OBJECTIVE:A small percentage of universities and colleges conducted mass SARS-CoV-2 testing. However, universal testing is resource-intensive, strains national testing capacity, and false negative tests can encourage unsafe behaviors. PARTICIPANTS:A large urban university campus. METHODS:Virus control centered on three pillars: mitigation, containment, and communication, with testing of symptomatic and a random subset of asymptomatic students. RESULTS:Random surveillance testing demonstrated a prevalence among asymptomatic students of 0.4% throughout the term. There were two surges in cases that were contained by enhanced mitigation and communication combined with targeted testing. Cumulative cases totaled 445 for the term, most resulting from unsafe undergraduate student behavior and among students living off-campus. A case rate of 232/10,000 undergraduates equaled or surpassed several peer institutions that conducted mass testing. CONCLUSIONS:An emphasis on behavioral mitigation and communication can control virus transmission on a large urban campus combined with a limited and targeted testing strategy.

Abstract Background Healthcare-associated infections can be acquired via transmission of pathogens within the healthcare setting. Often, patients are assumed to have short duration (< 90 days) of transmissibility with bacterial pathogens after developing a clinical infection. This assumption may wrongly exclude patients as sources of transmission when they have persistent bacterial carriage. We studied patients with persistent carriage and associated transmission using whole genome sequencing surveillance. Methods Patient culture positive isolates for select bacterial pathogens between 11/2016 and 11/2019 were collected if the patient was housed in the hospital for ≥3 days or had a recent healthcare exposure in the prior 30 days. Isolates were considered genetically related with ≤15 SNPs for all organisms except C. difficile (≤2 SNPs). Patients with serial isolates separated by >100 days were examined for other patients with related isolates and epidemiological commonalities between the first and last culture dates. Results There were 779 related isolates from 369 unique patients (range 2-11 isolates/patient). The mean time from first to last culture date was 81.9 days (median 33 days, range 1-899 days) (Figure 1). 77 patients had isolates that were related to another patient of which 18 (23%) patients had >100 days between their first and last isolate (median 216, range 103-899). Of these, 9 (50%) patients had epidemiological links with another patient between their first and last isolate culture dates. The median time from exposure to positive culture date of the exposed patient was 14 days (mean 34, range 2-115). An example of potential transmission is shown in Figure 2. Days between the first and last related isolates within the same patient Example of patient with persistent carriage of K. pneumoniae and evidence of transmission to another patient Conclusion Some patients had persistent carriage with the same strain for over two years and appear to be a potential source of ongoing transmission to other patients. WGS surveillance, in addition to detecting outbreaks, can identify patients with persistent colonization as potential a transmission source. Healthcare outbreak investigations should include patients with persistent carriage as potential sources based upon temporal restrictions. Disclosures Graham Snyder, MD, SM, Infectious Diseases Connect: Advisor/Consultant Daria Van Tyne, PhD, Century Therapeutics, Inc: Advisor/Consultant

BACKGROUND:Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) control on college campuses is challenging given communal living and student social dynamics. Understanding SARS-CoV-2 transmission among college students is important for the development of optimal control strategies. METHODS:SARS-CoV-2 nasal swab samples were collected from University of Pittsburgh students for symptomatic testing and asymptomatic surveillance from August 2020 through April 2021 from 3 campuses. Whole-genome sequencing (WGS) was performed on 308 samples, and contact tracing information collected from students was used to identify transmission clusters. RESULTS:We identified 31 Pangolin lineages of SARS-CoV-2, the majority belonging to B.1.1.7 (Alpha) and B.1.2 lineages. Contact tracing identified 142 students (46%) clustering with each other; WGS identified 53 putative transmission clusters involving 216 students (70%). WGS identified transmissions that were missed by contact tracing. However, 84 cases (27%) could not be linked by either WGS or contact tracing. Clusters were most frequently linked to students residing in the same dormitory, off-campus roommates, friends, or athletic activities. CONCLUSIONS:The majority of SARS-CoV-2-positive samples clustered by WGS, indicating significant transmission across campuses. The combination of WGS and contact tracing maximized the identification of SARS-CoV-2 transmission on campus. WGS can be used as a strategy to mitigate, and further prevent transmission among students.

