Next-generation whole genome sequencing identifies the direction of norovirus transmission in linked patients.
ABSTRACT: Noroviruses are a highly transmissible and major cause of nosocomial gastroenteritis resulting in bed and hospital-ward closures. Where hospital outbreaks are suspected, it is important to determine the routes of spread so that appropriate infection-control procedures can be implemented. To investigate a cluster of norovirus cases occurring in children undergoing bone marrow transplant, we undertook norovirus genome sequencing by next-generation methods. Detailed comparison of sequence data from 2 linked cases enabled us to identify the likely direction of spread.Norovirus complementary DNA was amplified by overlapping polymerase chain reaction (PCR) from 13 stool samples from 5 diagnostic real-time PCR-positive patients. The amplicons were sequenced by Roche 454, the genomes assembled by de novo assembly, and the data analyzed phylogenetically.Phylogenetic analysis indicated that patients were infected by viruses similar to 4 distinct GII.4 subtypes and 2 patients were linked by the same virus. Of the 14 sites at which there were differences between the consensus sequences of the 2 linked viral genomes, 9 had minor variants present within one or the other patient. Further analysis confirmed that minor variants at all 9 sites in patient B w ere present as the consensus sequence in patient A.Phylogenetic analysis excluded a common source of infection in this apparent outbreak. Two of 3 patients on the same ward had closely related viruses, raising the possibility of cross-infection despite protective isolation. Analysis of deep sequencing data enabled us to establish the likely direction of nosocomial transmission.
Project description:<b>Background:</b> Healthcare-associated infections represent a major threat to patient, staff and visitor safety. Identification of episodes that are likely to have resulted from nosocomial transmission has important implications for infection control. Routinely collected data on ward admissions and sample dates, combined with pathogen genomic information could provide useful insights. We describe a novel, open-source, application for visualising these data, and demonstrate its utility for investigating nosocomial transmission using a case study of a large outbreak of norovirus infection. <b>Methods:</b> We developed the application using Shiny, a web application framework for R. For the norovirus case study, cases were defined as patients who had a faecal sample collected at the hospital in a winter season that tested positive for norovirus. Patient demographics and ward admission dates were extracted from hospital systems. Detected norovirus strains were genotyped and further characterised through sequencing of the hypervariable P2 domain. The most commonly detected sub-strain was visualised using the interactive application. <b>Results:</b> There were 156 norovirus-positive specimens collected from 107 patients. The most commonly detected sub-strain affected 30 patients in five wards. We used the interactive application to produce three visualisations: a bar chart, a timeline, and a schematic ward plan highlighting plausible transmission links. Visualisations showed credible links between cases on the elderly care ward. <b>Conclusions:</b> Use of the interactive application provided insights into transmission in this large nosocomial outbreak of norovirus, highlighting where infection control practices worked well or could be improved. This is a flexible tool that could be used for investigation of any infection in any hospital by interactively changing parameters. Challenges include integration with hospital systems for extracting data. Prospective use of this application could inform better infection control in real time.
Project description:<h4>Background</h4>Norovirus outbreaks cause severe medico-socio-economic problems affecting healthcare workers and patients. The aim of the study was to investigate prevalence of norovirus infection and risk factors for infection in healthcare workers during nosocomial outbreaks.<h4>Methods</h4>A cross-sectional study of norovirus infections in healthcare workers was performed in seven outbreak wards in a large university hospital. Packs (swab for rectal sampling, and questionnaire) were posted to healthcare workers on notification of a ward outbreak. Rectal samples were examined with norovirus-specific real-time PCR. Replies from questionnaires were analysed using logistic regression models with norovirus genogroup (G)II positive findings as dependent variable. The results are expressed as odds ratios (OR) with 95% confidence intervals (CI). Sequencing and phylogenetic analyses (1040 nucleotides) were used to characterize norovirus strains from healthcare workers. Cluster analyses included norovirus GII.4 strains detected in ward patients during the ongoing outbreaks.<h4>Results</h4>Of 308 packs issued to healthcare workers, 129 (42%) were returned. norovirus GII was detected in 26 healthcare workers (20.2%). Work in cohort care (OR 4.8, 95% CI 1.4-16.3), work in wards for patients with dementia (OR 13.2, 95% CI 1.01-170.7), and having diarrhoea, loose stools or other gastrointestinal symptoms the last week (OR 7.7, 95% CI 2.5-27.2) were associated with increased norovirus prevalence in healthcare workers. Sequencing revealed norovirus GII.4 in healthcare workers samples, and strains detected in healthcare workers and ward patients during a given ward outbreak showed ≥ 99% similarity.<h4>Conclusion</h4>Norovirus positive findings in healthcare workers were strongly associated with symptomatic infection, close contact with sick patients, and dementia nursing.
