key: cord-0734172-itpmyrcg authors: Snell, Luke B.; Fisher, Chloe L.; Taj, Usman; Stirrup, Oliver; Merrick, Blair; Alcolea-Medina, Adela; Charalampous, Themoula; Signell, Adrian W.; Wilson, Harry D.; Betancor, Gilberto; Kia Ik, Mark Tan; Cunningham, Emma; Cliff, Penelope R.; Pickering, Suzanne; Galao, Rui Pedro; Batra, Rahul; Neil, Stuart J.D.; Malim, Michael H.; Doores, Katie J.; Douthwaite, Sam T.; Nebbia, Gaia title: Combined epidemiological and genomic analysis of nosocomial SARS-CoV-2 infection early in the pandemic and the role of unidentified cases in transmission date: 2021-08-13 journal: Clin Microbiol Infect DOI: 10.1016/j.cmi.2021.07.040 sha: 29bd7bc957ab9c0431f8493d9f2b35de0d28e3fc doc_id: 734172 cord_uid: itpmyrcg OBJECTIVES: Analysis of nosocomial transmission in the early stages of the pandemic at a large multi-site healthcare institution. Nosocomial incidence is linked with infection control interventions.. METHODS: Viral genome sequence and epidemiological data were analysed for 574 consecutive SARS-CoV-2 PCR-positive patients including 86 nosocomial cases during the first 19 days of the pandemic. RESULTS: 44 putative transmission clusters were found through epidemiological analysis, which included 234 cases and all 86 nosocomial cases. SARS-CoV-2 genome sequence was obtained from 168/234 (72%) of these cases in epidemiological clusters, including 77/86 (90%) nosocomial cases. Only 75/168 (45%) linked, sequenced cases were not refuted by applying genomic data, creating 14 final clusters accounting for 59/77 (77%) sequenced nosocomial cases. Viral haplotypes from these clusters were enriched 1-14x (median 4x) compared to the community. Three factors implicated unidentified cases in transmission: i) community-onset or indeterminate cases were absent in 7/14 (50%) of clusters ii) 4 (29%) clusters had additional evidence of cryptic transmission. iii) In 3 (21%) clusters, diagnosis of the earliest case was delayed which may have facilitated transmission. Nosocomial cases decreased to low levels (0-2 per day) despite continuing high numbers of admissions of community-onset SARS-CoV-2 cases (40-50 per day) and before the impact of introducing universal face-masks or banning hospital visitors. CONCLUSION: Genomics was necessary to accurately resolve transmission clusters Our data supports unidentified cases, such as healthcare workers or asymptomatic patients, as important vectors of transmission. Evidence is needed to ascertain whether routine screening increases case ascertainment and limits nosocomial transmission. Genomics UK (COG-UK) consortium 7# , Jonathan D Edgeworth 1,2,^ & Ali R Awan 3 *^ Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was first reported in Wuhan, China in December 2019 [1] , with over 4 million deaths having since been reported worldwide [2] . Cases in the UK increased rapidly during March 2020 leading to social distancing policies [3] [4] . On March 23 rd , legislation compelled the UK population to stay home with only limited exceptions. COVID-19 hospital admissions peaked 1 week later, around April 1st [5] . Nosocomial infection may account for 10-20% of all confirmed cases [6] [7] [8] , with associated mortality of up to 30% [7] . Most SARS-CoV-2 transmission studies during the first wave utilised epidemiological analysis alone to identify outbreaks [9] [10] [11] [12] [13] . The main limitation with using epidemiology alone is that when point prevalence is high, for instance at 2.2% in London during April 2020 [4] , this increases the chance two people in epidemiological contact are independent cases. Furthermore, a wide incubation period of 2-14 days [14, 15] means infections that arise several days after hospital admission may still have been acquired in the community. Epidemiological data can be supplemented with SARS-CoV-2 genome sequence to aid analysis of transmission [16] [17] . Genomic analysis is complicated during early stages of the pandemic due to low genetic diversity, with less than 200 mutations recognised by April 2020 [18] . Thus two people infected with an identical strain may not be epidemiologically-linked. This study combines epidemiological and genomic data to analyse clusters of nosocomial