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Lancet Reg Health Am ; 19: 100449, 2023 Mar.
Article in English | MEDLINE | ID: covidwho-2240692


Background: Schools are high-risk settings for SARS-CoV-2 transmission, but necessary for children's educational and social-emotional wellbeing. Previous research suggests that wastewater monitoring can detect SARS-CoV-2 infections in controlled residential settings with high levels of accuracy. However, its effective accuracy, cost, and feasibility in non-residential community settings is unknown. Methods: The objective of this study was to determine the effectiveness and accuracy of community-based passive wastewater and surface (environmental) surveillance to detect SARS-CoV-2 infection in neighborhood schools compared to weekly diagnostic (PCR) testing. We implemented an environmental surveillance system in nine elementary schools with 1700 regularly present staff and students in southern California. The system was validated from November 2020 to March 2021. Findings: In 447 data collection days across the nine sites 89 individuals tested positive for COVID-19, and SARS-CoV-2 was detected in 374 surface samples and 133 wastewater samples. Ninety-three percent of identified cases were associated with an environmental sample (95% CI: 88%-98%); 67% were associated with a positive wastewater sample (95% CI: 57%-77%), and 40% were associated with a positive surface sample (95% CI: 29%-52%). The techniques we utilized allowed for near-complete genomic sequencing of wastewater and surface samples. Interpretation: Passive environmental surveillance can detect the presence of COVID-19 cases in non-residential community school settings with a high degree of accuracy. Funding: County of San Diego, Health and Human Services Agency, National Institutes of Health, National Science Foundation, Centers for Disease Control.

Sci Rep ; 12(1): 5077, 2022 03 24.
Article in English | MEDLINE | ID: covidwho-1815587


Throughout the COVID-19 pandemic, massive sequencing and data sharing efforts enabled the real-time surveillance of novel SARS-CoV-2 strains throughout the world, the results of which provided public health officials with actionable information to prevent the spread of the virus. However, with great sequencing comes great computation, and while cloud computing platforms bring high-performance computing directly into the hands of all who seek it, optimal design and configuration of a cloud compute cluster requires significant system administration expertise. We developed ViReflow, a user-friendly viral consensus sequence reconstruction pipeline enabling rapid analysis of viral sequence datasets leveraging Amazon Web Services (AWS) cloud compute resources and the Reflow system. ViReflow was developed specifically in response to the COVID-19 pandemic, but it is general to any viral pathogen. Importantly, when utilized with sufficient compute resources, ViReflow can trim, map, call variants, and call consensus sequences from amplicon sequence data from 1000 SARS-CoV-2 samples at 1000X depth in < 10 min, with no user intervention. ViReflow's simplicity, flexibility, and scalability make it an ideal tool for viral molecular epidemiological efforts.

COVID-19 , Software , COVID-19/epidemiology , Genome, Viral/genetics , Humans , Pandemics , SARS-CoV-2/genetics
BMC Med Genomics ; 14(1): 138, 2021 05 24.
Article in English | MEDLINE | ID: covidwho-1241103


BACKGROUND: Older aged adults and those with pre-existing conditions are at highest risk for severe COVID-19 associated outcomes. METHODS: Using a large dataset of genome-wide RNA-seq profiles derived from human dermal fibroblasts (GSE113957) we investigated whether age affects the expression of pattern recognition receptor (PRR) genes and ACE2, the receptor for SARS-CoV-2. RESULTS: Extremes of age are associated with increased expression of selected PRR genes, ACE2 and four genes that encode proteins that have been shown to interact with SAR2-CoV-2 proteins. CONCLUSIONS: Assessment of PRR expression might provide a strategy for stratifying the risk of severe COVID-19 disease at both the individual and population levels.

COVID-19/genetics , COVID-19/virology , Gene Expression Regulation , Peptidyl-Dipeptidase A/genetics , Receptors, Pattern Recognition/genetics , Receptors, Virus/genetics , SARS-CoV-2/metabolism , Adolescent , Adult , Age Factors , Aged , Aged, 80 and over , Child , Dermis/pathology , Fibroblasts/metabolism , Gene Expression Profiling , Humans , Middle Aged , RNA-Seq , Receptors, Virus/metabolism , Young Adult