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如果您决定构建自己的预测细菌耐药性发展的数学模型,应该考虑哪些因素?基于文献系统综述的建议。

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.

作者信息

Arepeva Maria, Kolbin Alexey, Kurylev Alexey, Balykina Julia, Sidorenko Sergey

机构信息

Faculty of Applied Mathematics and Control Processes, St. Petersburg State University St. Petersburg, Russia.

Faculty of Applied Mathematics and Control Processes, St. Petersburg State University St. Petersburg, Russia ; Faculty of Medicine, First Pavlov State Medical University of St. Petersburg St. Petersburg, Russia.

出版信息

Front Microbiol. 2015 Apr 29;6:352. doi: 10.3389/fmicb.2015.00352. eCollection 2015.

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.

摘要

获得性细菌耐药是传染病导致死亡和发病的原因之一。数学建模使我们能够预测耐药性的传播,并在一定程度上控制其动态变化。本综述的目的是研究现有的数学模型,以了解当前使用方法的优缺点,并构建我们自己的模型。在分析过程中,选择了七篇符合纳入/排除标准的关于研究耐药性的数学方法的文章。所有模型均根据在抗生素存在下研究耐药性所使用的方法进行分类,并根据我们的研究进行分析。由于研究的特殊性,一些模型需要修改。模型构建的进一步工作计划如下:根据我们的研究修改一些模型,用我们的数据检验所有得到的模型,选择预测质量最佳的最优模型或多个模型。之后,我们将能够利用所得结果构建一个耐药性发展模型。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f3e9/4413671/8d79d92438e4/fmicb-06-00352-g0001.jpg

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