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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
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 u...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4413671/ https://www.ncbi.nlm.nih.gov/pubmed/25972847 http://dx.doi.org/10.3389/fmicb.2015.00352 |
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author | Arepeva, Maria Kolbin, Alexey Kurylev, Alexey Balykina, Julia Sidorenko, Sergey |
author_facet | Arepeva, Maria Kolbin, Alexey Kurylev, Alexey Balykina, Julia Sidorenko, Sergey |
author_sort | Arepeva, Maria |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-4413671 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-44136712015-05-13 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, Maria Kolbin, Alexey Kurylev, Alexey Balykina, Julia Sidorenko, Sergey Front Microbiol Microbiology 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. Frontiers Media S.A. 2015-04-29 /pmc/articles/PMC4413671/ /pubmed/25972847 http://dx.doi.org/10.3389/fmicb.2015.00352 Text en Copyright © 2015 Arepeva, Kolbin, Kurylev, Balykina and Sidorenko. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Microbiology Arepeva, Maria Kolbin, Alexey Kurylev, Alexey Balykina, Julia Sidorenko, Sergey 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 |
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 |
title_full | 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 |
title_fullStr | 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 |
title_full_unstemmed | 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 |
title_short | 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 |
title_sort | 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 |
topic | Microbiology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4413671/ https://www.ncbi.nlm.nih.gov/pubmed/25972847 http://dx.doi.org/10.3389/fmicb.2015.00352 |
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