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COVID-19 Prediction using Genomic Footprint of SARS-CoV-2 in Air, Surface Swab and Wastewater Samples.
Solo-Gabriele, Helena M; Kumar, Shelja; Abelson, Samantha; Penso, Johnathon; Contreras, Julio; Babler, Kristina M; Sharkey, Mark E; Mantero, Alejandro M A; Lamar, Walter E; Tallon, John J; Kobetz, Erin; Solle, Natasha Schaefer; Shukla, Bhavarth S; Kenney, Richard J; Mason, Christopher E; Schürer, Stephan C; Vidovic, Dusica; Williams, Sion L; Grills, George S; Jayaweera, Dushyantha T; Mirsaeidi, Mehdi; Kumar, Naresh.
Afiliação
  • Solo-Gabriele HM; Department of Chemical, Environmental, and Materials Engineering, College of Engineering, University of Miami; Coral Gables FL.
  • Kumar S; Department of Public Health Sciences, Miller School of Medicine, University of Miami; Miami FL 33136.
  • Abelson S; Department of Public Health Sciences, Miller School of Medicine, University of Miami; Miami FL 33136.
  • Penso J; Department of Public Health Sciences, Miller School of Medicine, University of Miami; Miami FL 33136.
  • Contreras J; Department of Public Health Sciences, Miller School of Medicine, University of Miami; Miami FL 33136.
  • Babler KM; Department of Chemical, Environmental, and Materials Engineering, College of Engineering, University of Miami; Coral Gables FL.
  • Sharkey ME; Department of Medicine, Miller School of Medicine, University of Miami; Miami FL.
  • Mantero AMA; Department of Public Health Sciences, Miller School of Medicine, University of Miami; Miami FL 33136.
  • Lamar WE; Facilities Safety & Compliance, Miller School of Medicine, University of Miami; Miami FL.
  • Tallon JJ; Facilities and Operations, University of Miami; Coral Gables FL.
  • Kobetz E; Department of Medicine, Miller School of Medicine, University of Miami; Miami FL.
  • Solle NS; Sylvester Comprehensive Cancer Center, Miller School of Medicine, University of Miami; Miami FL.
  • Shukla BS; Department of Medicine, Miller School of Medicine, University of Miami; Miami FL.
  • Kenney RJ; Sylvester Comprehensive Cancer Center, Miller School of Medicine, University of Miami; Miami FL.
  • Mason CE; Department of Medicine, Miller School of Medicine, University of Miami; Miami FL.
  • Schürer SC; Department of Housing & Residential Life, University of Miami; Coral Gables FL.
  • Vidovic D; Department of Physiology and Biophysics, Weill Cornell Medical College; New York City NY.
  • Williams SL; Sylvester Comprehensive Cancer Center, Miller School of Medicine, University of Miami; Miami FL.
  • Grills GS; Institute for Data Science & Computing, University of Miami; Coral Gables FL.
  • Jayaweera DT; Department of Molecular & Cellular Pharmacology, Miller School of Medicine, University of Miami; Miami FL.
  • Mirsaeidi M; Department of Molecular & Cellular Pharmacology, Miller School of Medicine, University of Miami; Miami FL.
  • Kumar N; Sylvester Comprehensive Cancer Center, Miller School of Medicine, University of Miami; Miami FL.
medRxiv ; 2022 Apr 01.
Article em En | MEDLINE | ID: mdl-35313580
Importance: Genomic footprints of pathogens shed by infected individuals can be traced in environmental samples. Analysis of these samples can be employed for noninvasive surveillance of infectious diseases. Objective: To evaluate the efficacy of environmental surveillance of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) for predicting COVID-19 cases in a college dormitory. Design: Using a prospective experimental design, air, surface swabs, and wastewater samples were collected from a college dormitory from March to May 2021. Students were randomly screened for COVID-19 during the study period. SARS-CoV-2 in environmental samples was concentrated with electronegative filtration and quantified using Volcano 2 nd Generation-qPCR. Descriptive analyses were conducted to examine the associations between time-lagged SARS-CoV-2 in environmental samples and clinically diagnosed COVID-19 cases. Setting: This study was conducted in a residential dormitory at the University of Miami, Coral Gables campus, FL, USA. The dormitory housed about 500 students. Participants: Students from the dormitory were randomly screened, for COVID-19 for 2-3 days / week while entering or exiting the dormitory. Main Outcome: Clinically diagnosed COVID-19 cases were of our main interest. We hypothesized that SARS-CoV-2 detection in environmental samples was an indicator of the presence of local COVID-19 cases in the dormitory, and SARS-CoV-2 can be detected in the environmental samples several days prior to the clinical diagnosis of COVID-19 cases. Results: SARS-CoV-2 genomic footprints were detected in air, surface swab and wastewater samples on 52 (63.4%), 40 (50.0%) and 57 (68.6%) days, respectively, during the study period. On 19 (24%) of 78 days SARS-CoV-2 was detected in all three sample types. Clinically diagnosed COVID-19 cases were reported on 11 days during the study period and SARS-CoV-2 was also detected two days before the case diagnosis on all 11 (100%), 9 (81.8%) and 8 (72.7%) days in air, surface swab and wastewater samples, respectively. Conclusion: Proactive environmental surveillance of SARS-CoV-2 or other pathogens in a community/public setting has potential to guide targeted measures to contain and/or mitigate infectious disease outbreaks. Key Points: Question: How effective is environmental surveillance of SARS-CoV-2 in public places for early detection of COVID-19 cases in a community?Findings: All clinically confirmed COVID-19 cases were predicted with the aid of 2 day lagged SARS-CoV-2 in environmental samples in a college dormitory. However, the prediction efficiency varied by sample type: best prediction by air samples, followed by wastewater and surface swab samples. SARS-CoV-2 was also detected in these samples even on days without any reported cases of COVID-19, suggesting underreporting of COVID-19 cases.Meaning: SARS-CoV-2 can be detected in environmental samples several days prior to clinical reporting of COVID-19 cases. Thus, proactive environmental surveillance of microbiome in public places can serve as a mean for early detection of location-time specific outbreaks of infectious diseases. It can also be used for underreporting of infectious diseases.

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies / Risk_factors_studies / Screening_studies Idioma: En Revista: MedRxiv Ano de publicação: 2022 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies / Risk_factors_studies / Screening_studies Idioma: En Revista: MedRxiv Ano de publicação: 2022 Tipo de documento: Article