SARS-CoV-2 transmission during the first weeks of the pandemic before infection control policies had been formalised, and when community incidence was high [3] [4] . Understanding nosocomial transmission would help set priorities for future infection control planning. From March 13th only patients requiring admission and with cough, fever or shortness of breath were tested for SARS-CoV-2 infection, as per PHE recommendations. Inpatients developing these symptoms were tested before isolation in side rooms whilst awaiting results. Confirmed cases were cohorted in wards with other confirmed cases only. Very exceptionally, confirmed cases stayed on a non-COVID ward in a side room due to capacity issues. Neither asymptomatic individuals nor patients/staff exposed to known cases were routinely screened for infection. RNA extracts were processed using the ARTIC protocol v1.0 [19] and V3 primers set[20] using Oxford Nanopore Technology and the ARTIC bioinformatics pipeline v1.0 [21] . Lineages were assigned using Pangolin [22] v1.1.14 with lineages v2020-05-19. Transmission clusters were deduced using combined epidemiological and genetic information. [16] . Notably however, one study found evidence that 2 SNP could occur in institutional outbreaks in 17 days [25] . Maximum likelihood phylogenetic trees were derived using phangorn (v2.5.5) and plotted with ggtree compatible symptoms and SARS-CoV-2 seroconversion. Sequential serum samples were collected every 1-2 weeks and tested using ELISA [27] . The median time between symptom onset and seroconversion in symptomatic HCW was used to infer infectious period for those asymptomatic. HCW absenteeism was retrieved from human resource records. J o u r n a l P r e -p r o o f By March 31 st there were 574 laboratory-confirmed cases (Supplementary Table S1 ). Most were admitted (483/574: 84%, Supplementary Table S2 ) with a median length of stay of 12 days (IQR: 5-27, Table 1 ). New cases peaked between March 31st and April 8th, before falling steadily through April ( Figure 1 ). The daily number of probable and definite nosocomial cases peaked earlier on March 23rd with 12 new cases. Nosocomial cases then rapidly declined to 0-2 cases per day during April (Figure 1 ) and none in the following 4 months (data not shown). Next, we reapplied our clustering method with a less stringent SNP threshold for excluding cases of ≥3 SNPs. This identified three further cases possibly linked to existing clusters (Supplementary Table S6 ): case 84 (probable nosocomial) and case 135 (community-onset) to STH3; and case 359 (definite nosocomial) to STH1. Including them in final clusters would increase the proportion of sequenced nosocomial cases accounted for to 61/77 (79%). Eighteen remaining sequenced nosocomial cases (18/77; 23%) are not present in the final clusters. We reviewed the epidemiological clusters in which these eighteen remaining nosocomial cases were placed (Supplementary Table S7 ). In total, 265/344 (77%) of all cases in these epidemiological clusters were sequenced and none shared a viral haplotype within <2 SNP of a remaining nosocomial patient, excluding them from being part of a transmission network. Instead, it is plausible that nonsequenced cases in these epidemiological clusters (79/344, 23%) or other unidentified cases (e.g. point-source infectors like HCW) could form a transmission cluster with our remaining nosocomial cases. Originators of final transmission clusters, spatial distribution and enrichment of haplotypes. 7/12 (58%) of hospital clusters were contained within single wards and 5/12 (42%) spread across ≥2 wards (Supplemental Figure 3 ). In-depth ward movement data available in Figure 4 and The validity of these 14 final clusters was supported by calculating haplotype enrichment compared to community sequences reported in COG-UK CLIMB database [26] of between 1 and 14-fold (median 4-fold) (Figure 2c , Supplementary Table S5) . We assessed whether non-sequenced cases could have originated clusters which contained no community-onset or indeterminate cases by reviewing non-sequenced cases present in the same epidemiological cluster (Supplementary Table S8 ). We excluded cases as potential originators if i) they were