Making genomic surveillance deliver: A lineage classification and nomenclature system to inform rabies elimination.

The availability of pathogen sequence data and use of genomic surveillance is rapidly increasing. Genomic tools and classification systems need updating to reflect this. Here, rabies virus is used as an example to showcase the potential value of updated genomic tools to enhance surveillance to better understand epidemiological dynamics and improve disease control. Previous studies have described the evolutionary history of rabies virus, however the resulting taxonomy lacks the definition necessary to identify incursions, lineage turnover and transmission routes at high resolution. Here we propose a lineage classification system based on the dynamic nomenclature used for SARS-CoV-2, defining a lineage by phylogenetic methods for tracking virus spread and comparing sequences across geographic areas. We demonstrate this system through application to the globally distributed Cosmopolitan clade of rabies virus, defining 96 total lineages within the clade, beyond the 22 previously reported. We further show how integration of this tool with a new rabies virus sequence data resource (RABV-GLUE) enables rapid application, for example, highlighting lineage dynamics relevant to control and elimination programmes, such as identifying importations and their sources, as well as areas of persistence and routes of virus movement, including transboundary incursions. This system and the tools developed should be useful for coordinating and targeting control programmes and monitoring progress as countries work towards eliminating dog-mediated rabies, as well as having potential for broader application to the surveillance of other viruses.

Characteristics Associated With Disparities Among Older Adults in Coronavirus Disease 2019 Outcomes in an Academic Health Care System.

BACKGROUND: An improved understanding of the coronavirus disease 2019 (COVID-19) pandemic is needed to identify predictors of outcomes among older adults with COVID-19. OBJECTIVE: The objective of this study was to examine patient and health system factors predictive of in-hospital mortality, intensive care unit (ICU) admission, and readmission among patients with COVID-19. DESIGN, SETTING, AND PARTICIPANTS: A cohort study of patients aged 18 years and older with COVID-19 discharged from 5 New York hospitals within the Mount Sinai Health System (March 1, 2020-June 30, 2020). MEASURES: Patient-level characteristics (age, sex, race/ethnicity, comorbidities/serious illness, transfer from skilled nursing facility, severe acute respiratory syndrome coronavirus 2 viral load, Sequential Organ Failure Assessment score, treatments); hospital characteristics. OUTCOMES: All-cause in-hospital mortality; ICU admission; 30-day readmission. RESULTS: Among 7556 subjects, mean age 61.1 (62.0) years; 1556 (20.6%) died, 949 (12.6%) had an ICU admission, and 227 (9.1%) had a 30-day readmission. Increased age [aged 55-64: odds ratio (OR), 3.28; 95% confidence interval (CI), 2.41-4.46; aged 65-74: OR, 4.67; 95% CI, 3.43-6.35; aged 75-84: OR, 10.73; 95% CI, 7.77-14.81; aged 85 y and older: OR, 20.57; 95% CI, 14.46-29.25] and comorbidities (OR, 1.11; 95% CI, 1.16, 2.13) were independent risk factors for in-hospital mortality. Yet older adults (aged 55-64 y: OR, 0.56; 95% CI, 0.40-0.77; aged 65-74: OR, 0.46; 95% CI, 0.33-0.65; aged 75-84: OR, 0.27; 95% CI, 0.18-0.40; aged above 85 y: OR, 0.21; 95% CI, 0.13-0.34) and those with Medicaid (OR, 0.74; 95% CI, 0.56-0.99) were less likely to be admitted to the ICU. Race/ethnicity, crowding, population density, and health system census were not associated with study outcomes. CONCLUSIONS: Increased age was the single greatest independent risk factor for mortality. Comorbidities and serious illness were independently associated with mortality. Understanding these risk factors can guide medical decision-making for older adults with COVID-19. Older adults and those admitted from a skilled nursing facility were half as likely to be admitted to the ICU. This finding requires further investigation to understand how age and treatment preferences factored into resource allocation.

Access to Palliative Care Consultation for Hospitalized Adults with COVID-19 in an Urban Health System: Were There Disparities at the Peak of the Pandemic?

Background: Palliative care (PC) services expanded rapidly to meet the needs of coronavirus disease 2019 (COVID-19) patients, yet little is known about which patients were referred for PC consultation during the pandemic. Objective: Examine factors predictive of PC consultation for COVID-19 patients. Design: Retrospective cohort study of COVID-19 patients discharged from four hospitals (March 1-June 30, 2020). Exposures: Patient demographic, socioeconomic, and clinical factors and hospital-level characteristics. Outcome Measurement: Inpatient PC consultation. Results: Of 4319 hospitalized COVID-19 patients, 581 (14%) received PC consultation. Increasing age, serious illness (cancer, chronic obstructive pulmonary disease, and dementia), greater illness severity, and admission to the quaternary hospital were associated with receipt of PC consultation. There was no association between PC consultation and race/ethnicity, household crowding, insurance status, or hospital-factors, including inpatient, emergency department, and intensive care unit census. Conclusions: Although site variation existed, the highest acuity patients were most likely to receive PC consultation without racial/ethnic or socioeconomic disparities.

Computational strategies to combat COVID-19: useful tools to accelerate SARS-CoV-2 and coronavirus research.

SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) is a novel virus of the family Coronaviridae. The virus causes the infectious disease COVID-19. The biology of coronaviruses has been studied for many years. However, bioinformatics tools designed explicitly for SARS-CoV-2 have only recently been developed as a rapid reaction to the need for fast detection, understanding and treatment of COVID-19. To control the ongoing COVID-19 pandemic, it is of utmost importance to get insight into the evolution and pathogenesis of the virus. In this review, we cover bioinformatics workflows and tools for the routine detection of SARS-CoV-2 infection, the reliable analysis of sequencing data, the tracking of the COVID-19 pandemic and evaluation of containment measures, the study of coronavirus evolution, the discovery of potential drug targets and development of therapeutic strategies. For each tool, we briefly describe its use case and how it advances research specifically for SARS-CoV-2. All tools are free to use and available online, either through web applications or public code repositories. Contact:evbc@unj-jena.de.

Rapid feedback on hospital onset SARS-CoV-2 infections combining epidemiological and sequencing data.

Background: Rapid identification and investigation of healthcare-associated infections (HCAIs) is important for suppression of SARS-CoV-2, but the infection source for hospital onset COVID-19 infections (HOCIs) cannot always be readily identified based only on epidemiological data. Viral sequencing data provides additional information regarding potential transmission clusters, but the low mutation rate of SARS-CoV-2 can make interpretation using standard phylogenetic methods difficult. Methods: We developed a novel statistical method and sequence reporting tool (SRT) that combines epidemiological and sequence data in order to provide a rapid assessment of the probability of HCAI among HOCI cases (defined as first positive test >48 hr following admission) and to identify infections that could plausibly constitute outbreak events. The method is designed for prospective use, but was validated using retrospective datasets from hospitals in Glasgow and Sheffield collected February-May 2020. Results: We analysed data from 326 HOCIs. Among HOCIs with time from admission ≥8 days, the SRT algorithm identified close sequence matches from the same ward for 160/244 (65.6%) and in the remainder 68/84 (81.0%) had at least one similar sequence elsewhere in the hospital, resulting in high estimated probabilities of within-ward and within-hospital transmission. For HOCIs with time from admission 3-7 days, the SRT probability of healthcare acquisition was >0.5 in 33/82 (40.2%). Conclusions: The methodology developed can provide rapid feedback on HOCIs that could be useful for infection prevention and control teams, and warrants further prospective evaluation. The integration of epidemiological and sequence data is important given the low mutation rate of SARS-CoV-2 and its variable incubation period. Funding: COG-UK HOCI funded by COG-UK consortium, supported by funding from UK Research and Innovation, National Institute of Health Research and Wellcome Sanger Institute.

Natural selection in the evolution of SARS-CoV-2 in bats created a generalist virus and highly capable human pathogen.

Virus host shifts are generally associated with novel adaptations to exploit the cells of the new host species optimally. Surprisingly, Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has apparently required little to no significant adaptation to humans since the start of the Coronavirus Disease 2019 (COVID-19) pandemic and to October 2020. Here we assess the types of natural selection taking place in Sarbecoviruses in horseshoe bats versus the early SARS-CoV-2 evolution in humans. While there is moderate evidence of diversifying positive selection in SARS-CoV-2 in humans, it is limited to the early phase of the pandemic, and purifying selection is much weaker in SARS-CoV-2 than in related bat Sarbecoviruses. In contrast, our analysis detects evidence for significant positive episodic diversifying selection acting at the base of the bat virus lineage SARS-CoV-2 emerged from, accompanied by an adaptive depletion in CpG composition presumed to be linked to the action of antiviral mechanisms in these ancestral bat hosts. The closest bat virus to SARS-CoV-2, RmYN02 (sharing an ancestor about 1976), is a recombinant with a structure that includes differential CpG content in Spike; clear evidence of coinfection and evolution in bats without involvement of other species. While an undiscovered "facilitating" intermediate species cannot be discounted, collectively, our results support the progenitor of SARS-CoV-2 being capable of efficient human-human transmission as a consequence of its adaptive evolutionary history in bats, not humans, which created a relatively generalist virus.

Genomic epidemiology reveals multiple introductions of SARS-CoV-2 from mainland Europe into Scotland.

Coronavirus disease 2019 (COVID-19) was first diagnosed in Scotland on 1 March 2020. During the first month of the outbreak, 2,641 cases of COVID-19 led to 1,832 hospital admissions, 207 intensive care admissions and 126 deaths. We aimed to identify the source and number of introductions of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) into Scotland using a combined phylogenetic and epidemiological approach. Sequencing of 1,314 SARS-CoV-2 viral genomes from available patient samples enabled us to estimate that SARS-CoV-2 was introduced to Scotland on at least 283 occasions during February and March 2020. Epidemiological analysis confirmed that early introductions of SARS-CoV-2 originated from mainland Europe (the majority from Italy and Spain). We identified subsequent early outbreaks in the community, within healthcare facilities and at an international conference. Community transmission occurred after 2 March, 3 weeks before control measures were introduced. Earlier travel restrictions or quarantine measures, both locally and internationally, would have reduced the number of COVID-19 cases in Scotland. The risk of multiple reintroduction events in future waves of infection remains high in the absence of population immunity.

No evidence for distinct types in the evolution of SARS-CoV-2.

A recent study by Tang et al. claimed that two major types of severe acute respiratory syndrome-coronavirus-2 (CoV-2) had evolved in the ongoing CoV disease-2019 pandemic and that one of these types was more 'aggressive' than the other. Given the repercussions of these claims and the intense media coverage of these types of articles, we have examined in detail the data presented by Tang et al., and show that the major conclusions of that paper cannot be substantiated. Using examples from other viral outbreaks, we discuss the difficulty in demonstrating the existence or nature of a functional effect of a viral mutation, and we advise against overinterpretation of genomic data during the pandemic.