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      SARS-CoV-2 Distribution in Residential Housing Suggests Contact Deposition and Correlates with Rothia sp.

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      medRxiv
      Cold Spring Harbor Laboratory

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          Abstract

          Monitoring severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) on surfaces is emerging as an important tool for identifying past exposure to individuals shedding viral RNA. Our past work has demonstrated that SARS-CoV-2 reverse transcription-quantitative PCR (RT-qPCR) signals from surfaces can identify when infected individuals have touched surfaces such as Halloween candy, and when they have been present in hospital rooms or schools. However, the sensitivity and specificity of surface sampling as a method for detecting the presence of a SARS-CoV-2 positive individual, as well as guidance about where to sample, has not been established. To address these questions, and to test whether our past observations linking SARS-CoV-2 abundance to Rothia spp. in hospitals also hold in a residential setting, we performed detailed spatial sampling of three isolation housing units, assessing each sample for SARS-CoV-2 abundance by RT-qPCR, linking the results to 16S rRNA gene amplicon sequences to assess the bacterial community at each location and to the Cq value of the contemporaneous clinical test. Our results show that the highest SARS-CoV-2 load in this setting is on touched surfaces such as light switches and faucets, but detectable signal is present in many non-touched surfaces that may be more relevant in settings such as schools where mask wearing is enforced. As in past studies, the bacterial community predicts which samples are positive for SARS-CoV-2, with Rothia sp. showing a positive association.

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          Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2

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            Matplotlib: A 2D Graphics Environment

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              Deblur Rapidly Resolves Single-Nucleotide Community Sequence Patterns

              Deblur provides a rapid and sensitive means to assess ecological patterns driven by differentiation of closely related taxa. This algorithm provides a solution to the problem of identifying real ecological differences between taxa whose amplicons differ by a single base pair, is applicable in an automated fashion to large-scale sequencing data sets, and can integrate sequencing runs collected over time.
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                Author and article information

                Journal
                medRxiv
                MEDRXIV
                medRxiv
                Cold Spring Harbor Laboratory
                07 December 2021
                : 2021.12.06.21267101
                Affiliations
                [1. ]These authors contributed equally
                [2. ]Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA
                [3. ]Department of Pediatrics, University of California San Diego, La Jolla, CA
                [4. ]Bioinformatics and Systems Biology Graduate Program, University of California San Diego, La Jolla, CA
                [5. ]Department of Obstetrics, Gynecology, and Reproductive Sciences, University of California San Diego, USA
                [6. ]Division of Infectious Diseases and Global Public Health, Department of Medicine; University of California San Diego School of Medicine, 9500 Gilman Drive, La Jolla, California 92093, USA
                [7. ]Sanford Consortium of Regenerative Medicine, University of California San Diego, La Jolla, CA
                [8. ]Expedited COVID Identification Environment (EXCITE) Laboratory, Department of Pediatrics, University of California San Diego, La Jolla, CA
                [9. ]Herbert Wertheim School of Public Health, University of California, San Diego 9500 Gilman Drive, La Jolla, CA 92093
                [10. ]Rady Children’s Hospital, San Diego, CA.
                [11. ]Department of Obstetrics, Gynecology, and Reproductive Sciences, University of California San Diego, USA
                [12 ]Dept of Cellular and Molecular Medicine, University of California San Diego, La Jolla, CA
                [13 ]San Diego State University, San Diego, CA
                [14 ]Department of Computer Science and Engineering, University of California San Diego, La Jolla, CA, USA
                [15. ]Center for Microbiome Innovation, Jacobs School of Engineering, University of California San Diego, La Jolla, CA, USA
                Author notes
                [16. ]Co-corresponding authors
                Article
                10.1101/2021.12.06.21267101
                8669860
                34909793
                557045bc-1715-48fe-a891-9ef4169d4ef8

                This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which allows reusers to copy and distribute the material in any medium or format in unadapted form only, for noncommercial purposes only, and only so long as attribution is given to the creator.

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