使用时间序列分析预测细菌病原体的抗微生物耐药性
Jeonghoon Kim1, Ruwini Rupasinghe2, Avishai Halev1
1Department of Mathematics, University of California, Davis, Davis, CA, United States.
Frontiers in microbiology
|May 30, 2023
概括
机器学习可以准确预测食品动物的抗菌素耐药性 (AMR). 这种方法有助于AMR监测,为细菌病原体的传统方法提供了更快,更具成本效益的替代方案.
科学领域:
- 兽医医学 兽医医学 兽医医学
- 微生物学 微生物学
- 数据科学数据科学数据科学
背景情况:
- 抗菌素耐药性 (AMR) 构成了全球健康和经济的重大威胁.
- 在食品动物生产中有效的AMR监测至关重要,但由于昂贵和耗时的检测方法 (如最小抑制度 (MIC) 测试) 而受到挑战.
研究的目的:
- 开发和评估机器学习模型,用于预测细菌病原体中未来AMR负担.
- 为食品动物生产中的常规AMR监测提供更有效,更准确的工具.
主要方法:
- 从600多个美国农场 (2010-2021) 收集了病原体和抗微生物数据,以创建AMR时间序列数据.
- 应用机器学习,特别是季节性自动回归集成移动平均值 (SARIMA),以预测AMR趋势.
- 与五个基线模型 (包括ARMA和ARIMA) 进行SARIMA性能比较.
主要成果:
- 与基线模型相比,SARIMA模型在预测AMR趋势方面表现优越.
- 这项研究成功生成了对五种主要细菌病原体的预测性AMR时间序列数据:大肠杆菌,Streptococcus suis,沙门氏菌,Pasteurella multocida和Bordetella bronchiseptica.
结论:
- 机器学习,特别是SARIMA,为预测食品动物病原体中的AMR负担提供了一个强大的工具.
- 这种预测能力可以增强AMR监测策略,潜在地降低成本和改善响应时间.
- 该方法可以扩展到预测除了研究的细菌病原体之外的其他细菌病原体的AMR.
更多相关视频
06:54Author Spotlight: Understanding and Detecting Environmental Antimicrobial Resistance by Combining Culture-Based Techniques and Genomics
Published on: July 19, 2024
1.1K
08:30One-day Workflow Scheme for Bacterial Pathogen Detection and Antimicrobial Resistance Testing from Blood Cultures
Published on: July 9, 2012
25.6K
相关概念视频
Antimicrobial Effectiveness
98
The effectiveness of antimicrobial agents depends on various factors influencing their ability to eliminate microbial populations. Larger microbial populations require more time for complete eradication, emphasizing the importance of population size analysis when evaluating antimicrobial efficacy.Microbial resistance to antimicrobial agents varies significantly. Highly resilient microorganisms include endospores, gram-negative bacteria, and non-enveloped viruses, while prions are exceptionally...
98
Development of Antibiotic Resistance
50
Antibiotic resistance is a major public health concern that arises when bacteria evolve mechanisms to withstand the effects of antibiotic treatments. This resistance can be intrinsic, acquired through genetic mutations, or transferred between bacteria via horizontal gene transfer. The development of antibiotic resistance poses significant challenges in treating bacterial infections and necessitates ongoing research to develop new therapeutic strategies.Intrinsic resistance occurs when bacterial...
50
Antibiotic Selection
54.8K
Overview
54.8K
Steps in Outbreak Investigation
155
In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
155
Defense Against Bacterial Pathogens
1.5K
The human immune system is a complex network of cells, tissues, and organs that work together to defend the body against bacterial infections. It consists of various immune cells, each playing a specific role in the defense mechanism.
Phagocytes
Phagocytes are the frontline soldiers of the immune system. They include neutrophils and macrophages. Neutrophils are the most abundant type of white blood cell and are quickly mobilized to the site of infection. Macrophages are larger cells that patrol...
Phagocytes
Phagocytes are the frontline soldiers of the immune system. They include neutrophils and macrophages. Neutrophils are the most abundant type of white blood cell and are quickly mobilized to the site of infection. Macrophages are larger cells that patrol...
1.5K
