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medrxiv; 2022.
Preprint in English | medRxiv | ID: ppzbmed-10.1101.2022.02.10.22270799


Introduction Viral sequencing of SARS-CoV-2 has been used for outbreak investigation, but there is limited evidence supporting routine use for infection prevention and control (IPC) within hospital settings. Methods We conducted a prospective non-randomised trial of sequencing at 14 acute UK hospital trusts. Sites each had a 4-week baseline data-collection period, followed by intervention periods comprising 8 weeks of 'rapid' (<48h) and 4 weeks of 'longer-turnaround' (5-10 day) sequencing using a sequence reporting tool (SRT). Data were collected on all hospital onset COVID-19 infections (HOCIs; detected [≥]48h from admission). The impact of the sequencing intervention on IPC knowledge and actions, and on incidence of probable/definite hospital-acquired infections (HAIs) was evaluated. Results A total of 2170 HOCI cases were recorded from October 2020-April 2021, with sequence reports returned for 650/1320 (49.2%) during intervention phases. We did not detect a statistically significant change in weekly incidence of HAIs in longer-turnaround (IRR 1.60, 95%CI 0.85-3.01; P=0.14) or rapid (0.85, 0.48-1.50; P=0.54) intervention phases compared to baseline phase. However, IPC practice was changed in 7.8% and 7.4% of all HOCI cases in rapid and longer-turnaround phases, respectively, and 17.2% and 11.6% of cases where the report was returned. In a per-protocol sensitivity analysis there was an impact on IPC actions in 20.7% of HOCI cases when the SRT report was returned within 5 days. Conclusion While we did not demonstrate a direct impact of sequencing on the incidence of nosocomial transmission, our results suggest that sequencing can inform IPC response to HOCIs, particularly when returned within 5 days.

medrxiv; 2021.
Preprint in English | medRxiv | ID: ppzbmed-10.1101.2021.09.28.21264240


BackgroundReal-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. MethodsOur 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). FindingsBased on the training data (London data spanning surges 1 and 2), the framework achieved high predictive performance using all variables (AUC-ROC 0{middle dot}89 [0{middle dot}88-0{middle dot}90]) but was almost as predictive using only contact network variables (AUC-ROC 0{middle dot}88 [0{middle dot}86-0{middle dot}90]), and more so than using only hospital contextual (AUC-ROC 0{middle dot}82 [0{middle dot}80-0{middle dot}84]) or patient clinical (AUC-ROC 0{middle dot}64 [0{middle dot}62-0{middle dot}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{middle dot}85 [0{middle dot}82-0{middle dot}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{middle dot}84 [0{middle dot}82-0{middle dot}86] to 0{middle dot}88 [0{middle dot}86-0{middle dot}90]) and the UK validation datasets (AUC-ROC increased from 0{middle dot}52 [0{middle dot}49-0{middle dot}53] to 0{middle dot}68 [0{middle dot}64-0{middle dot}70]). InterpretationOur 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. FundingMedical Research Foundation, World Health Organisation, Engineering and Physical Sciences Research Council, National Institute for Health Research, Swiss National Science Foundation, German Research Foundation.

Respiratory Tract Infections
medrxiv; 2021.
Preprint in English | medRxiv | ID: ppzbmed-10.1101.2021.04.13.21255342