Healthcare-associated infections (HAIs) cause mortality, morbidity, and waste of health care resources. HAIs are also an important driver of antimicrobial resistance, which is increasing around the world. Beginning in November 2016, we instituted an initiative to detect outbreaks of HAIs using prospective whole-genome sequencing-based surveillance of bacterial pathogens collected from hospitalized patients. Here, we describe the diversity of bacteria sampled from hospitalized patients at a single center, as revealed through systematic analysis of bacterial isolate genomes. We sequenced the genomes of 3,004 bacterial isolates from hospitalized patients collected over a 25-month period. We identified bacteria belonging to 97 distinct species, which were distributed among 14 groups of related species. Within these groups, isolates could be distinguished from one another by both average nucleotide identity (ANI) and principal-component analysis of accessory genes (PCA-A). Core genome genetic distances and rates of evolution varied among species, which has practical implications for defining shared ancestry during outbreaks and for our broader understanding of the origins of bacterial strains and species. Finally, antimicrobial resistance genes and putative mobile genetic elements were frequently observed, and our systematic analysis revealed patterns of occurrence across the different species sampled from our hospital. Overall, this study shows how understanding the population structure of diverse pathogens circulating in a single health care setting can improve the discriminatory power of genomic epidemiology studies and can help define the processes leading to strain and species differentiation. IMPORTANCE Hospitalized patients are at increased risk of becoming infected with antibiotic-resistant organisms. We used whole-genome sequencing to survey and compare over 3,000 clinical bacterial isolates collected from hospitalized patients at a large medical center over a 2-year period. We identified nearly 100 different bacterial species, which we divided into 14 different groups of related species. When we examined how genetic relatedness differed between species, we found that different species were likely evolving at different rates within our hospital. This is significant because the identification of bacterial outbreaks in the hospital currently relies on genetic similarity cutoffs, which are often applied uniformly across organisms. Finally, we found that antibiotic resistance genes and mobile genetic elements were abundant and were shared among the bacterial isolates we sampled. Overall, this study provides an in-depth view of the genomic diversity and evolutionary processes of bacteria sampled from hospitalized patients, as well as genetic similarity estimates that can inform hospital outbreak detection and prevention efforts.

OBJECTIVE:We used SARS-CoV-2 whole-genome sequencing (WGS) and electronic health record (EHR) data to investigate the associations between viral genomes and clinical characteristics and severe outcomes among hospitalized COVID-19 patients. METHODS:We conducted a case-control study of severe COVID-19 infection among patients hospitalized at a large academic referral hospital between March 2020 and May 2021. SARS-CoV-2 WGS was performed, and demographic and clinical characteristics were obtained from the EHR. Severe COVID-19 (case patients) was defined as having one or more of the following: requirement for supplemental oxygen, mechanical ventilation, or death during hospital admission. Controls were hospitalized patients diagnosed with COVID-19 who did not meet the criteria for severe infection. We constructed predictive models incorporating clinical and demographic variables as well as WGS data including lineage, clade, and SARS-CoV-2 SNP/GWAS data for severe COVID-19 using multiple logistic regression. RESULTS:Of 1,802 hospitalized SARS-CoV-2-positive patients, we performed WGS on samples collected from 590 patients, of whom 396 were case patients and 194 were controls. Age (p = 0.001), BMI (p = 0.032), test positive time period (p = 0.001), Charlson comorbidity index (p = 0.001), history of chronic heart failure (p = 0.003), atrial fibrillation (p = 0.002), or diabetes (p = 0.007) were significantly associated with case-control status. SARS-CoV-2 WGS data did not appreciably change the results of the above risk factor analysis, though infection with clade 20A was associated with a higher risk of severe disease, after adjusting for confounder variables (p = 0.024, OR = 3.25; 95%CI: 1.31-8.06). CONCLUSIONS:Among people hospitalized with COVID-19, older age, higher BMI, earlier test positive period, history of chronic heart failure, atrial fibrillation, or diabetes, and infection with clade 20A SARS-CoV-2 strains can predict severe COVID-19.

