Epidemiological data and genome sequencing reveals that nosocomial transmission of SARS-CoV-2 is underestimated and mostly mediated by a small number of highly infectious individuals

Lumley, Sheila F; Constantinides, Bede; Sanderson, Nicholas; Rodger, Gillian; Street, Teresa; Swann, Jeremy; Chau, Kevin; O’Donnell, Denise; Warren, Fiona C; Hoosdally, Sarah; Team, Control; O’Donnell, Anne-Marie; Walker, Timothy M; Stoesser, Nicole; Butcher, Lisa; Peto, Tim; Crook, Derrick W.; Jeffery, Katie; Matthews, Philippa C.; Eyre, David W

doi:10.1016/j.jinf.2021.07.034

Cited by 63 publications

(76 citation statements)

References 32 publications

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“…Length of stay was also found significantly longer for HOCIs (similar to other healthcare-acquired infections 3 ) (Table 2). Moreover, the length of stay and the consecutive length of stay both being significantly longer for HOCIs supports a genomic analysis of HOCI by Lumley and colleagues 10 , suggesting acquisition could be linked to previous hospital visits (i.e. patient admitted, discharged, followed by re-admission and a SARS-CoV-2–positive test sample three or more days post re-admission).…”

Section: Discussionsupporting

confidence: 73%

“…We use this as a single category for HOCIs, which includes: indeterminate (positive sample between 3-6 days), probable (positive sample between 7-13 days) and definite (positive sample after 14 or more days) 23 ). This follows from recent genomic evidence 10 that a significant proportion of lower likelihood HOCIs (having spent less time in hospital) are still hospital-acquired, and the comprehensive admission screening policy in our data.…”

Section: Methodssupporting

confidence: 61%

“…Isolation and cohorting of infected patients prevent onward spreading by interrupting transmission chains 7 . Investigating contacts to known infected patients has been an effective epidemiological tool to identify at-risk secondary cases and disease 'super-spreaders' 8 , a strong driver of HOCIs 9,10 , and has played a pivotal role in national COVID-19 responses [11][12][13] . However, exploiting the entire contact network (not just direct links to known infections) provides greater depth to characterise disease spreading 14 .…”

Section: Introductionmentioning

confidence: 99%

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Predicting hospital-onset COVID-19 infections using dynamic networks of patient contacts: an observational study

Myall

Price

Peach

et al. 2021

Preprint

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Background. Real–time prediction is key to prevention and control of healthcare–associated infections. Contacts between individuals drive infections, yet most prediction frameworks fail to capture the dynamics of contact. We develop a real–time machine learning framework that incorporates dynamic patient contact networks to predict patient–level hospital–onset COVID–19 infections (HOCIs), which we test and validate on international multi–site datasets spanning epidemic and endemic periods. Methods. Our framework extracts dynamic contact networks from routinely collected hospital data and combines them with patient clinical attributes and background contextual hospital data to forecast the infection status of individual patients. We train and test the HOCI prediction framework using 51,157 hospital patients admitted to a UK (London) National Health Service (NHS) Trust from 01 April 2020 to 01 April 2021, spanning UK COVID-19 surges 1 and 2. We then validate the framework by applying it to data from a non-UK (Geneva) hospital site during an epidemic surge (40,057 total inpatients) and to data from the same London Trust from a subsequent period post surge 2, when COVID-19 had become endemic (43,375 total inpatients). Findings. Based on the training data (London data spanning surges 1 and 2), the framework achieved high predictive performance using all variables (AUC–ROC 0.89 [0.88–0.90]) but was almost as predictive using only contact network variables (AUC–ROC 0.88 [0.86–0.90]), and more so than using only hospital contextual (AUC–ROC 0.82 [0.80–0.84]) or patient clinical (AUC–ROC 0.64 [0.62–0.66]) variables. The top three risk factors we identified consisted of one hospital contextual variable (background hospital COVID–19 prevalence) and two contact network variables (network closeness, and number of direct contacts to infectious patients), and together achieved AUC–ROC 0.85 [0.82–0.88]. Furthermore, the addition of contact network variables improved performance relative to hospital contextual variables on both the non–UK (AUC–ROC increased from 0.84 [0.82–0.86] to 0.88 [0.86–0.90]) and the UK validation datasets (AUC–ROC increased from 0.52 [0.49–0.53] to 0.68 [0.64–0.70]). Interpretation. Our results suggest that dynamic patient contact networks can be a robust predictor of respiratory viral infections spreading in hospitals. Their integration in clinical care has the potential to enhance individualised infection prevention and early diagnosis.

