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Background: Drivers of COVID-19 severity are multifactorial and include multidimensional and potentially interacting factors encompassing viral determinants and host-related factors (i.e., demographics, pre-existing conditions and/or genetics), thus complicating the prediction of clinical outcomes for different severe acute respiratory syndrome coronavirus (SARS-CoV-2) variants. Although millions of SARS-CoV-2 genomes have been publicly shared in global databases, linkages with detailed clinical data are scarce. Therefore, we aimed to establish a COVID-19 patient dataset with linked clinical and viral genomic data to then examine associations between SARS-CoV-2 genomic signatures and clinical disease phenotypes., Methods: A cohort of adult patients with laboratory confirmed SARS-CoV-2 from 11 participating healthcare institutions in the Greater Toronto Area (GTA) were recruited from March 2020 to April 2022. Supervised machine learning (ML) models were developed to predict hospitalization using SARS-CoV-2 lineage-specific genomic signatures, patient demographics, symptoms, and pre-existing comorbidities. The relative importance of these features was then evaluated., Results: Complete clinical data and viral whole genome level information were obtained from 617 patients, 50.4% of whom were hospitalized. Notably, inpatients were older with a mean age of 66.67 years (SD ± 17.64 years), whereas outpatients had a mean age of 44.89 years (SD ± 16.00 years). SHapley Additive exPlanations (SHAP) analyses revealed that underlying vascular disease, underlying pulmonary disease, and fever were the most significant clinical features associated with hospitalization. In models built on the amino acid sequences of functional regions including spike, nucleocapsid, ORF3a, and ORF8 proteins, variants preceding the emergence of variants of concern (VOCs) or pre-VOC variants, were associated with hospitalization., Conclusions: Viral genomic features have limited utility in predicting hospitalization across SARS-CoV-2 diversity. Combining clinical and viral genomic datasets provides perspective on patient specific and virus-related factors that impact COVID-19 disease severity. Overall, clinical features had greater discriminatory power than viral genomic features in predicting hospitalization., Competing Interests: Declarations. Ethics approval and consent to participate: This study was approved by the ethics committee of all participating TIBDN hospitals (REB# 02-0118-U/05/0016-C) and Sunnybrook Health Sciences Centre’s ethics board (REB# 5533). All participants provided informed consent to partake in this study. All research was conducted in accordance with the second edition of the Tri-Council Policy Statement: Ethical Conduct for Research Involving Humans standard. Consent for publication: Not applicable. Competing interests: The authors declare no competing interests., (© 2025. The Author(s).)

Background: We determined the burden of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in air and on surfaces in rooms of patients hospitalized with coronavirus disease 2019 (COVID-19) and investigated patient characteristics associated with SARS-CoV-2 environmental contamination., Methods: Nasopharyngeal swabs, surface, and air samples were collected from the rooms of 78 inpatients with COVID-19 at 6 acute care hospitals in Toronto from March to May 2020. Samples were tested for SARS-CoV-2 ribonucleic acid (RNA), cultured to determine potential infectivity, and whole viral genomes were sequenced. Association between patient factors and detection of SARS-CoV-2 RNA in surface samples were investigated., Results: Severe acute respiratory syndrome coronavirus 2 RNA was detected from surfaces (125 of 474 samples; 42 of 78 patients) and air (3 of 146 samples; 3 of 45 patients); 17% (6 of 36) of surface samples from 3 patients yielded viable virus. Viral sequences from nasopharyngeal and surface samples clustered by patient. Multivariable analysis indicated hypoxia at admission, polymerase chain reaction-positive nasopharyngeal swab (cycle threshold of ≤30) on or after surface sampling date, higher Charlson comorbidity score, and shorter time from onset of illness to sampling date were significantly associated with detection of SARS-CoV-2 RNA in surface samples., Conclusions: The infrequent recovery of infectious SARS-CoV-2 virus from the environment suggests that the risk to healthcare workers from air and near-patient surfaces in acute care hospital wards is likely limited., (© The Author(s) 2021. Published by Oxford University Press for the Infectious Diseases Society of America. All rights reserved. For permissions, e-mail: journals.permissions@oup.com.)

The emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) from wildlife has raised concerns about spillover from humans to animals, the establishment of novel wildlife reservoirs, and the potential for future outbreaks caused by variants of wildlife origin. Norway rats (Rattus norvegicus) are abundant in urban areas and live in close proximity to humans, providing the opportunity for spillover of SARS-CoV-2. Evidence of SARS-CoV-2 infection and exposure has been reported in Norway rats. We investigated SARS-CoV-2 infection and exposure in Norway rats from Southern Ontario, Canada. From October 2019 to June 2021, 224 rats were submitted by collaborating pest control companies. The majority of samples were collected in Windsor (79.9%; n = 179), Hamilton (13.8%; n = 31), and the Greater Toronto Area (5.8%; n = 13). Overall, 50.0% (n = 112) were female and most rats were sexually mature (55.8%; n = 125). Notably, 202 samples were collected prior to the emergence of variants of concern (VOC) and 22 were collected while the Alpha variant (B.1.1.7) was the predominant circulating VOC in humans. Nasal turbinate (n = 164) and small intestinal (n = 213) tissue samples were analyzed for SARS-CoV-2 RNA by RT-PCR. Thoracic cavity fluid samples (n = 213) were tested for neutralizing antibodies using a surrogate virus neutralization test (sVNT) (GenScript cPass); confirmatory plaque reduction neutralization test (PRNT) was conducted on presumptive positive samples. We did not detect SARS-CoV-2 RNA in any samples tested. Two out of eleven samples positive on sVNT had neutralizing antibodies confirmed positive by PRNT (1 : 40 and 1 : 320 PRNT70); both were collected prior to the emergence of VOC. It is imperative that efforts to control and monitor SARS-CoV-2 include surveillance of rats and other relevant wildlife species as novel variants continue to emerge. [ABSTRACT FROM AUTHOR]

Background: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus responsible for the coronavirus disease 2019 pandemic, is capable of infecting a variety of wildlife species. Wildlife living in close contact with humans are at an increased risk of SARSCoV- 2 exposure and, if infected, have the potential to become a reservoir for the pathogen, making control and management more difficult. The objective of this study is to conduct SARSCoV- 2 surveillance in urban wildlife from Ontario and Québec, increasing our knowledge of the epidemiology of the virus and our chances of detecting spillover from humans into wildlife. Methods: Using a One Health approach, we leveraged activities of existing research, surveillance and rehabilitation programs among multiple agencies to collect samples from 776 animals from 17 different wildlife species between June 2020 and May 2021. Samples from all animals were tested for the presence of SARS-CoV-2 viral ribonucleic acid, and a subset of samples from 219 animals across three species (raccoons, Procyon lotor; striped skunks, Mephitis mephitis; and mink, Neovison vison) were also tested for the presence of neutralizing antibodies. Results: No evidence of SARS-CoV-2 viral ribonucleic acid or neutralizing antibodies was detected in any of the tested samples. Conclusion: Although we were unable to identify positive SARS-CoV-2 cases in wildlife, continued research and surveillance activities are critical to better understand the rapidly changing landscape of susceptible animal species. Collaboration between academic, public and animal health sectors should include experts from relevant fields to build coordinated surveillance and response capacity. [ABSTRACT FROM AUTHOR]