The first case of the new SARS-CoV-2 Omicron Variant of Concern (VOC) from South Africa was reported to WHO on November 24, 2021.…

Background: Traditional infection prevention (IP) methods for outbreak detection often rely on geotemporal clustering confined to single locations. We recently developed the Enhanced Detection System for Healthcare-Associated Transmission (EDS-HAT), which combines whole-genome sequencing (WGS) surveillance and machine learning of the electronic health record (EHR). Our retrospective research findings show potential transmissions averted and cost savings using EDS-HAT in real time. Here, we describe the process and initial findings from EDS-HAT real-time implementation. Methods: Real-time whole-genome sequencing surveillance began on November 1, 2021. Patient cultures positive for select bacterial pathogens who were hospitalized for ≥3 days or had a recent healthcare exposure in the prior 30-days were collected. Isolates were deemed genetically related if ≤15 single-nucleotide polymorphisms (SNPs) were identified for all organisms except Clostridioides difficile (≤2 SNPs). Clusters were manually investigated by both research and IP teams, and interventions were performed by the IP team. Data on collection, analysis, notification, and intervention dates were gathered. Results: As of January 11, 2022, 413 isolates had undergone whole-genome sequencing. Among them, 18 unique patient isolates were genetically related to ≥1 other isolate, comprising 7 clusters (range, 2–6 patients). Notable findings include a Pseudomonas aeruginosa cluster possibly related to a shared bronchoscope, a pseudo-outbreak of Serratia marcescens related to autopsy blood culture practice, and a cluster of vancomycin-resistant Enterococcus faecium on a shared transplant unit. Only 1 cluster of 2 isolates of Klebsiella pneumoniae had no known possible transmission routes. The median turnaround time from patient’s culture date to IP notification was 19 days (range, 13–28), with noted delays over the winter holiday. Concusions: Real-time WGS can identify small clusters including potentially interruptible transmission routes. Rapid turnaround time, coordination between clinical and genomic laboratories, and a robust IP team are key factors in implementing a WGS surveillance program. Real-time WGS surveillance has the potential to reduce costs for hospitals, improve patient safety, and save lives.Funding: NoneDisclosures: None

At the time of writing, the world continues to witness the extraordinarily rapid evolution and selection of SARS-CoV-2, with the Omicron variants comprising five lineages known as BA.1, BA.2, BA.3, BA.4 and BA.5. In this study, there were 141 SARS-CoV-2 positive nasopharyngeal specimens tested using the RT-PCR BA.1 assay during January-April 2022. Of these, 83.0% specimens were BA.1. While the prevalence rate of BA.1 continued to decrease, BA.2 emerged. Interestingly, BA.4 was detected for the first time in Western Pennsylvania, United States. While the unexpected detection of BA.4 in our study is interesting, and even a single case, our finding underscores the importance of genomic surveillance as a critical tool for tracking emerging variants of SARS-CoV-2 This article is protected by copyright. All rights reserved.

We performed whole genome sequencing on SARS-CoV-2 from 59 vaccinated individuals from southwest Pennsylvania who tested positive between February and September, 2021. A comparison of mutations among vaccine breakthrough cases to a time-matched control group identified potential adaptive responses of SARS-CoV-2 to vaccination.

Background:Whole-genome sequencing (WGS) has traditionally been used in infection prevention to confirm or refute the presence of an outbreak after it has occurred. Due to decreasing costs of WGS, an increasing number of institutions have been utilizing WGS-based surveillance. Additionally, machine learning or statistical modeling to supplement infection prevention practice have also been used. We systematically reviewed the use of WGS surveillance and machine learning to detect and investigate outbreaks in healthcare settings.Methods:We performed a PubMed search using separate terms for WGS surveillance and/or machine-learning technologies for infection prevention through March 15, 2021.Results:Of 767 studies returned using the WGS search terms, 42 articles were included for review. Only 2 studies (4.8%) were performed in real time, and 39 (92.9%) studied only 1 pathogen. Nearly all studies (n = 41, 97.6%) found genetic relatedness between some isolates collected. Across all studies, 525 outbreaks were detected among 2,837 related isolates (average, 5.4 isolates per outbreak). Also, 35 studies (83.3%) only utilized geotemporal clustering to identify outbreak transmission routes. Of 21 studies identified using the machine-learning search terms, 4 were included for review. In each study, machine learning aided outbreak investigations by complementing methods to gather epidemiologic data and automating identification of transmission pathways.Conclusions:WGS surveillance is an emerging method that can enhance outbreak detection. Machine learning has the potential to identify novel routes of pathogen transmission. Broader incorporation of WGS surveillance into infection prevention practice has the potential to transform the detection and control of healthcare outbreaks.

Journal of Medical VirologyEarly View LETTER TO THE EDITORFree Access Emergence of SARS-CoV-2 Omicron BA.5 variant of concern in Western Pennsylvania, United States Tung Phan, Corresponding Author Tung Phan phantg@upmc.edu Department of Pathology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA Correspondence: Tung Phan, Department of Pathology, University of Pittsburgh, Pittsburgh, PA 15213, USA. Email: phantg@upmc.eduSearch for more papers by this authorStephanie Boes, Stephanie Boes Clinical Microbiology Laboratory, UPMC Hospital System, Pittsburgh, Pennsylvania, USASearch for more papers by this authorMelissa McCullough, Melissa McCullough Clinical Microbiology Laboratory, UPMC Hospital System, Pittsburgh, Pennsylvania, USASearch for more papers by this authorJamie Gribschaw, Jamie Gribschaw Clinical Microbiology Laboratory, UPMC Hospital System, Pittsburgh, Pennsylvania, USASearch for more papers by this authorJane W. Marsh, Jane W. Marsh Center for Genomic Epidemiology, University of Pittsburgh, Pittsburgh, Pennsylvania, USASearch for more papers by this authorLee H. Harrison, Lee H. Harrison Center for Genomic Epidemiology, University of Pittsburgh, Pittsburgh, Pennsylvania, USASearch for more papers by this authorAlan Wells, Alan Wells Department of Pathology, University of Pittsburgh, Pittsburgh, Pennsylvania, USASearch for more papers by this author Tung Phan, Corresponding Author Tung Phan phantg@upmc.edu Department of Pathology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA Correspondence: Tung Phan, Department of Pathology, University of Pittsburgh, Pittsburgh, PA 15213, USA. Email: phantg@upmc.eduSearch for more papers by this authorStephanie Boes, Stephanie Boes Clinical Microbiology Laboratory, UPMC Hospital System, Pittsburgh, Pennsylvania, USASearch for more papers by this authorMelissa McCullough, Melissa McCullough Clinical Microbiology Laboratory, UPMC Hospital System, Pittsburgh, Pennsylvania, USASearch for more papers by this authorJamie Gribschaw, Jamie Gribschaw Clinical Microbiology Laboratory, UPMC Hospital System, Pittsburgh, Pennsylvania, USASearch for more papers by this authorJane W. Marsh, Jane W. Marsh Center for Genomic Epidemiology, University of Pittsburgh, Pittsburgh, Pennsylvania, USASearch for more papers by this authorLee H. Harrison, Lee H. Harrison Center for Genomic Epidemiology, University of Pittsburgh, Pittsburgh, Pennsylvania, USASearch for more papers by this authorAlan Wells, Alan Wells Department of Pathology, University of Pittsburgh, Pittsburgh, Pennsylvania, USASearch for more papers by this author First published: 17 June 2022 https://doi.org/10.1002/jmv.27945AboutSectionsPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat Dear Editor The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron variant of concern (B.1.1.529) was initially comprised of three lineages, BA.1, BA.2, and BA.3. Subsequently, BA.4 and BA.5 were detected in South Africa in January and February 2022, respectively (https://www.ecdc.europa.eu/en/news-events/epidemiological-update-sars-cov-2-omicron-sub-lineages-ba4-and-ba5). These two new variants have emerged and become the dominant lineages, driving a new coronavirus disease 2019 (COVID-19) wave in South Africa (https://www.gavi.org/vaccineswork/five-things-weve-learned-about-ba4-and-ba5-omicron-variants). In some provinces of South Africa, BA.4 and BA.5 were reported to be responsible for up to 75% of COVID-19 cases.1 BA.4 and BA.5 are believed to spread to at least 17 countries, suggesting that these variants could spread globally in a similar pattern to BA.2.2 Supporting this contention is a surge in cases and subsequent deaths in Portugal, where BA.4 and BA.5 accounted for 90% of new infections.3 While BA.2 remained the dominant variant in Germany, the number of new infections due to BA.5 has approximately doubled every week, from 0.2% of cases at the end of April to 5.2% by the end of May.4 The increasing numbers of cases due to BA.4 and BA.5 may relate to these variants escaping neutralizing antibodies induced by both vaccination and infection by earlier SARS-CoV-2 variants.5 In addition, BA.4 and BA.5 demonstrated reduced serum neutralization from triple AstraZeneca or Pfizer vaccinated individuals compared to BA.1 and BA.2.6 These data suggest that Omicron has continued to evolve with increasing neutralization escape, raising concern about the possibility of repeat Omicron infections due to continuing evolution within this clade.5, 6 As reported in a previous study, we successfully implemented a two-step approach to identify and distinguish the genetic lineages of SARS-CoV-2 strains circulating in Western Pennsylvania, United States.7 Initially, SARS-CoV-2 positive nasopharyngeal swab specimens, which were previously determined by the Cepheid Xpert Xpress SARS-CoV-2/Flu/RSV test, were further tested by the SARS-CoV-2 Omicron BA.1 variant reverse-transcription polymerase chain reaction (RT-PCR) assay.8 Briefly, viral RNA was extracted from 200 μl of clinical specimens using the bioMérieux EasyMag automated system (Marcy-l'Etoile). All RT-PCR experiments previously described were run on the Applied Biosystems 7500 Fast Dx real-time PCR instrument.8 Positive or negative results of two targets (the deletion del69-70 and the insertion ins214EPE of the spike gene) were generated. BA.1 and BA.2 had “del69-70 positive/ins214EPE positive” and “del69-70 