What should be considered if you decide to build your own mathematical model for predicting the development of bacterial resistance? Recommendations based on a systematic review of the literature

Abstract

Acquired bacterial resistance is one of the causes of mortality and morbidity from infectious diseases. Mathematical modeling allows us to predict the spread of resistance and to some extent to control its dynamics. The purpose of this review was to examine existing mathematical models in order to understand the pros and cons of currently used approaches and to build our own model. During the analysis, seven articles on mathematical approaches to studying resistance that satisfied the inclusion/exclusion criteria were selected. All models were classified according to the approach used to study resistance in the presence of an antibiotic and were analyzed in terms of our research. Some models require modifications due to the specifics of the research. The plan for further work on model building is as follows: modify some models, according to our research, check all obtained models against our data, and select the optimal model or models with the best quality of prediction. After that we would be able to build a model for the development of resistance using the obtained results.

Application of dynamic modelling techniques to the problem of antibacterial use and resistance: a scoping review

“…Nine articles (11%) were published before 2000, 25 articles (31%) were published between 2000 and 2009 (inclusive) and 47 (58%) articles were published between 2010 and the most recent database search in November 2016. Twenty-two of the included articles (27%) were published after 2012, the most recent year covered by a review in our supplemental search [32]. The corresponding author in nearly half of the articles (43%) was affiliated with an American institution; 12 articles (15%) and eight articles (10%) were affiliated with corresponding authors based in France and the UK, respectively.…”

“…In order to identify relevant studies missed by the database search, the team conducted a supplementary review of reference lists from pertinent articles. Included in this subset of articles were previous reviews of studies modelling bacterial resistance [30–32] and studies published between 2013 and 2016 and retained for inclusion after full-text review (see the ‘Article screening’ section). The reference lists of retained studies concerning food-producing animals [e.g.…”

“…36, 37] were likewise reviewed, given that these were comparably unique and suggestive of missed search terms. Retained studies published prior to 2013 were not reviewed further, as this time period was comprehensively covered by [30–32] and new articles were no longer being identified. As the focus of this review was peer-reviewed literature, the team chose not to include grey literature in the search.…”

Medical prescribing and antibiotic resistance: A game-theoretic analysis of a potentially catastrophic social dilemma

“…Several mathematical models of antimicrobial resistance have been published—for reviews, see [2–4]. Some previous research has even focused on game-theoretic models of the evolution of resistance at the level of competing bacteria [5], but we are unaware of any game-theoretic models of the antimicrobial prescribing behavior of medical practitioners or physicians.…”

Identification of the elements of models of antimicrobial resistance of bacteria for assessing their usefulness and usability in One Health decision making: a protocol for scoping review

“…16 17 While previous reviews have attempted to synthesise the knowledge from various models of AMR in bacteria, their focus was solely on measuring the impact of mathematical models, 18 on AMR due to antimicrobial use only, on antimicrobial resistance in general without a specific focus on bacteria, on a limited number of bacterial genera and species, and on limited types of modelling approaches and settings. [17][18][19][20][21][22][23][24] While these reviews provided important insights in composing our research questions, most of these reviews used non-reproducible and/or less sensitive search strategies, and excluded statistical models, within pathogen/host models, publications in languages other than English, and grey literatures, that is, preprints, thesis, reports and other documents that have not been published in scholarly journals. [17][18][19][20][21][22][23][24] Consequently, previous reviews excluded a wealth of knowledge on AMR models that could enhance our understanding of AMR.…”