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Section: Discussionsupporting

confidence: 73%

Section: Methodssupporting

confidence: 61%

Section: Introductionmentioning

confidence: 99%

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Predicting hospital-onset COVID-19 infections using dynamic networks of patient contacts: an observational study

Myall

Price

Peach

et al. 2021

Preprint

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“…Importantly, despite robust efforts, patients and staff acquire SARS-CoV-2 infection in hospitals and this nosocomial infection occurred in rapid super-spreading events when mixing COVID-19 and non-COVID-19 patients through a limited number of highly infectious individuals [6] . Interestingly, as previously hypothesized, despite vaccines administration, SARS-CoV-2 transmission has not been reduced in several countries [7] .…”

Section: Introductionmentioning

confidence: 99%

Confirmation of SARS-CoV-2 airborne dissemination indoors using “COVID-19 traps”

Orenes‐Piñero¹,

Navas-Carrillo²,

Moreno-Docón³

et al. 2022

Journal of Infection

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Understanding the capacity of SARS-CoV-2 virus to infect by surfaces and by airborne dissemination has become in one of the most important issues in the world nowadays. Thus, the aim of this study was to confirm aerosol dissemination from patients with coronavirus infection using “COVID-19 traps” that included different untouched surfaces within them. In a previous study, we evaluated the presence of the virus and its stability in 6 different surfaces placed in the rooms of 6 patients with a positive diagnostic of COVID-19. For that, we performed a case series study with 180 samples collected from the surfaces included into the “COVID-19 traps” located in the rooms of patients in a COVID-19 ward unit (CWU) at a Spanish referral hospital at 24, 48 and 72 hours. RNA was extracted from the surfaces with a swab and subsequently analyzed with RT-PCR to evaluate the presence of the virus and its stability. Positives were found in almost all the rooms and at all analyzed times. Surfaces could not be touched by patients or health workers, so viral spreading was unequivocally produced by airborne. In addition, ROC curves were performed to corroborate that airborne dissemination was directly associated with patients’ viral load. Thus, aerosol dissemination and patients’ viral load were confirmed as key parameters for SARS-CoV-2 infection. Our results point the importance of SARS-CoV-2 virus airborne dissemination indoors and may shed some light in this debate.

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“…Clinical and management variables were investigated, aiming at identifying potential predictors of late SARS‐CoV‐2 positivity and their impact on patient outcome. 6 , 7 Alongside, potential value of common laboratory test at hospital admission was explored.…”

Section: Introductionmentioning

confidence: 99%

Clinical predictors of late SARS‐CoV‐2 positivity in Italian internal medicine wards

Carbone

Ministrini

Garbarino

et al. 2021

Eur J Clin Investigation

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Epidemiological data and genome sequencing reveals that nosocomial transmission of SARS-CoV-2 is underestimated and mostly mediated by a small number of highly infectious individuals

Cited by 63 publications

References 32 publications

Predicting hospital-onset COVID-19 infections using dynamic networks of patient contacts: an observational study

Predicting hospital-onset COVID-19 infections using dynamic networks of patient contacts: an observational study

Confirmation of SARS-CoV-2 airborne dissemination indoors using “COVID-19 traps”

Clinical predictors of late SARS‐CoV‐2 positivity in Italian internal medicine wards

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