symptomatic after the first nosocomial case (or sampling date was later, if symptom onset not known), or ii) viral haplotype differed by ≥2 SNP or iii) if cases were not community-onset or indeterminate. Only 2/8 (25%) of the clusters without originators could have potentially been originated by an non-sequenced community-onset case with epidemiological linkage (case 50 or case 187 cluster GUY4; case 62 for cluster GUY5). Conversely, where community-onset or indeterminate cases were found as possible originators, earlier testing after symptom onset could have identified possible originators in 3 clusters (Supplementary Figure 2) . For example case 34 in STH2 was symptomatic for 3 days before sample collection; case 90 in STH3 for 5 days, and case 277 in GUY1 for 6 days. Additionally, other cases were tested several days after symptom onset possibly facilitating onward transmission; for instance case 173 in STH2, case 160 in GUY1, case 295 in GUY3 and case 471 in GUY5. Four clusters had other evidence of cryptic transmission: Clusters GUY1 and GUY2 involved different wards in the same building with an identical viral haplotype that was highly enriched compared with J o u r n a l P r e -p r o o f community haplotypes, suggesting these clusters are linked by cryptic transmission. GUY4 and GUY5 both similarly involve neighbouring wards with high enrichment of viral haplotype. Of note, these neighbouring wards share multiple HCW, including allied health professionals, cleaners, and visiting clinicians. These HCW plausibly may have served as vectors for cryptic transmission between wards. Importantly, nosocomial cases declined before any possible impact from universal surgical mask use by HCWs or banning of hospital visitors. This may be due to falling infection rates in the community after implementation of non-pharmacological measures, effectively social distancing, decreasing transmission to admitted patients in hospitals. Interestingly community infections were predicted to peak around the same time as social distancing was introduced [4] , with nosocomial cases beginning to fall around 7 days after this point, consistent with a delay of 5-7 days for incubation. Moreover, nosocomial cases declined even whilst admission of community-onset cases continued to rise. This suggests that infection control measures can be effective at preventing transmission from admitted cases to other patients by rapid diagnosis, isolation and use of personal protective equipment. Community-onset cases may have passed peak viral shedding (often first 4 days of illness [29] ) upon admission to hospital, with admission being a median of 7 days after symptom onset in our cohort. Instead, we hypothesise that infection is often introduced into the hospital by HCW or patients who are minimally/asymptomatic, who remain unidentified. In summary, this study supports the role of genome sequencing in SARS-CoV-2 outbreak investigation. In addition, the presence of cryptic transmission and the implication of unidentified cases suggests routine screening of both HCW and patients may be valuable. It will be important to assess whether interventions such as universal mask use and intermittent screening limit nosocomial transmission. Funding. Table S7 , Methods) is shown on top of each cluster column. .....1.....1.......11....... 1......1.....1.......1.1...... 1...1..1.1...1...1...1.....111 1......1.....1.......1.. 1 11.....1.....1......11.....111 11.....1.....1......11 1....1...1.11........1.1... ..1...........1.1.......11.... .....11.......1...11....1..... .....11.......1.........1..... .....11.......1.........1..... .....11.......1.........1..... .....11...11..1.........1..... C241T C1150T T1490A A1678G C2306T A2480G C2558T C3037T C3096T C3784T C6070T A12918G G13627T C14408T C14805T C15540T T17247C G18653A G22094A A22252G A22481T A23403G C25350T G25429T G26144T T26870G A28338G G28881A G28882A G28883C A pneumonia outbreak associated with a new coronavirus of probable bat origin COVID-19 map-Johns Hopkins Coronavirus Resource Center SARS-CoV-2 infection in London, England: changes to community point prevalence around lockdown time COVID-19) in the UK. 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