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Liu Y, Yin Y, Ward MP, Li K, Chen Y, Duan M, Wong PPY, Hong J, Huang J, Shi J, Zhou X, Chen X, Xu J, Yuan R, Kong L, Zhang Z. Optimization of Screening Strategies for COVID-19: Scoping Review. JMIR Public Health Surveill 2024; 10:e44349. [PMID: 38412011 PMCID: PMC10933748 DOI: 10.2196/44349] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Revised: 06/29/2023] [Accepted: 11/21/2023] [Indexed: 02/28/2024] Open
Abstract
BACKGROUND COVID-19 screening is an effective nonpharmaceutical intervention for identifying infected individuals and interrupting viral transmission. However, questions have been raised regarding its effectiveness in controlling the spread of novel variants and its high socioeconomic costs. Therefore, the optimization of COVID-19 screening strategies has attracted great attention. OBJECTIVE This review aims to summarize the evidence and provide a reference basis for the optimization of screening strategies for the prevention and control of COVID-19. METHODS We applied a methodological framework for scoping reviews and the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) checklist. We conducted a scoping review of the present publications on the optimization of COVID-19 screening strategies. We searched the PubMed, Web of Science, and Elsevier ScienceDirect databases for publications up to December 31, 2022. English publications related to screening and testing strategies for COVID-19 were included. A data-charting form, jointly developed by 2 reviewers, was used for data extraction according to the optimization directions of the screening strategies. RESULTS A total of 2770 unique publications were retrieved from the database search, and 95 abstracts were retained for full-text review. There were 62 studies included in the final review. We summarized the results in 4 major aspects: the screening population (people at various risk conditions such as different regions and occupations; 12/62, 19%), the timing of screening (when the target population is tested before travel or during an outbreak; 12/62, 19%), the frequency of screening (appropriate frequencies for outbreak prevention, outbreak response, or community transmission control; 6/62, 10%), and the screening and detection procedure (the choice of individual or pooled detection and optimization of the pooling approach; 35/62, 56%). CONCLUSIONS This review reveals gaps in the optimization of COVID-19 screening strategies and suggests that a number of factors such as prevalence, screening accuracy, effective allocation of resources, and feasibility of strategies should be carefully considered in the development of future screening strategies.
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Affiliation(s)
- Yuanhua Liu
- Department of Epidemiology and Health Statistics, School of Public Health, Fudan University, Shanghai, China
- Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
| | - Yun Yin
- Department of Epidemiology and Health Statistics, School of Public Health, Fudan University, Shanghai, China
- Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
| | - Michael P Ward
- Sydney School of Veterinary Science, The University of Sydney, NSW, Australia
| | - Ke Li
- Department of Epidemiology and Health Statistics, School of Public Health, Fudan University, Shanghai, China
- Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
| | - Yue Chen
- School of Epidemiology and Public Health, Faculty of Medicine, University of Ottawa, Ottawa, ON, Canada
| | - Mengwei Duan
- Department of Mathematics and Physics, North China Electric Power University, Baoding, China
| | | | - Jie Hong
- Department of Epidemiology and Health Statistics, School of Public Health, Fudan University, Shanghai, China
- Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
| | - Jiaqi Huang
- Department of Epidemiology and Health Statistics, School of Public Health, Fudan University, Shanghai, China
- Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
| | - Jin Shi
- Department of Epidemiology and Health Statistics, School of Public Health, Fudan University, Shanghai, China
- Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
| | - Xuan Zhou
- Department of Mathematics and Physics, North China Electric Power University, Baoding, China
| | - Xi Chen
- Department of Epidemiology and Health Statistics, School of Public Health, Fudan University, Shanghai, China
- Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
| | - Jiayao Xu
- Department of Epidemiology and Health Statistics, School of Public Health, Fudan University, Shanghai, China
- Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
| | - Rui Yuan
- Department of Epidemiology and Health Statistics, School of Public Health, Fudan University, Shanghai, China
- Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
| | - Lingcai Kong
- Department of Mathematics and Physics, North China Electric Power University, Baoding, China
| | - Zhijie Zhang
- Department of Epidemiology and Health Statistics, School of Public Health, Fudan University, Shanghai, China
- Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
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Mailepessov D, Arivalan S, Kong M, Griffiths J, Low SL, Chen H, Hapuarachchi HC, Gu X, Lee WL, Alm EJ, Thompson J, Wuertz S, Gin K, Ng LC, Wong JCC. Development of an efficient wastewater testing protocol for high-throughput country-wide SARS-CoV-2 monitoring. Sci Total Environ 2022; 826:154024. [PMID: 35217043 PMCID: PMC8860745 DOI: 10.1016/j.scitotenv.2022.154024] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/29/2021] [Revised: 02/15/2022] [Accepted: 02/16/2022] [Indexed: 05/04/2023]