negative/ins214EPE negative,” respectively.7 BA.3, BA4, and BA.5, which shared the same “del69-70 positive/ins214EPE negative” result, underwent whole-genome sequencing to determine their genetic lineages.7 A total of 206 SARS-CoV-2 positive nasopharyngeal swab specimens were randomly selected and tested using the RT-PCR BA.1 assay during January–May 2022. It was found that BA.1 was the predominant variant until 4/5/2022 (94.3%, 115/122). Within a 1-month period, BA.2 outnumbered BA.1, being responsible for 95.5% (21/22) of SARS-CoV-2 specimens during April 20–May 10, 2022 and 95.3% (41/43) during May 11–May 31, 2022 as shown in Figure 1; this was similarly noted in a larger study undertaken by the Allegheny County Health Department (ACHD) (https://www.alleghenycounty.us/Health-Department/Resources/COVID-19/COVID-19-Dashboards.aspx). Figure 1Open in figure viewerPowerPoint The graph presented the changing prevalence of SARS-CoV-2 variants in the UPMC health care system in Allegheny County, Pennsylvania. The numbers below the graph are the number of SARS-CoV-2 positive specimens tested in each time window. SARS-CoV-2, severe acute respiratory syndrome coronavirus 2. Of note, a single case of BA.4 was detected in the period of April 6–April 19, 2022, and another single case of BA.4 was found in the following period of April 20–May 10, 2022; ACHD also detected two cases of BA.4 during this time. Interestingly, two cases of BA.5 were unexpectedly discovered during May 11–May 31, 2022, accounting for 4.7% (2/43) of the SARS-CoV-2 specimens. While the number of the BA.4 and BA.5 variants remain small, their prevalence should be closely monitored through epidemiological investigations and virus whole-genome sequence-based surveillance to see if it continues to increase rapidly. The low level of BA.4 over the past 6 weeks suggests that BA.4 may not outcompete BA.2. Concerningly, the new emergence of BA.5 may not be so benign. Our study is also the first report on the presence of BA.5 currently circulating in Western Pennsylvania, United States. AUTHOR CONTRIBUTIONS Tung Phan and Alan Wells: Designed the study and wrote the manuscript. Stephanie Boes, Jamie Gribschaw, and Melissa McCullough: Performed and managed the PCR-specific testing. Jane W. Marsh and Lee H. Harrison: Performed and managed WGS. All read and approved the final manuscript. ACKNOWLEDGMENTS We thank the UPMC Clinical Microbiology Laboratory for testing the specimens and performing the evaluation. We thank the Microbial Genomic Epidemiology Laboratory and SeqCenter for expert SARS-CoV-2 whole-genome sequencing and analysis. The study was internally funded by the UPMC Clinical Laboratories as part of a Quality Improvement initiative. No funding was obtained from any commercial sources. The funders did not have input into study design, analysis, nor generation of this communication. ETHICS STATEMENT All testing was performed as apart of routine clinical care and performed according to CLIA '88 regulations by appropriate personnel. The entire study was deemed to be a Quality Improvement initiative by the UPMC IRB and approved by the UPMC QI Review Board. DATA AVAILABILITY STATEMENT Data sharing is not applicable to this article as no datasets were generated or analyzed during the current study. All relevant data are presented in the article. Open Research DATA AVAILABILITY STATEMENT Data sharing is not applicable to this article as no datasets were generated or analyzed during the current study. All relevant data are presented in the article. REFERENCES 1Tegally H, Moir M, Everatt J, et al. Continued emergence and evolution of Omicron in South Africa: new BA.4 and BA.5 lineages. medRxiv. 2022. doi:10.1101/2022.05.01.22274406Google Scholar 2WHO says omicron BA.4 and BA.5 subvariants have spread to over a dozen countries. Accessed May 11, 2022. https://www.cnbc.com/2022/05/11/who-says-omicron-bapoint4-and-bapoint5-subvariants-have-spread-to-over-a-dozen-countries.htmlGoogle Scholar 3Omicron variants BA.4 and BA.5 cause surge in deaths and cases in Portugal. Accessed June 03, 2022. https://www.deseret.com/coronavirus/2022/6/3/23153378/new-omicron-variants-ba-4-ba-5-surge-cases-death-in-europe-portugal-south-africaGoogle Scholar 4Omicron subvariant drives spike in cases and deaths in Portugal. Accessed June 03, 2022. https://www.theguardian.com/world/2022/jun/03/omicron-covid-subvariant-drives-spike-in-cases-and-deaths-in-portgualGoogle Scholar 5Hachmann PN, Miller J, Collier YA, et al. Neutralization escape by the SARS-CoV-2 Omicron variants BA.2.12.1 and BA.4/BA.5. medRxiv. 2022. doi:10.1101/2022.05.16.22275151Google Scholar 6Tuekprakhon A, Huo J, Nutalai R, et al. Further antibody escape by Omicron BA.4 and BA.5 from vaccine and BA.1 serum. medRxiv. 2022. doi:10.1101/2022.05.21.492554Google Scholar 7Phan T, Boes S, McCullough M, et al. First detection of SARS-CoV-2 Omicron BA.4 variant in Western Pennsylvania, United States. J Med Virol. 2022. doi:10.1002/jmv.27846Google Scholar 8Phan T, Boes S, McCullough M, et al. Development of a one-step qualitative RT-PCR assay to detect the SARS-CoV-2 Omicron (B.1.1.529) variant in respiratory specimens. J Clin Microbiol. 2022; 60:e0002422. doi:10.1128/jcm.00024-22CrossrefPubMedGoogle Scholar CONFLICT OF INTEREST The authors declare no conflict of interest. Early ViewOnline Version of Record before inclusion in an issue FiguresReferencesRelatedInformation

