Superspreaders drive the largest outbreaks of hospital onset COVID-19 infections.

Publisher: eLife Sciences Publications, Ltd Country of Publication: England NLM ID: 101579614 Publication Model: Electronic Cited Medium: Internet ISSN: 2050-084X (Electronic) Linking ISSN: 2050084X NLM ISO Abbreviation: Elife

Original Publication: Cambridge, UK : eLife Sciences Publications, Ltd., 2012-

COVID-19*/transmission ; COVID-19*/epidemiology ; COVID-19*/virology ; SARS-CoV-2*/genetics ; Cross Infection*/epidemiology ; Cross Infection*/transmission ; Cross Infection*/virology ; Disease Outbreaks*; Humans ; Retrospective Studies ; United Kingdom/epidemiology ; Health Personnel/statistics & numerical data

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.
Competing Interests: CI, WH, BW, MR, AP, CJ, TF, LM, CH, MH, AJ, LC, SC, AY, GH, FK, TF, MP, IG, YC, MC, SP, DS, LR, NJ, SS, SF, TD, KG, CW, MF, EG, NB, MW, SB, SP, IG, TG, Dd, MT No competing interests declared
(© 2021, Illingworth et al.)

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G0701652 United Kingdom MRC_ Medical Research Council; 204870/Z/16/Z United Kingdom WT_ Wellcome Trust; 097997/Z/11/Z United Kingdom WT_ Wellcome Trust; MC_UU_12014/12 United Kingdom MRC_ Medical Research Council; MC_PC_19026 United Kingdom MRC_ Medical Research Council; MC_UU_00002/11 United Kingdom MRC_ Medical Research Council; SFB 1310 Deutsche Forschungsgemeinschaft; MR/N029399/1 United Kingdom MRC_ Medical Research Council; 215515/Z/19/Z United Kingdom WT_ Wellcome Trust; United Kingdom WT_ Wellcome Trust; 108070/Z/15/Z United Kingdom WT_ Wellcome Trust

Keywords: SARS-CoV-2; evolutionary biology; hospital; infectious disease; microbiology; nosocomial transmission; superspreader; virus
Local Abstract: [plain-language-summary] 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.

Date Created: 20210824 Date Completed: 20240725 Latest Revision: 20240725

20240726

PMC8384420

10.7554/eLife.67308

34425938