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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.
Afiliación
  • 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 en En | MEDLINE | ID: mdl-35313580
ABSTRACT
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 Base de datos: MEDLINE Tipo de estudio: Prognostic_studies / Risk_factors_studies / Screening_studies Idioma: En Revista: MedRxiv Año: 2022 Tipo del documento: Article

Texto completo: 1 Base de datos: MEDLINE Tipo de estudio: Prognostic_studies / Risk_factors_studies / Screening_studies Idioma: En Revista: MedRxiv Año: 2022 Tipo del documento: Article