Background. Whole genome sequencing (WGS) surveillance and electronic health record data mining have the potential to greatly enhance the identification and control of hospital outbreaks. The objective was to develop methods for examining economic value of a WGS surveillance-based infection prevention (IP) program compared to standard of care (SoC). Methods. The economic value of a WGS surveillance-based IP program was assessed from a hospital's perspective using historical outbreaks from 2011-2016. We used transmission network of outbreaks to estimate incremental cost per transmission averted. The number of transmissions averted depended on the effectiveness of intervening against transmission routes, time from transmission to positive culture results and time taken to obtain WGS results and intervene on the transmission route identified. The total cost of an IP program included cost of staffing, WGS, and treating infections. Results. Approximately 41 out of 89 (46%) transmissions could have been averted under the WGS surveillance-based IP program, and it was found to be a less costly and more effective strategy than SoC. The results were most sensitive to the cost of performing WGS and the number of isolates sequenced per year under WGS surveillance. The probability of the WGS surveillancebased IP program being cost-effective was 80% if willingness to pay exceeded $2400 per transmission averted. Conclusions. The proposed economic analysis is a useful tool to examine economic value of a WGS surveillance-based IP program. These methods will be applied to a prospective evaluation of WGS surveillance compared to SoC.

Background Most hospitals use traditional infection prevention (IP) methods for outbreak detection. We developed the Enhanced Detection System for Healthcare-Associated Transmission (EDS-HAT), which combines whole-genome sequencing (WGS) surveillance and machine learning (ML) of the electronic health record (EHR) to identify undetected outbreaks and the responsible transmission routes, respectively. Methods We performed WGS surveillance of healthcare-associated bacterial pathogens from November 2016 to November 2018. EHR ML was used to identify the transmission routes for WGS-detected outbreaks, which were investigated by an IP expert. Potential infections prevented were estimated and compared with traditional IP practice during the same period. Results Of 3165 isolates, there were 2752 unique patient isolates in 99 clusters involving 297 (10.8%) patient isolates identified by WGS; clusters ranged from 2-14 patients. At least 1 transmission route was detected for 65.7% of clusters. During the same time, traditional IP investigation prompted WGS for 15 suspected outbreaks involving 133 patients, for which transmission events were identified for 5 (3.8%). If EDS-HAT had been running in real time, 25-63 transmissions could have been prevented. EDS-HAT was found to be cost-saving and more effective than traditional IP practice, with overall savings of $192 408-$692 532. Conclusions EDS-HAT detected multiple outbreaks not identified using traditional IP methods, correctly identified the transmission routes for most outbreaks, and would save the hospital substantial costs. Traditional IP practice misidentified outbreaks for which transmission did not occur. WGS surveillance combined with EHR ML has the potential to save costs and enhance patient safety. Whole-genome sequencing surveillance of bacterial pathogens and machine learning of the electronic health record finds previously undetected outbreaks and their transmission routes, which can increase patient safety and save costs.