Abstract
Wastewater-based surveillance has been widely used as a non-intrusive tool to monitor population-level transmission of COVID-19. Although various approaches are available to concentrate viruses from wastewater samples, scalable methods remain limited. Here, we sought to identify and evaluate SARS-CoV-2 virus concentration protocols for high-throughput wastewater testing. A total of twelve protocols for polyethylene glycol (PEG) precipitation and four protocols for ultrafiltration-based approaches were evaluated across two phases. The first phase entailed an initial evaluation using a small sample set, while the second phase further evaluated five protocols using wastewater samples of varying SARS-CoV-2 concentrations. Permutations in the pre-concentration, virus concentration and RNA extraction steps were evaluated. Among PEG-based methods, SARS-CoV-2 virus recovery was optimal with 1) the removal of debris prior to processing, 2) 2 h to 24 h incubation with 8% PEG at 4 °C, 3) 4000 xg or 14,000 xg centrifugation, and 4) a column-based RNA extraction method, yielding virus recovery of 42.4-52.5%. Similarly, the optimal protocol for ultrafiltration included 1) the removal of debris prior to processing, 2) ultrafiltration, and 3) a column-based RNA extraction method, yielding a recovery of 38.2%. This study also revealed that SARS-CoV-2 RNA recovery for samples with higher virus concentration were less sensitive to changes in the PEG method, but permutations in the PEG protocol could significantly impact virus yields when wastewater samples with lower SARS-CoV-2 RNA were used. Although both PEG precipitation and ultrafiltration methods resulted in similar SARS-CoV-2 RNA recoveries, the former method is more cost-effective while the latter method provided operational efficiency as it required a shorter turn-around-time (PEG precipitation, 9-23 h; Ultrafiltration, 5 h). The decision on which method to adopt will thus depend on the use-case for wastewater testing, and the need for cost-effectiveness, sensitivity, operational feasibility and scalability.
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Affiliation(s)
- Diyar Mailepessov
- Environmental Health Institute, National Environment Agency, 11 Biopolis Way #06-05/08, Helios Block, Singapore 138667, Singapore
| | - Sathish Arivalan
- Environmental Health Institute, National Environment Agency, 11 Biopolis Way #06-05/08, Helios Block, Singapore 138667, Singapore
| | - Marcella Kong
- Environmental Health Institute, National Environment Agency, 11 Biopolis Way #06-05/08, Helios Block, Singapore 138667, Singapore
| | - Jane Griffiths
- Environmental Health Institute, National Environment Agency, 11 Biopolis Way #06-05/08, Helios Block, Singapore 138667, Singapore
| | - Swee Ling Low
- Environmental Health Institute, National Environment Agency, 11 Biopolis Way #06-05/08, Helios Block, Singapore 138667, Singapore
| | - Hongjie Chen
- Antimicrobial Resistance Interdisciplinary Research Group, Singapore-MIT Alliance for Research and Technology, Singapore 138602, Singapore; Campus for Research Excellence and Technological Enterprise (CREATE), Singapore 138602, Singapore
| | | | - Xiaoqiong Gu
- Antimicrobial Resistance Interdisciplinary Research Group, Singapore-MIT Alliance for Research and Technology, Singapore 138602, Singapore; Campus for Research Excellence and Technological Enterprise (CREATE), Singapore 138602, Singapore
| | - Wei Lin Lee
- Antimicrobial Resistance Interdisciplinary Research Group, Singapore-MIT Alliance for Research and Technology, Singapore 138602, Singapore; Campus for Research Excellence and Technological Enterprise (CREATE), Singapore 138602, Singapore
| | - Eric J Alm
- Antimicrobial Resistance Interdisciplinary Research Group, Singapore-MIT Alliance for Research and Technology, Singapore 138602, Singapore; Campus for Research Excellence and Technological Enterprise (CREATE), Singapore 138602, Singapore; Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Janelle Thompson
- Campus for Research Excellence and Technological Enterprise (CREATE), Singapore 138602, Singapore; Singapore Centre for Environmental Life Sciences Engineering, Nanyang Technological University, Singapore 637551, Singapore; Asian School of the Environment, Nanyang Technological University, Singapore 637459, Singapore
| | - Stefan Wuertz
- Singapore Centre for Environmental Life Sciences Engineering, Nanyang Technological University, Singapore 637551, Singapore; School of Civil and Environmental Engineering, Nanyang Technological University, Singapore 639798, Singapore
| | - Karina Gin
- Department of Civil and Environmental Engineering, Faculty of Engineering, National University of Singapore, 1 Engineering Drive 2, Singapore 117576, Singapore
| | - Lee Ching Ng
- Environmental Health Institute, National Environment Agency, 11 Biopolis Way #06-05/08, Helios Block, Singapore 138667, Singapore; School of Biological Sciences, Nanyang Technological University, Singapore 639798, Singapore
| | - Judith Chui Ching Wong
- Environmental Health Institute, National Environment Agency, 11 Biopolis Way #06-05/08, Helios Block, Singapore 138667, Singapore.
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