Sundermann Alexander J, Chen Jieshi, Miller James K, Martin Elise M, Snyder Graham M, Van Tyne Daria, Marsh Jane W, Dubrawski Artur, Harrison Lee H
Microbial Genomic Epidemiology Laboratory, Center for Genomic Epidemiology, University of Pittsburgh, Pittsburgh, Pennsylvania.
Division of Infectious Diseases, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania.
Antimicrob Steward Healthc Epidemiol. 2022 Jun 13;2(1):e91. doi: 10.1017/ash.2021.241. eCollection 2022.
Whole-genome sequencing (WGS) has traditionally been used in infection prevention to confirm or refute the presence of an outbreak after it has occurred. Due to decreasing costs of WGS, an increasing number of institutions have been utilizing WGS-based surveillance. Additionally, machine learning or statistical modeling to supplement infection prevention practice have also been used. We systematically reviewed the use of WGS surveillance and machine learning to detect and investigate outbreaks in healthcare settings.
We performed a PubMed search using separate terms for WGS surveillance and/or machine-learning technologies for infection prevention through March 15, 2021.
Of 767 studies returned using the WGS search terms, 42 articles were included for review. Only 2 studies (4.8%) were performed in real time, and 39 (92.9%) studied only 1 pathogen. Nearly all studies (n = 41, 97.6%) found genetic relatedness between some isolates collected. Across all studies, 525 outbreaks were detected among 2,837 related isolates (average, 5.4 isolates per outbreak). Also, 35 studies (83.3%) only utilized geotemporal clustering to identify outbreak transmission routes. Of 21 studies identified using the machine-learning search terms, 4 were included for review. In each study, machine learning aided outbreak investigations by complementing methods to gather epidemiologic data and automating identification of transmission pathways.
WGS surveillance is an emerging method that can enhance outbreak detection. Machine learning has the potential to identify novel routes of pathogen transmission. Broader incorporation of WGS surveillance into infection prevention practice has the potential to transform the detection and control of healthcare outbreaks.
全基因组测序（WGS）传统上用于感染预防，以在疫情爆发后确认或排除其存在。由于WGS成本降低，越来越多的机构开始采用基于WGS的监测。此外，机器学习或统计建模也被用于辅助感染预防实践。我们系统回顾了WGS监测和机器学习在医疗机构中检测和调查疫情的应用。
我们在PubMed上进行了搜索，使用单独的术语搜索截至2021年3月15日用于感染预防的WGS监测和/或机器学习技术。
在使用WGS搜索词返回的767项研究中，纳入42篇文章进行综述。仅2项研究（4.8%）为实时研究，39项研究（92.9%）仅研究1种病原体。几乎所有研究（n = 41，97.6%）都发现了一些收集的分离株之间存在基因相关性。在所有研究中，在2837个相关分离株中检测到525起疫情（平均每起疫情5.4个分离株）。此外，35项研究（83.3%）仅利用地理时间聚类来确定疫情传播途径。在使用机器学习搜索词识别出的21项研究中，纳入4篇文章进行综述。在每项研究中，机器学习通过补充收集流行病学数据的方法和自动识别传播途径来辅助疫情调查。
WGS监测是一种新兴方法，可增强疫情检测。机器学习有潜力识别病原体传播的新途径。将WGS监测更广泛地纳入感染预防实践有可能改变医疗机构疫情的检测和控制。
