Antimicrobial Resistance Risk Assessment Models and Database System for Animal-Derived Pathogens

(1) Background: The high use of antibiotics has made the issue of antimicrobial resistance (AMR) increasingly serious, which poses a substantial threat to the health of animals and humans. However, there remains a certain gap in the AMR system and risk assessment models between China and the advance...

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Main Authors: Xinxing Li, Buwen Liang, Ding Xu, Congming Wu, Jianping Li, Yongjun Zheng
Format: Article
Language:English
Published: MDPI AG 2020-11-01
Series:Antibiotics
Subjects:
DRI
Online Access:https://www.mdpi.com/2079-6382/9/11/829
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spelling doaj-4a7b1a1f0d3e48e7a1fa6e901023e4d72020-11-25T04:05:11ZengMDPI AGAntibiotics2079-63822020-11-01982982910.3390/antibiotics9110829Antimicrobial Resistance Risk Assessment Models and Database System for Animal-Derived PathogensXinxing Li0Buwen Liang1Ding Xu2Congming Wu3Jianping Li4Yongjun Zheng5Beijing Advanced Innovation Center for Food Nutrition and Human Health, College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, ChinaBeijing Advanced Innovation Center for Food Nutrition and Human Health, College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, ChinaBeijing Advanced Innovation Center for Food Nutrition and Human Health, College of Engineering, China Agricultural University, Beijing 100083, ChinaCollege of Veterinary Medicine, China Agricultural University, Beijing 100083, ChinaBeijing Advanced Innovation Center for Food Nutrition and Human Health, College of Engineering, China Agricultural University, Beijing 100083, ChinaBeijing Advanced Innovation Center for Food Nutrition and Human Health, College of Engineering, China Agricultural University, Beijing 100083, China(1) Background: The high use of antibiotics has made the issue of antimicrobial resistance (AMR) increasingly serious, which poses a substantial threat to the health of animals and humans. However, there remains a certain gap in the AMR system and risk assessment models between China and the advanced world level. Therefore, this paper aims to provide advanced means for the monitoring of antibiotic use and AMR data, and take piglets as an example to evaluate the risk and highlight the seriousness of AMR in China. (2) Methods: Based on the principal component analysis method, a drug resistance index model of anti-<i>E. coli</i> drugs was established to evaluate the antibiotic risk status in China. Additionally, based on the second-order Monte Carlo methods, a disease risk assessment model for piglets was established to predict the probability of <i>E. coli</i> disease within 30 days of taking florfenicol. Finally, a browser/server architecture-based visualization database system for animal-derived pathogens was developed. (3) Results: The risk of <i>E. coli</i> in the main area was assessed and Hohhot was the highest risk area in China. Compared with the true disease risk probability of 4.1%, the result of the disease risk assessment model is 7.174%, and the absolute error was 3.074%. Conclusions: Taking <i>E. coli</i> as an example, this paper provides an innovative method for rapid and accurate risk assessment of drug resistance. Additionally, the established system and assessment models have potential value for the monitoring and evaluating AMR, highlight the seriousness of antimicrobial resistance, advocate the prudent use of antibiotics, and ensure the safety of animal-derived foods and human health.https://www.mdpi.com/2079-6382/9/11/829drug resistancemicrobialdatabase systemrisk assessmentDRIsecond-order Monte Carlo method
collection DOAJ
language English
format Article
sources DOAJ
author Xinxing Li
Buwen Liang
Ding Xu
Congming Wu
Jianping Li
Yongjun Zheng
spellingShingle Xinxing Li
Buwen Liang
Ding Xu
Congming Wu
Jianping Li
Yongjun Zheng
Antimicrobial Resistance Risk Assessment Models and Database System for Animal-Derived Pathogens
Antibiotics
drug resistance
microbial
database system
risk assessment
DRI
second-order Monte Carlo method
author_facet Xinxing Li
Buwen Liang
Ding Xu
Congming Wu
Jianping Li
Yongjun Zheng
author_sort Xinxing Li
title Antimicrobial Resistance Risk Assessment Models and Database System for Animal-Derived Pathogens
title_short Antimicrobial Resistance Risk Assessment Models and Database System for Animal-Derived Pathogens
title_full Antimicrobial Resistance Risk Assessment Models and Database System for Animal-Derived Pathogens
title_fullStr Antimicrobial Resistance Risk Assessment Models and Database System for Animal-Derived Pathogens
title_full_unstemmed Antimicrobial Resistance Risk Assessment Models and Database System for Animal-Derived Pathogens
title_sort antimicrobial resistance risk assessment models and database system for animal-derived pathogens
publisher MDPI AG
series Antibiotics
issn 2079-6382
publishDate 2020-11-01
description (1) Background: The high use of antibiotics has made the issue of antimicrobial resistance (AMR) increasingly serious, which poses a substantial threat to the health of animals and humans. However, there remains a certain gap in the AMR system and risk assessment models between China and the advanced world level. Therefore, this paper aims to provide advanced means for the monitoring of antibiotic use and AMR data, and take piglets as an example to evaluate the risk and highlight the seriousness of AMR in China. (2) Methods: Based on the principal component analysis method, a drug resistance index model of anti-<i>E. coli</i> drugs was established to evaluate the antibiotic risk status in China. Additionally, based on the second-order Monte Carlo methods, a disease risk assessment model for piglets was established to predict the probability of <i>E. coli</i> disease within 30 days of taking florfenicol. Finally, a browser/server architecture-based visualization database system for animal-derived pathogens was developed. (3) Results: The risk of <i>E. coli</i> in the main area was assessed and Hohhot was the highest risk area in China. Compared with the true disease risk probability of 4.1%, the result of the disease risk assessment model is 7.174%, and the absolute error was 3.074%. Conclusions: Taking <i>E. coli</i> as an example, this paper provides an innovative method for rapid and accurate risk assessment of drug resistance. Additionally, the established system and assessment models have potential value for the monitoring and evaluating AMR, highlight the seriousness of antimicrobial resistance, advocate the prudent use of antibiotics, and ensure the safety of animal-derived foods and human health.
topic drug resistance
microbial
database system
risk assessment
DRI
second-order Monte Carlo method
url https://www.mdpi.com/2079-6382/9/11/829
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