Evidence map›Paper›PMID 36898549›Full record

ArticleThe Science of the total environment2023

The city-wide full-scale interactive application of sewage surveillance programme for assisting real-time COVID-19 pandemic control - A case study in Hong Kong.

Wai-Yin Ng, Wai Thoe, Rong Yang, Wai-Ping Cheung, Che-Kong Chen, King-Ho To, Kan-Ming Pak, Hon-Wan Leung, Wai-Kwan Lai, Tsz-Kin Wong and 15 more

Abstract read
In one paragraph

Article in The Science of the total environment, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

13 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

25 authors.

Wai-Yin NgEnvironmental Protection Department, Hong Kong SAR Government, China.
Wai ThoeEnvironmental Protection Department, Hong Kong SAR Government, China.
Rong YangEnvironmental Protection Department, Hong Kong SAR Government, China.
Wai-Ping CheungEnvironmental Protection Department, Hong Kong SAR Government, China.
Che-Kong ChenEnvironmental Protection Department, Hong Kong SAR Government, China.
King-Ho ToEnvironmental Protection Department, Hong Kong SAR Government, China.
Kan-Ming PakDrainage Service Department, Hong Kong SAR Government, China.
Hon-Wan LeungDrainage Service Department, Hong Kong SAR Government, China.
Wai-Kwan LaiDrainage Service Department, Hong Kong SAR Government, China.
Tsz-Kin WongDrainage Service Department, Hong Kong SAR Government, China.
Tat-Kwong LauDrainage Service Department, Hong Kong SAR Government, China.
Ka-Wing AuCentre for Health Protection, Department of Health, Hong Kong SAR Government, China.
Xiao-Qing XuDepartment of Civil Engineering, The University of Hong Kong, China.
Xia-Wan ZhengDepartment of Civil Engineering, The University of Hong Kong, China.
Yu DengDepartment of Civil Engineering, The University of Hong Kong, China.
Yan-Kin LauCMA Industrial Development Foundation Limited, Hong Kong, China.
Chi-Kai ToCMA Industrial Development Foundation Limited, Hong Kong, China.
Malik PeirisSchool of Public Health, The University of Hong Kong, China.
Gabriel M LeungSchool of Public Health, The University of Hong Kong, China.
Tong ZhangDepartment of Civil Engineering, The University of Hong Kong, China.
Min YangKey Laboratory of Drinking Water Science and Technology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China.
Wei AnKey Laboratory of Drinking Water Science and Technology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China.
Wenxiu ChenKey Laboratory of Drinking Water Science and Technology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China.
Chen WangKey Laboratory of Drinking Water Science and Technology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China.
Ho-Kwong ChuiEnvironmental Protection Department, Hong Kong SAR Government, China; Hong Kong University of Science and Technology, China. Electronic address: samuel_hk_chui@epd.gov.hk.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The paper discusses the implementation of Hong Kong's tailor-made sewage surveillance programme led by the Government, which has demonstrated how an efficient and well-organized sewage surveillance system can complement conventional epidemiological surveillance to facilitate the planning of intervention strategies and actions for combating COVID-19 pandemic in real-time. This included the setting up of a comprehensive sewerage network-based SARS-CoV-2 virus surveillance programme with 154 stationary sites covering 6 million people (or 80 % of the total population), and employing an intensive monitoring programme to take samples from each stationary site every 2 days. From 1 January to 22 May 2022, the daily confirmed case count started with 17 cases per day on 1 January to a maximum of 76,991 cases on 3 March and dropped to 237 cases on 22 May. During this period, a total of 270 "Restriction-Testing Declaration" (RTD) operations at high-risk residential areas were conducted based on the sewage virus testing results, where over 26,500 confirmed cases were detected with a majority being asymptomatic. In addition, Compulsory Testing Notices (CTN) were issued to residents, and the distribution of Rapid Antigen Test kits was adopted as alternatives to RTD operations in areas of moderate risk. These measures formulated a tiered and cost-effective approach to combat the disease in the local setting. Some ongoing and future enhancement efforts to improve efficacy are discussed from the perspective of wastewater-based epidemiology. Forecast models on case counts based on sewage virus testing results were also developed with R

Indexed as

COVID-19Hong KongHumansPandemicsSARS-CoV-2SewageWastewater-Based Epidemiological MonitoringSewageCity-wide sewage surveillanceCommunity prevalence rateCOVID-19Epidemic forecast modelGovernment intervention measures

Identifiers

PMID36898549
PMCPMC9991928

What OpenQuestion holds

Textmetadata
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.