to generate the evidence base needed to inform policies for managing antibiotic resistance is by using mathematical models. These models can distil the key drivers of the dynamics of resistance transmission from complex infection and evolutionary processes, as well as predict likely responses to policy change in silico. Here, we ask whether we know enough about antibiotic resistance for mathematical modelling to robustly and effectively inform policy. We consider in turn the challenges associated with capturing antibiotic resistance evolution using mathematical models, and with translating mathematical modelling evidence into policy.ConclusionsWe suggest that in spite of promising advances, we lack a complete understanding of key principles. From this we advocate for priority areas of future empirical and theoretical research.

Effectiveness of Antibiotics: Anti-Bacterial Activity or Microbial Drug Resistance?

“…Besides treatment/ chemotherapeutic purpose, the unreasonable use of antibiotics in agriculture should be minimized since the over dosage of antibiotics have been correlated with the dominance of an array of pathogenic drug-resistant microorganisms 18 . A recent interesting aspect on controlling the antibiotic exposure with a motive to minimize the drug-resistance phenomenon has been seen through the suggestion of generating the evidence based policies by using mathematical models which can play as the key drivers of the drug-resistance transmission dynamics in a complicated infection 27 . Such modelling also focuses on the evolutionary processes of the microbial drug resistance.…”

“…Such modelling also focuses on the evolutionary processes of the microbial drug resistance. Of course the challenges associated with measuring the antibiotic resistance evolution using such mathematical models needs to be further chalked out, together with translating the mathematical modelling evidence into policy 27 .…”

“…Indeed the presence of an antibiotic is thought to be a selection marker for a high frequency of microorganisms resistant to that specific antibiotic since resistance to any antibiotic treatment actually imparts the microbial strains a benefit compared to the antibiotic susceptible strains 27,29 . This leads to a bit strange situation that the drug-resistant bacteria may possess lower survival capacity within the host in absence of antibiotics 27,30 . Lack of understanding of such selection pressure for the antibiotic resistance is a major challenge to resolve the problem of the microbial drug resistance.…”

Leveraging Computational Modeling to Understand Infectious Diseases

“…1 ). Mathematical modeling allows for the integration of a mechanistic understanding of biological processes into precise and logical structures [ 7 ]. A correctly specified mathematical model reproduces observed empirical patterns and enables predictions of the impact of changing conditions on real-world outcomes [ 7 ].…”

“…Mathematical modeling allows for the integration of a mechanistic understanding of biological processes into precise and logical structures [ 7 ]. A correctly specified mathematical model reproduces observed empirical patterns and enables predictions of the impact of changing conditions on real-world outcomes [ 7 ]. Many techniques are used to model infectious diseases, including multiscale models [ 8 ], stochastic modeling [ 9 ], game theory [ 10 ], continuous single- or multi-variable models [ 11 ], and, more recently, machine learning and artificial intelligence [ 12 ].…”