Nasal and nasopharyngeal swab specimens tested by the Cepheid Xpert Xpress SARS-CoV-2 were analyzed by whole-genome sequencing based on impaired detection of the N2 target. Each viral genome had at least one mutation in the N gene, which likely arose independently in the New York City and Pittsburgh study sites.

Background. The mechanisms by which Neisseria meningitidis cause persistent human carriage and transition from carriage to invasive disease have not been fully elucidated. Methods. Georgia and Maryland high school students were sampled for pharyngeal carriage of N. meningitidis during the 2006-2007 school year. A total of 321 isolates from 188 carriers and all 67 invasive disease isolates collected during the same time and from the same geographic region underwent whole-genome sequencing. Core-genome multilocus sequence typing was used to compare allelic profiles, and direct read mapping was used to study strain evolution. Results. Among 188 N. meningitidis culture-positive students, 98 (52.1%) were N. meningitidis culture positive at 2 or 3 samplings. Most students who were positive at >1 sampling (98%) had persistence of a single strain. More than a third of students carried isolates that were highly genetically related to isolates from other students in the same school, and occasional transmission within the same county was also evident. The major pilin subunit gene, pilE, was the most variable gene, and no carrier had identical pilE sequences at different time points. Conclusion. We found strong evidence of local meningococcal transmission at both the school and county levels. Allelic variation within genes encoding bacterial surface structures, particularly pilE, was common.