IntroductionNosocomial transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been a significant cause of mortality in National Health Service (NHS) hospitals during the coronavirus disease 2019 (COVID-19) pandemic. The aim of this study is to evaluate the impact of rapid whole genome sequencing of SARS-CoV-2, supported by a novel probabilistic reporting methodology, to inform infection prevention and control (IPC) practice within NHS hospital settings. Methods and analysisCOG-UK HOCI (COG-UK Consortium Hospital-Onset COVID-19 Infections study) is a multicentre, prospective, interventional, superiority study. Eligible patients must be admitted to hospital with first confirmed SARS-CoV-2 PCR positive test result >48h from time of admission, where COVID-19 diagnosis was not suspected upon admission. The projected sample size for 14 participating sites covering all study phases over winter-spring 2020/2021 in the United Kingdom is 2,380 patients. The intervention is the return of a sequence report, within 48 hours in one phase (rapid local lab) and within 5-10 days in a second phase (mimicking central lab use), comparing the viral genome from an eligible study participant with others within and outside the hospital site. The primary outcomes are the incidence of Public Health England (PHE)/IPC-defined SARS-CoV-2 hospital-acquired infection during the baseline and two interventional phases, and proportion of hospital-onset cases with genomic evidence of transmission linkage following implementation of the intervention where such linkage was not suspected by initial IPC investigation. Secondary outcomes include incidence of hospital outbreaks, with and without sequencing data; actual and desirable changes to IPC actions; periods of healthcare worker (HCW) absence. A process evaluation using qualitative interviews with HCWs will be conducted alongside the study and analysis, underpinned by iterative programme theory of the sequence report. Health economic analysis will be conducted to determine cost-benefit of the intervention, and whether this leads to economic advantages within the NHS setting. Ethics and disseminationThe protocol has been approved by the National Research Ethics Service Committee (Cambridge South 20/EE/0118). This manuscript is based on version 5.0 of the protocol. The study findings will be disseminated through peer-reviewed publications. Study Registration numberISRCTN50212645 Strengths and limitations of this studyO_LIThe COG-UK HOCI study harnesses the infrastructure of the UKs existing national COVID-19 genome sequencing platform to evaluate the specific benefit of sequencing to hospital infection control. C_LIO_LIThe evaluation is thought to be the first interventional study globally to assess effectiveness of genomic sequencing for infection control in an unbiased patient selection in secondary care settings. C_LIO_LIA range of institutional settings will participate, from specialist NHS-embedded or academic centres experienced in using pathogen genomics to district general hospitals. C_LIO_LIThe findings are likely to have wider applicability in future decisions to utilise genome sequencing for infection control of other pathogens (such as influenza, respiratory syncytial virus, norovirus, clostridium difficile and antimicrobial resistant pathogens) in secondary care settings. C_LIO_LIThe study has been awarded UK NIHR Urgent Public Health status, ensuring prioritised access to NIHR Clinical Research Network (CRN) research staff to recruit patients. C_LIO_LIThe study does not have a randomised controlled design due to the logistics of managing this against diverse standard practice. C_LI

medrxiv; 2021.
Preprint in English | medRxiv | ID: ppzbmed-10.1101.2021.04.07.21254497


Contact tracing is a key tool in epidemiology to identify and control outbreaks of infectious diseases. Existing contact tracing methodologies produce contact maps of individuals based on a binary definition of contact which can be hampered by missing data and indirect contacts. Here, we present our Spatial-temporal Epidemiological Proximity (StEP) model to recover contact maps in disease outbreaks based on movement data. The StEP model accounts for imperfect data by considering probabilistic contacts between individuals based on spatial-temporal proximity of their movement trajectories, creating a robust movement network despite possible missing data and unseen transmission routes. We showcase the potential of StEP for contact tracing with outbreaks of multidrug-resistant bacteria and COVID-19 in a large hospital group in London, UK. In addition to the core structure of contacts that can be recovered using traditional methods of contact tracing, the StEP model reveals missing contacts that connect seemingly separate outbreaks. Comparison with genomic data further confirmed that these additional contacts indeed improve characterisation of disease transmission and so highlights how the StEP framework can inform effective strategies of infection control and prevention.

Communicable Diseases
medrxiv; 2020.
Preprint in English | medRxiv | ID: ppzbmed-10.1101.2020.05.24.20110346


Objectives: To evaluate SARS-CoV-2 surface and air contamination during the peak of the COVID-19 pandemic in London. Design: Prospective cross-sectional observational study. Setting: An acute NHS healthcare provider. Participants: All inpatient wards were fully occupied by patients with COVID-19 at the time of sampling. Interventions: Air and surface samples were collected from a range of clinical areas and a public area of the hospital. An active air sampler was used to collect three or four 1.0 m3 air samples in each area. Surface samples were collected by swabbing approximately 25 cm2 of items in the immediate vicinity of each air sample. SARS-CoV-2 was detected by RT-qPCR and viral culture using Vero E6 and Caco2 cells; additionally the limit of detection for culturing SARS-CoV-2 dried onto surfaces was determined. Main outcome measures: SARS-CoV-2 detected by PCR or culture. Results: Viral RNA was detected on 114/218 (52.3%) of surface and 14/31 (38.7%) air samples but no virus was cultured. The proportion of surface samples contaminated with viral RNA varied by item sampled and by clinical area. Viral RNA was detected on surfaces and in air in public areas of the hospital but was more likely to be found in areas immediately occupied by COVID-19 patients (67/105 (63.8%) in areas immediately occupied by COVID-19 patients vs. 29/64 (45.3%) in other areas (odds ratio 0.5, 95% confidence interval 0.2-0.9, p=0.025, Fishers exact test). The PCR Ct value for all surface and air samples (>30) indicated a viral load that would not be culturable. Conclusions: Our findings of extensive viral RNA contamination of surfaces and air across a range of acute healthcare settings in the absence of cultured virus underlines the potential risk from surface and air contamination in managing COVID-19, and the need for effective use of PPE, social distancing, and hand/surface hygiene.