Strengthening sewage surveillance for SARS-CoV-2
Description: Objective To strengthen the sewage surveillance for SARS-CoV-2 virus for monitoring the spread and progression of COVID-19 in Hong Kong in the post-vaccination era. Hypothesis to be tested Sewage surveillance of SARS-CoV-2 can provide quantitative detection of SARS-CoV-2 variants in sewage. Design and subjects Quantitative detection method for SARS-CoV-2 variants in sewage will be developed. The method will be applied to detect clades and new mutations in local communities and estimate their abundance for a period of 28 months. Study instruments Quantitative Polymerase Chain Reaction instrument for Reverse transcription qPCR; nextgeneration sequencing platforms. Interventions Method sensitivity is the key issue for sequencing the SARS-CoV-2 variants of concern (VOC) in sewages due to the low viral load compared with clinical specimens. Main outcome measures We will develop rapid and sensitive methods for detecting and quantifying the SARSCoV- 2 variants in sewage. Both short-read and long-read sequencing technologies will be adopted for sequencing the viral genome. Data Analysis Absolute concentrations of detected SARS-CoV-2 variants in sewage samples will be normalized to surrogate organisms to monitor the spread of SARS-CoV-2 in the communities. Genome of SARS-CoV-2 variants of concern in positive sewage samples will be sequenced. Relative abundances of different variants in sewage will be quantified. Expected Results • A fast and sensitive sewage testing method for SARS-CoV-2 • Methods for detecting SARS-CoV-2 VOC in sewage by genomic sequencing and RT-qPCR. • Longitudinal detection and sequencing of SARS-CoV-2 in local sewage
Funder: Health and Medical Research Fund
Amount: HKD 9,092,000
Funding type: grant
Funder scheme: Commissioned Research on the Novel Coronavirus Disease (COVID-19)
Investigator: Tong ZHANG (lead)
Institutions: University of Hong Kong, Department of Civil Engineering, The University of Hong Kong
Funded outputs count: 9
Funder award ID: COVID1903015
OpenAlex ID: G6182808413