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Koiso S, Gulbas E, Dike L, Mulroy NM, Ciaranello AL, Freedberg KA, Jalali MS, Walker AT, Ryan ET, LaRocque RC, Hyle EP. Modeling approaches to inform travel-related policies for COVID-19 containment: a scoping review and future directions. Travel Med Infect Dis 2024:102730. [PMID: 38830442 DOI: 10.1016/j.tmaid.2024.102730] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Revised: 05/22/2024] [Accepted: 05/28/2024] [Indexed: 06/05/2024]
Abstract
BACKGROUND Travel-related strategies to reduce the spread of COVID-19 evolved rapidly in response to changes in the understanding of SARS-CoV-2 and newly available tools for prevention, diagnosis, and treatment. Modeling is an important methodology to investigate the range of outcomes that could occur from different disease containment strategies. METHODS We examined 43 articles published from December 2019 through September 2022 that used modeling to evaluate travel-related COVID-19 containment strategies. We extracted and synthesized data regarding study objectives, methods, outcomes, populations, settings, strategies, and costs. We used a standardized approach to evaluate each analysis according to 26 criteria for modeling quality and rigor. RESULTS The most frequent approaches included compartmental modeling to examine quarantine, isolation, or testing. Early in the pandemic, the goal was to prevent travel-related COVID-19 cases with a focus on individual-level outcomes and assessing strategies such as travel restrictions, quarantine without testing, social distancing, and on-arrival PCR testing. After the development of diagnostic tests and vaccines, modeling studies projected population-level outcomes and investigated these tools to limit COVID-19 spread. Very few published studies included rapid antigen screening strategies, costs, explicit model calibration, or critical evaluation of the modeling approaches. CONCLUSION Future modeling analyses should leverage open-source data, improve the transparency of modeling methods, incorporate newly available prevention, diagnostics, and treatments, and include costs and cost-effectiveness so that modeling analyses can be informative to address future SARS-CoV-2 variants of concern and other emerging infectious diseases (e.g., mpox and Ebola) for travel-related health policies.
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Affiliation(s)
- Satoshi Koiso
- Medical Practice Evaluation Center, Massachusetts General Hospital, 100 Cambridge St., 16th Floor, Boston, MA, USA.
| | - Eren Gulbas
- Medical Practice Evaluation Center, Massachusetts General Hospital, 100 Cambridge St., 16th Floor, Boston, MA, USA
| | - Lotanna Dike
- Medical Practice Evaluation Center, Massachusetts General Hospital, 100 Cambridge St., 16th Floor, Boston, MA, USA
| | - Nora M Mulroy
- Medical Practice Evaluation Center, Massachusetts General Hospital, 100 Cambridge St., 16th Floor, Boston, MA, USA
| | - Andrea L Ciaranello
- Medical Practice Evaluation Center, Massachusetts General Hospital, 100 Cambridge St., 16th Floor, Boston, MA, USA; Harvard Medical School, 25 Shattuck Street, Boston, MA, USA; Division of Infectious Diseases, Massachusetts General Hospital, 55 Fruit Street, Boston, MA, USA
| | - Kenneth A Freedberg
- Medical Practice Evaluation Center, Massachusetts General Hospital, 100 Cambridge St., 16th Floor, Boston, MA, USA; Harvard Medical School, 25 Shattuck Street, Boston, MA, USA; Division of Infectious Diseases, Massachusetts General Hospital, 55 Fruit Street, Boston, MA, USA; Harvard T.H. Chan School of Public Health, 677 Huntington Avenue, Boston, MA, USA
| | - Mohammad S Jalali
- Harvard Medical School, 25 Shattuck Street, Boston, MA, USA; Institute for Technology Assessment, Massachusetts General Hospital, 101 Merrimac St., Suite 1010, Boston, MA, USA
| | - Allison T Walker
- Division of Global Migration and Quarantine, Centers for Disease Control and Prevention, 1600 Clifton Road, Atlanta, GA, USA
| | - Edward T Ryan
- Medical Practice Evaluation Center, Massachusetts General Hospital, 100 Cambridge St., 16th Floor, Boston, MA, USA; Harvard Medical School, 25 Shattuck Street, Boston, MA, USA; Division of Infectious Diseases, Massachusetts General Hospital, 55 Fruit Street, Boston, MA, USA; Harvard T.H. Chan School of Public Health, 677 Huntington Avenue, Boston, MA, USA; Travelers' Advice and Immunization Center, Massachusetts General Hospital, Cox Building, 5th Floor, 55 Fruit Street, Boston, MA, USA
| | - Regina C LaRocque
- Harvard Medical School, 25 Shattuck Street, Boston, MA, USA; Division of Infectious Diseases, Massachusetts General Hospital, 55 Fruit Street, Boston, MA, USA; Travelers' Advice and Immunization Center, Massachusetts General Hospital, Cox Building, 5th Floor, 55 Fruit Street, Boston, MA, USA
| | - Emily P Hyle
- Medical Practice Evaluation Center, Massachusetts General Hospital, 100 Cambridge St., 16th Floor, Boston, MA, USA; Harvard Medical School, 25 Shattuck Street, Boston, MA, USA; Division of Infectious Diseases, Massachusetts General Hospital, 55 Fruit Street, Boston, MA, USA; Travelers' Advice and Immunization Center, Massachusetts General Hospital, Cox Building, 5th Floor, 55 Fruit Street, Boston, MA, USA.
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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] [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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Momenifar N, Pirouzfar M, Hashemian Z, Daneshvar Amoli A. Development of an optimized RT-LAMP test for the detection of SARS-CoV-2. Biologicals 2023; 84:101716. [PMID: 37801803 DOI: 10.1016/j.biologicals.2023.101716] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2022] [Revised: 06/17/2023] [Accepted: 09/25/2023] [Indexed: 10/08/2023] Open
Abstract
SARS-COV-2 is the causative agent of an acute respiratory syndrome called Coronavirus disease 2019 (COVID-19) with a varying mortality rate from 2019 to 2022. There are several measures for control and prevention of Covid-19 including using mask, vaccine injections, as well as screening the potential cases. We aimed to design and develop a molecular method (RT-LAMP) for detecting coronavirus in biological samples that is cheaper, faster and easier than conventional molecular methods. In this study, various reaction components were explored to make the optimal combination of an RT-LAMP master mix composition. The results revealed the ability of this RT-LAMP test in specifically identifying 100 copies of mixture of N and E genes in just 30-45 min. This study demonstrated the reliable performance of the RT-LAMP method for the detection of SARS-COV-2 in biological samples. Given the significant advantages of this method compared to the gold standard qRT-PCR, it can be employed as a promising tool for the diagnosis of coronavirus as well as other pathogenic viruses.
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Affiliation(s)
- Navid Momenifar
- Human and Animal Cell Bank, Iranian Biological Resource Center (IBRC), ACECR, Tehran, Iran
| | - Mohammad Pirouzfar
- Human and Animal Cell Bank, Iranian Biological Resource Center (IBRC), ACECR, Tehran, Iran
| | - Zohreh Hashemian
- Human and Animal Cell Bank, Iranian Biological Resource Center (IBRC), ACECR, Tehran, Iran
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