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Monitoring and Early Warning Analysis of the Epidemic Situation of Escherichia coli Based on Big Data Technology and Cloud Computing

The purpose of this study is to analyze the molecular epidemiological characteristics and resistance mechanisms of Escherichia coli. The study established a big data cloud computing prediction model for the epidemic mechanism of the pathogen. The study establishes the early warning, control paramete...

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Detalles Bibliográficos
Autores principales: Yan, Meishu, Yan, Meizi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8849918/
https://www.ncbi.nlm.nih.gov/pubmed/35186242
http://dx.doi.org/10.1155/2022/8739447
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author Yan, Meishu
Yan, Meizi
author_facet Yan, Meishu
Yan, Meizi
author_sort Yan, Meishu
collection PubMed
description The purpose of this study is to analyze the molecular epidemiological characteristics and resistance mechanisms of Escherichia coli. The study established a big data cloud computing prediction model for the epidemic mechanism of the pathogen. The study establishes the early warning, control parameters, and mathematical model of Escherichia coli infectious disease and monitors the molecular sequence of the pathogen based on discrete indicators. A nonlinear mathematical model equation was used to establish the epidemic trend model of Escherichia coli. The study shows that the use of the model can control the relative error at about 5%. The experiment proves the effectiveness of the combined model.
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spelling pubmed-88499182022-02-17 Monitoring and Early Warning Analysis of the Epidemic Situation of Escherichia coli Based on Big Data Technology and Cloud Computing Yan, Meishu Yan, Meizi J Healthc Eng Research Article The purpose of this study is to analyze the molecular epidemiological characteristics and resistance mechanisms of Escherichia coli. The study established a big data cloud computing prediction model for the epidemic mechanism of the pathogen. The study establishes the early warning, control parameters, and mathematical model of Escherichia coli infectious disease and monitors the molecular sequence of the pathogen based on discrete indicators. A nonlinear mathematical model equation was used to establish the epidemic trend model of Escherichia coli. The study shows that the use of the model can control the relative error at about 5%. The experiment proves the effectiveness of the combined model. Hindawi 2022-02-09 /pmc/articles/PMC8849918/ /pubmed/35186242 http://dx.doi.org/10.1155/2022/8739447 Text en Copyright © 2022 Meishu Yan and Meizi Yan. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Yan, Meishu
Yan, Meizi
Monitoring and Early Warning Analysis of the Epidemic Situation of Escherichia coli Based on Big Data Technology and Cloud Computing
title Monitoring and Early Warning Analysis of the Epidemic Situation of Escherichia coli Based on Big Data Technology and Cloud Computing
title_full Monitoring and Early Warning Analysis of the Epidemic Situation of Escherichia coli Based on Big Data Technology and Cloud Computing
title_fullStr Monitoring and Early Warning Analysis of the Epidemic Situation of Escherichia coli Based on Big Data Technology and Cloud Computing
title_full_unstemmed Monitoring and Early Warning Analysis of the Epidemic Situation of Escherichia coli Based on Big Data Technology and Cloud Computing
title_short Monitoring and Early Warning Analysis of the Epidemic Situation of Escherichia coli Based on Big Data Technology and Cloud Computing
title_sort monitoring and early warning analysis of the epidemic situation of escherichia coli based on big data technology and cloud computing
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8849918/
https://www.ncbi.nlm.nih.gov/pubmed/35186242
http://dx.doi.org/10.1155/2022/8739447
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