Standard

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. / Arepeva, M.; Kolbin, A.; Kurylev, A.; Balykina, J.; Sidorenko, S.

In: Frontiers in Microbiology, Vol. 6, 2015.

Research output: Contribution to journal › Literature review

Harvard

Arepeva, M, Kolbin, A, Kurylev, A, Balykina, J & Sidorenko, S 2015, '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', Frontiers in Microbiology, vol. 6. https://doi.org/10.3389/fmicb.2015.00352

APA

Arepeva, M., Kolbin, A., Kurylev, A., Balykina, J., & Sidorenko, S. (2015). 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. Frontiers in Microbiology, 6. https://doi.org/10.3389/fmicb.2015.00352

Vancouver

Arepeva M, Kolbin A, Kurylev A, Balykina J, Sidorenko S. 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. Frontiers in Microbiology. 2015;6. https://doi.org/10.3389/fmicb.2015.00352

Author

Arepeva, M. ; Kolbin, A. ; Kurylev, A. ; Balykina, J. ; Sidorenko, S. / 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. In: Frontiers in Microbiology. 2015 ; Vol. 6.

BibTeX

@article{f4d190ee99eb46948c5bc27f84981651,
title = "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",
author = "M. Arepeva and A. Kolbin and A. Kurylev and J. Balykina and S. Sidorenko",
year = "2015",
doi = "10.3389/fmicb.2015.00352",
language = "English",
volume = "6",
journal = "Frontiers in Microbiology",
issn = "1664-302X",
publisher = "Frontiers Media S.A.",

}

RIS

TY - JOUR

T1 - 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

AU - Arepeva, M.

AU - Kolbin, A.

AU - Kurylev, A.

AU - Balykina, J.

AU - Sidorenko, S.

PY - 2015

Y1 - 2015

N2 - 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

AB - 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

U2 - 10.3389/fmicb.2015.00352

DO - 10.3389/fmicb.2015.00352

M3 - Literature review

VL - 6

JO - Frontiers in Microbiology

JF - Frontiers in Microbiology

SN - 1664-302X

ER -

ID: 3990578