Project description:Background:Norovirus is a leading cause of worldwide and nosocomial gastroenteritis. The study aim was to assess the utility of molecular epidemiology using full genome sequences compared to routine infection prevention and control (IPC) investigations. Methods:Norovirus genomes were generated from new episodes of norovirus at a pediatric tertiary referral hospital over a 19-month period (n = 182). Phylogeny identified clusters of related sequences that were verified using epidemiological and clinical data. Results:Twenty-four clusters of related norovirus sequences ("sequence clusters") were observed, including 8 previously identified by IPC investigations ("IPC outbreaks"). Seventeen sequence clusters (involving 77/182 patients) were corroborated by epidemiological data ("epidemiologically supported clusters"), suggesting transmission between patients. Linked infections were identified among 44 patients who were missed by IPC investigations. Thirty-three percent of norovirus sequences were linked, suggesting nosocomial transmission; 24% of patients had nosocomial infections from an unknown source; and 43% were norovirus positive on admission. Conclusions:We show there are frequent introductions of multiple norovirus strains with extensive onward nosocomial transmission of norovirus in a pediatric hospital with a high proportion of immunosuppressed patients nursed in isolation. Phylogenetic analysis using full genome sequences is more sensitive than classic IPC investigations for identifying linked cases and should be considered when investigating norovirus nosocomial transmission. Sampling of staff, visitors, and the environment may be required for complete understanding of infection sources and transmission routes in patients with nosocomial infections not linked to other patients and among patients with phylogenetically linked cases but no evidence of direct contact.
Project description:RAW264.7 macrophages infected with MNV-1 and mock infected gene expression measured by microarray. To be published in Waugh, E. Chen, A. Baird, M. Fleming, S. Brown, C.M. and V K. Ward (2014) Characterization of the chemokine response of RAW264.7 cells to infection by murine norovirus. Virus Genes Four Samples, two mock and two MNV-1 infected.
Project description:Nosocomial severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections have severely affected bed capacity and patient flow. We utilized whole-genome sequencing (WGS) to identify outbreaks and focus infection control resources and intervention during the United Kingdom's second pandemic wave in late 2020. Phylogenetic analysis of WGS and epidemiological data pinpointed an initial transmission event to an admission ward, with immediate prior community infection linkage documented. High incidence of asymptomatic staff infection with genetically identical viral sequences was also observed, which may have contributed to the propagation of the outbreak. WGS allowed timely nosocomial transmission intervention measures, including admissions ward point-of-care testing and introduction of portable HEPA14 filters. Conversely, WGS excluded nosocomial transmission in 2 instances with temporospatial linkage, conserving time and resources. In summary, WGS significantly enhanced understanding of SARS-CoV-2 clusters in a hospital setting, both identifying high-risk areas and conversely validating existing control measures in other units, maintaining clinical service overall.
Project description:Norovirus (NoV) is an important cause of nosocomial gastroenteric outbreaks. This 5-month study was designed to characterize NoV contamination and airborne dispersal in patient rooms during hospital outbreaks. Air vents, overbed tables, washbasins, dust, and virus traps designed to collect charged particles from the air were swabbed to investigate the possibility of NoV contamination in patient rooms during outbreaks in seven wards and in an outbreak-free ward. Symptomatic inpatients were also sampled. Nucleic acid extracts of the samples were examined for NoV RNA using genogroup I (GI) and GII real-time reverse transcription-PCR (RT-PCR). The NoV strains were characterized by RT-PCR, sequencing, and phylogenetic analysis of the RNA-dependent RNA-polymerase-N/S capsid-coding region (1,040 nucleotides [nt]). Patient strains from two outbreaks in one ward were sequenced across the RNA-dependent-RNA-polymerase major capsid-coding region (2.5 kb), including the hypervariable P2 domain. In the outbreak wards, NoV GII was detected in 48 of 101 (47%) environmental swabs and 63 of 108 patients (58%); NoV genotype II.4 was sequenced from 18 environmental samples, dust (n = 8), virus traps (n = 4), surfaces (n = 6), and 56 patients. In contrast, NoV GII was detected in 2 (GII.4) of 28 (7%) environmental samples and in 2 (GII.6 and GII.4) of 17 patients in the outbreak-free ward. Sequence analyses revealed a high degree of similarity (>99.5%, 1,040 nt) between NoV GII.4 environmental and patient strains from a given ward at a given time. The strains clustered on 11 subbranches of the phylogenetic tree, with strong correlations to time and place. The high nucleotide similarity between the NoV GII.4 strains from patients and their hospital room environment provided molecular evidence of GII.4 dispersal in the air and dust; therefore, interventional cleaning studies are justified.