We describe 2 human cases of infection with a new Neisseria species (putatively N. brasiliensis), 1 of which involved bacteremia. Genomic analyses found that both isolates were distinct strains of the same species, were closely related to N. iguanae, and contained a capsule synthesis operon similar to N. meningitidis.

Multidrug-resistant bacteria pose a serious health threat, especially in hospitals. Horizontal gene transfer (HGT) of mobile genetic elements (MGEs) facilitates the spread of antibiotic resistance, virulence, and environmental persistence genes between nosocomial pathogens. We screened the genomes of 2173 bacterial isolates from healthcare-associated infections from a single hospital over 18 months, and identified identical nucleotide regions in bacteria belonging to distinct genera. To further resolve these shared sequences, we performed long-read sequencing on a subset of isolates and generated highly contiguous genomes. We then tracked the appearance of ten different plasmids in all 2173 genomes, and found evidence of plasmid transfer independent from bacterial transmission. Finally, we identified two instances of likely plasmid transfer within individual patients, including one plasmid that likely transferred to a second patient. This work expands our understanding of HGT in healthcare settings, and can inform efforts to limit the spread of drug-resistant pathogens in hospitals.

Background Vancomycin-resistant enterococci (VRE) are a major cause of hospital-acquired infections. The risk of infection from interventional radiology (IR) procedures is not well documented. Whole-genome sequencing (WGS) surveillance of clinical bacterial isolates among hospitalized patients can identify previously unrecognized outbreaks. Methods We analyzed WGS surveillance data from November 2016 to November 2017 for evidence of VRE transmission. A previously unrecognized cluster of 10 genetically related VRE (Enterococcus faecium) infections was discovered. Electronic health record review identified IR procedures as a potential source. An outbreak investigation was conducted. Results Of the 10 outbreak patients, 9 had undergone an IR procedure with intravenous (IV) contrast ≤22 days before infection. In a matched case-control study, preceding IR procedure and IR procedure with contrast were associated with VRE infection (matched odds ratio [MOR], 16.72; 95% confidence interval [CI], 2.01 to 138.73; P = .009 and MOR, 39.35; 95% CI, 7.85 to infinity; P < .001, respectively). Investigation of IR practices and review of the manufacturer’s training video revealed sterility breaches in contrast preparation. Our investigation also supported possible transmission from an IR technician. Infection prevention interventions were implemented, and no further IR-associated VRE transmissions have been observed. Conclusions A prolonged outbreak of VRE infections related to IR procedures with IV contrast resulted from nonsterile preparation of injectable contrast. The fact that our VRE outbreak was discovered through WGS surveillance and the manufacturer’s training video that demonstrated nonsterile technique raise the possibility that infections following invasive IR procedures may be more common than previously recognized.

BACKGROUND:Traditional methods of outbreak investigations utilize reactive whole genome sequencing (WGS) to confirm or refute the outbreak. We have implemented WGS surveillance and a machine learning (ML) algorithm for the electronic health record (EHR) to retrospectively detect previously unidentified outbreaks and to determine the responsible transmission routes.METHODS:We performed WGS surveillance to identify and characterize clusters of genetically-related Pseudomonas aeruginosa infections during a 24-month period. ML of the EHR was used to identify potential transmission routes. A manual review of the EHR was performed by an infection preventionist to determine the most likely route and results were compared to the ML algorithm.RESULTS:We identified a cluster of 6 genetically related P. aeruginosa cases that occurred during a 7-month period. The ML algorithm identified gastroscopy as a potential transmission route for 4 of the 6 patients. Manual EHR review confirmed gastroscopy as the most likely route for 5 patients. This transmission route was confirmed by identification of a genetically-related P. aeruginosa incidentally cultured from a gastroscope used on 4of the 5 patients. Three infections, 2 of which were blood stream infections, could have been prevented if the ML algorithm had been running in real-time.CONCLUSIONS:WGS surveillance combined with a ML algorithm of the EHR identified a previously undetected outbreak of gastroscope-associated P. aeruginosa infections. These results underscore the value of WGS surveillance and ML of the EHR for enhancing outbreak detection in hospitals and preventing serious infections.