COVID-19 Prediction using Genomic Footprint of SARS-CoV-2 in Air, Surface Swab and Wastewater Samples.

Autor:	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 Jr; Facilities and Operations, University of Miami; Coral Gables FL., Kobetz E; Department of Medicine, Miller School of Medicine, University of Miami; Miami FL.; Sylvester Comprehensive Cancer Center, Miller School of Medicine, University of Miami; Miami FL., Solle NS; Department of Medicine, Miller School of Medicine, University of Miami; Miami FL.; 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; Department of Housing & Residential Life, University of Miami; Coral Gables FL., Mason CE; Department of Physiology and Biophysics, Weill Cornell Medical College; New York City NY., Schürer SC; Sylvester Comprehensive Cancer Center, Miller School of Medicine, University of Miami; Miami FL.; Institute for Data Science & Computing, University of Miami; Coral Gables FL.; Department of Molecular & Cellular Pharmacology, Miller School of Medicine, University of Miami; Miami FL., Vidovic D; Department of Molecular & Cellular Pharmacology, Miller School of Medicine, University of Miami; Miami FL., Williams SL; Sylvester Comprehensive Cancer Center, Miller School of Medicine, University of Miami; Miami FL., Grills GS; Sylvester Comprehensive Cancer Center, Miller School of Medicine, University of Miami; Miami FL., Jayaweera DT; Department of Medicine, Miller School of Medicine, University of Miami; Miami FL., Mirsaeidi M; Division of Pulmonary, Critical Care and Sleep, College of Medicine-Jacksonville University of Florida, Jacksonville FL., Kumar N; Department of Public Health Sciences, Miller School of Medicine, University of Miami; Miami FL 33136.
Jazyk:	angličtina
Zdroj:	MedRxiv : the preprint server for health sciences [medRxiv] 2022 Apr 01. Date of Electronic Publication: 2022 Apr 01.
DOI:	10.1101/2022.03.14.22272314
Abstrakt:	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.
Databáze:	MEDLINE
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