Literature DB >> 21467930

Contribution of mathematical modeling to the fight against bacterial antibiotic resistance.

Lulla Opatowski1, Didier Guillemot, Pierre-Yves Boëlle, Laura Temime.   

Abstract

PURPOSE OF REVIEW: Modeling of antibiotic resistance in pathogenic bacteria responsible for human disease has developed considerably over the last decade. Herein, we summarize the main published studies to illustrate the contribution of models for understanding both within-host and population-based phenomena. We then suggest possible topics for future studies. RECENT
FINDINGS: Model building of bacterial resistance has involved epidemiologists, biologists and modelers with two different objectives. First, modeling has helped largely in identifying and understanding the factors and biological phenomena responsible for the emergence and spread of resistant strains. Second, these models have become important decision support tools for medicine and public health.
SUMMARY: Major improvements of models in the coming years should take into account specific pathogen characteristics (resistance mechanisms, multiple colonization phenomena, cooperation and competition among species) and better description of the contacts associated with transmission risk within populations.

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Year:  2011        PMID: 21467930     DOI: 10.1097/QCO.0b013e3283462362

Source DB:  PubMed          Journal:  Curr Opin Infect Dis        ISSN: 0951-7375            Impact factor:   4.915


  31 in total

1.  Antibiotic reduction campaigns do not necessarily decrease bacterial resistance: the example of methicillin-resistant Staphylococcus aureus.

Authors:  Lidia Kardas-Sloma; Pierre-Yves Boëlle; Lulla Opatowski; Didier Guillemot; Laura Temime
Journal:  Antimicrob Agents Chemother       Date:  2013-07-01       Impact factor: 5.191

2.  Modeling the role of altruism of antibiotic-resistant bacteria.

Authors:  Wendi Wang; Xingfu Zou
Journal:  J Math Biol       Date:  2013-03-30       Impact factor: 2.259

3.  Addressing the Unknowns of Antimicrobial Resistance: Quantifying and Mapping the Drivers of Burden.

Authors:  Gwenan M Knight; Ceire Costelloe; Kris A Murray; Julie V Robotham; Rifat Atun; Alison H Holmes
Journal:  Clin Infect Dis       Date:  2018-02-01       Impact factor: 9.079

4.  Vaccination can drive an increase in frequencies of antibiotic resistance among nonvaccine serotypes of Streptococcus pneumoniae.

Authors:  Uri Obolski; José Lourenço; Craig Thompson; Robin Thompson; Andrea Gori; Sunetra Gupta
Journal:  Proc Natl Acad Sci U S A       Date:  2018-03-06       Impact factor: 11.205

5.  Modeling the Emergence of Antibiotic Resistance in the Environment: an Analytical Solution for the Minimum Selection Concentration.

Authors:  Ben K Greenfield; Shanna Shaked; Carl F Marrs; Patrick Nelson; Ian Raxter; Chuanwu Xi; Thomas E McKone; Olivier Jolliet
Journal:  Antimicrob Agents Chemother       Date:  2018-02-23       Impact factor: 5.191

6.  Pharmacokinetic-pharmacodynamic model to evaluate intramuscular tetracycline treatment protocols to prevent antimicrobial resistance in pigs.

Authors:  Amais Ahmad; Kaare Græsbøll; Lasse Engbo Christiansen; Nils Toft; Louise Matthews; Søren Saxmose Nielsen
Journal:  Antimicrob Agents Chemother       Date:  2014-12-29       Impact factor: 5.191

7.  Microbiome-pathogen interactions drive epidemiological dynamics of antibiotic resistance: A modeling study applied to nosocomial pathogen control.

Authors:  Laura Temime; Lulla Opatowski; David Rm Smith
Journal:  Elife       Date:  2021-09-14       Impact factor: 8.140

8.  Mathematical modelling to study the horizontal transfer of antimicrobial resistance genes in bacteria: current state of the field and recommendations.

Authors:  Quentin J Leclerc; Jodi A Lindsay; Gwenan M Knight
Journal:  J R Soc Interface       Date:  2019-08-14       Impact factor: 4.118

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

Authors:  D E Ramsay; J Invik; S L Checkley; S P Gow; N D Osgood; C L Waldner
Journal:  Epidemiol Infect       Date:  2018-07-31       Impact factor: 4.434

Review 10.  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.

Authors:  Maria Arepeva; Alexey Kolbin; Alexey Kurylev; Julia Balykina; Sergey Sidorenko
Journal:  Front Microbiol       Date:  2015-04-29       Impact factor: 5.640

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