Project description:This study was addressed to the relationship between norovirus and acute diarrhea in hospitalized children, including hospital-acquired infection (HAI) and community-acquired infection (CAI) in a children's hospital in Beijing. RT-PCR was used to detect norovirus in stool specimen, followed by sequence analysis for PCR products. From 2010 to 2013, a total of 1248 specimens, including 661 from the HAI group and 587 from the CAI group were tested for norovirus. Norovirus were detected in 380 of 1248 (30.4%) diarrheal specimens. The positive rate for norovirus detection was higher in children within HAI group than CAI group (35.3%, 232/661 vs. 25.6%, 148/587), and the difference was significant (X2 = 14.35, P<0.05). For age distribution, the highest positivity rates of norovirus were in age of 0-5 months for HAI group and 12-23 months for CAI group. In the study, 262 amplicons of the VP1 region from norovirus-positive specimens were sequenced, which showed GII.3 and GII.4 norovirus were the most common genotypes detected in 50.0% (n = 131) and 48.9% (n = 128) of the positive specimens, respectively. Regarding the wards distribution, GII.3 norovirus was mainly detected in ward for neonatal diseases (36/85 in HAI group; 19/46 in CAI group), GII.4 norovirus was mainly detected in ward for respiratory and digestive diseases (21/85 in HAI group; 15/33 in CAI group).The data elaborated the importance of norovirus in hospital associated infectious diarrhea. The prevalence of norovirus is higher from HAI group than CAI group, and the norovirus from the patients in CAI group could be the source of infection in HAI group.
Project description:Over the last years, a number of stochastic models have been proposed for analysing the spread of nosocomial infections in hospital settings. These models often account for a number of factors governing the spread dynamics: spontaneous patient colonization, patient-staff contamination/colonization, environmental contamination, patient cohorting or healthcare workers (HCWs) hand-washing compliance levels. For each model, tailor-designed methods are implemented in order to analyse the dynamics of the nosocomial outbreak, usually by means of studying quantities of interest such as the reproduction number of each agent in the hospital ward, which is usually computed by means of stochastic simulations or deterministic approximations. In this work, we propose a highly versatile stochastic modelling framework that can account for all these factors simultaneously, and which allows one to exactly analyse the reproduction number of each agent at the hospital ward during a nosocomial outbreak. By means of five representative case studies, we show how this unified modelling framework comprehends, as particular cases, many of the existing models in the literature. We implement various numerical studies via which we (i) highlight the importance of maintaining high hand-hygiene compliance levels by HCWs, (ii) support infection control strategies including to improve environmental cleaning during an outbreak and (iii) show the potential of some HCWs to act as super-spreaders during nosocomial outbreaks.
Project description:The analysis of nosocomial infection data for communicable pathogens is complicated by two facts. First, typical pathogens more commonly cause asymptomatic colonization than overt disease, so transmission can be only imperfectly observed through a sequence of surveillance swabs, which themselves have imperfect sensitivity. Any given set of swab results can therefore be consistent with many different patterns of transmission. Second, data are often highly dependent: the colonization status of one patient affects the risk for others, and, in some wards, repeated admissions are common. Here, the authors present a method for analyzing typical nosocomial infection data consisting of results from arbitrarily timed screening swabs that overcomes these problems and enables simultaneous estimation of transmission and importation parameters, duration of colonization, swab sensitivity, and ward- and patient-level covariates. The method accounts for dependencies by using a mechanistic stochastic transmission model, and it allows for uncertainty in the data by imputing the imperfectly observed colonization status of patients over repeated admissions. The approach uses a Markov chain Monte Carlo algorithm, allowing inference within a Bayesian framework. The method is applied to illustrative data from an interrupted time-series study of vancomycin-resistant enterococci transmission in a hematology ward.
Project description:This study describes a sporadically occurring 4-year outbreak of methicillin-resistant <i>Staphylococcus aureus</i> (MRSA) originating from a surgical ward. Whole-genome sequencing (WGS) identified the outbreak clone as <i>spa</i> type t267, sequence type ST97, and SCC<i>mec</i> IVa. Prompted by the finding of four patients within 6 months in the same ward with this unusual MRSA type, an outbreak was suspected. Subsequent MRSA screening in the ward in February 2017 identified three-additional patients and two health care workers (HCWs) with t267/ST97-IVa. WGS linked these 9 isolates to 16 previous isolates in our WGS database and the outbreak thus included 23 patients and two HCWs. Twenty-one patients had a connection to the surgery ward during the period 2013-2017, but half of them had MRSA diagnosed in the community long after discharge. The community debut of several patients MRSA infections weeks to months after hospital discharge made the identification of a hospital source difficult and it was the SNP relatedness of the isolates that led us to identify the common denominator of hospitalization. An index patient was not identified, but our hypothesis is that HCWs with unrecognized long-term MRSA colonization could have caused sporadic nosocomial transmission due to intermittent breaches in infection prevention and control practice.