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Identification of SARS-CoV-2 Variants in Indoor Dust

Overview
Journal PLoS One
Date 2024 Feb 9
PMID 38335205
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Abstract

Environmental surveillance of pathogens underlying infectious disease is critical to ensure public health. Recent efforts to track SARS-CoV-2 have utilized wastewater sampling to infer community trends in viral abundance and variant composition. Indoor dust has also been used for building-level inferences, though to date no sequencing data providing variant-scale resolution have been reported from dust samples, and strategies to monitor circulating variants in dust are needed to help inform public health decisions. In this study, we demonstrate that SARS-CoV-2 lineages can be detected and sequenced from indoor bulk dust samples. We collected 93 vacuum bags from April 2021 to March 2022 from buildings on The Ohio State University's (OSU) Columbus campus, and the dust was used to develop and apply an amplicon-based whole-genome sequencing protocol to identify the variants present and estimate their relative abundances. Three variants of concern were detected in the dust: Alpha, Delta, and Omicron. Alpha was found in our earliest sample in April 2021 with an estimated frequency of 100%. Delta was the primary variant present from October of 2021 to January 2022, with an average estimated frequency of 91% (±1.3%). Omicron became the primary variant in January 2022 and was the dominant strain in circulation through March with an estimated frequency of 87% (±3.2%). The detection of these variants on OSU's campus correlates with the circulation of these variants in the surrounding population (Delta p<0.0001 and Omicron p = 0.02). Overall, these results support the hypothesis that dust can be used to track COVID-19 variants in buildings.

Citing Articles

SARS-CoV-2 RNA Detection in Wastewater and Its Effective Correlation with Clinical Data during the Outbreak of COVID-19 in Salamanca.

Martinez de Alba A, Moran-Diez M, Garcia-Prieto J, Diego J, Fernandez-Soto P, Serrano Leon E Int J Mol Sci. 2024; 25(15).

PMID: 39125640 PMC: 11311535. DOI: 10.3390/ijms25158071.

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