KCL TEST: an open-source inspired asymptomatic SARS-CoV-2 surveillance programme in an academic institution Article Swipe
Joana Andrade
,
Edward J. Scourfield
,
Shilpa Lekhraj Peswani-Sajnani
,
Kate Poulton
,
Tom ap Rees
,
Paniz Khooshemehri
,
G. Doherty
,
Sharon Ong
,
Iulia Ivan
,
Navid Goudarzi
,
Iain Gardiner
,
Eric D. Caine
,
Thomas J. A. Maguire
,
Daniel Leightley
,
Lorena Torrico
,
Alex Gasulla
,
Angel Menendez-Vazquez
,
Ana Maria Ortega Prieto
,
Suzanne Pickering
,
Jose Guardeño
,
Rahul Batra
,
Alai Tan
,
Anthony M. Griffin
,
Stelios Papaioannou
,
Charlotte Trouillet
,
Hannah E. Mischo
,
Victoriano Giralt
,
Stevin Wilson
,
Michael Kirk
,
Stuart J. D. Neil
,
Rui Pedro Galão
,
John Martindale
,
Carmel Curtis
,
Mark Zuckerman
,
Reza Razavi
,
Michael H. Malim
,
Rocío T. Martínez-Nuñez
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1101/2023.07.25.23293154
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1101/2023.07.25.23293154
Testing was paramount in the management of the COVID-19 pandemic. Our university established KCL TEST: a SARS-CoV-2 asymptomatic testing programme that enabled sensitive and accessible PCR testing of SARS-CoV-2 RNA in saliva. We performed 158,277 PCRs for our staff, students, and their household contacts, free of charge. Our average turnaround time was 16h and 37 mins from user registration to result delivery. KCL TEST combined open-source automation and in house non-commercial reagents, which allows for rapid deployment. Here we provide our blueprint, recently recommended for ISO15189 accreditation, and results to enable the rapid launch of diagnostic laboratories where and when needed, particularly in low-resource settings. Our data span over 18 months and parallels that of the UK Office for National Statistics, with a lower positive rate and virtually no delta wave. Our observations strongly support regular asymptomatic community testing to decrease outbreaks and provide safe working spaces. Universities can therefore provide agile, resilient, and accurate testing that reflects the infection rate and trend of the general population. We call for the integration of academic institutions in pandemic preparedness, with capabilities to rapidly deploy highly skilled staff, as well as develop, test and accommodate efficient low-cost pipelines.
Related Topics
Concepts
Test (biology)
Turnaround time
Population
Software deployment
Preparedness
Agile software development
Blueprint
Pandemic
Computer science
Business
Medical emergency
Medicine
Operations management
Coronavirus disease 2019 (COVID-19)
Engineering
Political science
Environmental health
Pathology
Biology
Operating system
Disease
Paleontology
Mechanical engineering
Infectious disease (medical specialty)
Law
Software engineering
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1101/2023.07.25.23293154
- https://www.medrxiv.org/content/medrxiv/early/2023/08/22/2023.07.25.23293154.full.pdf
- OA Status
- green
- References
- 27
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4385323613
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4385323613Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1101/2023.07.25.23293154Digital Object Identifier
- Title
-
KCL TEST: an open-source inspired asymptomatic SARS-CoV-2 surveillance programme in an academic institutionWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-07-27Full publication date if available
- Authors
-
Joana Andrade, Edward J. Scourfield, Shilpa Lekhraj Peswani-Sajnani, Kate Poulton, Tom ap Rees, Paniz Khooshemehri, G. Doherty, Sharon Ong, Iulia Ivan, Navid Goudarzi, Iain Gardiner, Eric D. Caine, Thomas J. A. Maguire, Daniel Leightley, Lorena Torrico, Alex Gasulla, Angel Menendez-Vazquez, Ana Maria Ortega Prieto, Suzanne Pickering, Jose Guardeño, Rahul Batra, Alai Tan, Anthony M. Griffin, Stelios Papaioannou, Charlotte Trouillet, Hannah E. Mischo, Victoriano Giralt, Stevin Wilson, Michael Kirk, Stuart J. D. Neil, Rui Pedro Galão, John Martindale, Carmel Curtis, Mark Zuckerman, Reza Razavi, Michael H. Malim, Rocío T. Martínez-NuñezList of authors in order
- Landing page
-
https://doi.org/10.1101/2023.07.25.23293154Publisher landing page
- PDF URL
-
https://www.medrxiv.org/content/medrxiv/early/2023/08/22/2023.07.25.23293154.full.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://www.medrxiv.org/content/medrxiv/early/2023/08/22/2023.07.25.23293154.full.pdfDirect OA link when available
- Concepts
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Test (biology), Turnaround time, Population, Software deployment, Preparedness, Agile software development, Blueprint, Pandemic, Computer science, Business, Medical emergency, Medicine, Operations management, Coronavirus disease 2019 (COVID-19), Engineering, Political science, Environmental health, Pathology, Biology, Operating system, Disease, Paleontology, Mechanical engineering, Infectious disease (medical specialty), Law, Software engineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- References (count)
-
27Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.with | 122, 179 |
| abstract_inverted_index.TEST: | 15 |
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| abstract_inverted_index.which | 73 |
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| abstract_inverted_index.staff, | 39, 186 |
| abstract_inverted_index.158,277 | 35 |
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| abstract_inverted_index.delivery. | 62 |
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| cited_by_percentile_year | |
| corresponding_author_ids | https://openalex.org/A5006723670 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 37 |
| corresponding_institution_ids | https://openalex.org/I183935753 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/3 |
| sustainable_development_goals[0].score | 0.44999998807907104 |
| sustainable_development_goals[0].display_name | Good health and well-being |
| citation_normalized_percentile.value | 0.14497088 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |