Superspreaders drive the largest outbreaks of hospital onset COVID-19 infections.
Superspreaders drive the largest outbreaks of hospital onset COVID-19 infections.
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DOI：
10.7554/elife.67308
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发表时间：
2021-08-24
期刊：
影响因子：
7.7
通讯作者：
Török ME
中科院分区：
文献类型：
--
作者：
Illingworth CJ;Hamilton WL;Warne B;Routledge M;Popay A;Jackson C;Fieldman T;Meredith LW;Houldcroft CJ;Hosmillo M;Jahun AS;Caller LG;Caddy SL;Yakovleva A;Hall G;Khokhar FA;Feltwell T;Pinckert ML;Georgana I;Chaudhry Y;Curran MD;Parmar S;Sparkes D;Rivett L;Jones NK;Sridhar S;Forrest S;Dymond T;Grainger K;Workman C;Ferris M;Gkrania-Klotsas E;Brown NM;Weekes MP;Baker S;Peacock SJ;Goodfellow IG;Gouliouris T;de Angelis D;Török ME
SARS-CoV-2以其快速传播和传播模式的异质性而闻名，多次发表了超级传播行为的发生率。在这里，我们应用了一种新的网络重建算法来推断在英国剑桥大学医院NHS基金会信托基金会第一波疫情期间确定的最大COVID-19感染集群中患者和医护人员（HCW）之间发生的病毒传播模式。根据个人报告症状的日期，记录的个人位置和病毒基因组序列数据，我们显示了个人之间的传播模式不均匀，患者更有可能被其他患者感染，而不是HCW。此外，这些数据与超级传播模式一致，即21%的个体导致了80%的传播事件。我们的研究提供了一个详细的回顾性分析医院SARS-CoV-2传播，并揭示了需要密集和普遍的感染控制程序。由SARS-CoV-2病毒引起的COVID-19大流行对全球公共卫生构成挑战。医院一直处于这场战斗的最前沿，在几波感染中治疗了大量患病患者。找到控制病毒在医院传播的方法是保护弱势患者和工作人员的关键，同时保持医院的运转，但要产生有效的感染控制，研究人员必须了解SARS-CoV-2是如何传播的。一系列因素使得研究SARS-CoV-2在医院的传播变得棘手。例如，有些人没有出现任何症状，而在那些出现症状的人中，很难确定他们是在医院还是在其他地方感染了病毒。然而，比较来自医院不同人的SARS-CoV-2病毒的遗传信息可以让科学家了解它是如何传播的。SARS-CoV-2的遗传物质样本可以通过擦拭受感染的个体获得。如果两个样本的基因序列非常不同，那么提供样本的人不太可能将病毒传播给另一个人。伊林沃思、汉密尔顿等人利用这些信息，沿着其他关于SARS-CoV-2如何传播的数据，开发了一种算法，可以确定病毒如何在不同医院病房的人与人之间传播。为了构建他们的算法，伊林沃思、汉密尔顿等人从医院的病人和工作人员那里收集了SARS-CoV-2基因数据，并将其与SARS-CoV-2如何传播以及这些人如何在医院移动的信息结合起来。该算法显示，在大多数情况下，患者是被其他患者感染的（22例中有20例），而工作人员是被患者和工作人员感染的。通过进一步研究这些数据，Illingworth，汉密尔顿等人揭示了80%的医院获得性感染是由研究中仅21%的个体引起的，确定了“超级传播者”模式。这些发现可能有助于为SARS-CoV-2感染控制措施提供信息，以减少医院内的传播，并可能用于改善其他情况下的感染控制。
SARS-CoV-2 is notable both for its rapid spread, and for the heterogeneity of its patterns of transmission, with multiple published incidences of superspreading behaviour. Here, we applied a novel network reconstruction algorithm to infer patterns of viral transmission occurring between patients and health care workers (HCWs) in the largest clusters of COVID-19 infection identified during the first wave of the epidemic at Cambridge University Hospitals NHS Foundation Trust, UK. Based upon dates of individuals reporting symptoms, recorded individual locations, and viral genome sequence data, we show an uneven pattern of transmission between individuals, with patients being much more likely to be infected by other patients than by HCWs. Further, the data were consistent with a pattern of superspreading, whereby 21% of individuals caused 80% of transmission events. Our study provides a detailed retrospective analysis of nosocomial SARS-CoV-2 transmission, and sheds light on the need for intensive and pervasive infection control procedures. The COVID-19 pandemic, caused by the SARS-CoV-2 virus, presents a global public health challenge. Hospitals have been at the forefront of this battle, treating large numbers of sick patients over several waves of infection. Finding ways to manage the spread of the virus in hospitals is key to protecting vulnerable patients and workers, while keeping hospitals running, but to generate effective infection control, researchers must understand how SARS-CoV-2 spreads. A range of factors make studying the transmission of SARS-CoV-2 in hospitals tricky. For instance, some people do not present any symptoms, and, amongst those who do, it can be difficult to determine whether they caught the virus in the hospital or somewhere else. However, comparing the genetic information of the SARS-CoV-2 virus from different people in a hospital could allow scientists to understand how it spreads. Samples of the genetic material of SARS-CoV-2 can be obtained by swabbing infected individuals. If the genetic sequences of two samples are very different, it is unlikely that the individuals who provided the samples transmitted the virus to one another. Illingworth, Hamilton et al. used this information, along with other data about how SARS-CoV-2 is transmitted, to develop an algorithm that can determine how the virus spreads from person to person in different hospital wards. To build their algorithm, Illingworth, Hamilton et al. collected SARS-CoV-2 genetic data from patients and staff in a hospital, and combined it with information about how SARS-CoV-2 spreads and how these people moved in the hospital . The algorithm showed that, for the most part, patients were infected by other patients (20 out of 22 cases), while staff were infected equally by patients and staff. By further probing these data, Illingworth, Hamilton et al. revealed that 80% of hospital-acquired infections were caused by a group of just 21% of individuals in the study, identifying a ‘superspreader’ pattern. These findings may help to inform SARS-CoV-2 infection control measures to reduce spread within hospitals, and could potentially be used to improve infection control in other contexts.