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Spatio-temporal surveillance and early detection of SARS-CoV-2 variants of concern: a retrospective analysis
The SARS-CoV-2 pandemic has been characterized by the repeated emergence of genetically distinct virus variants of increased transmissibility and immune evasion compared to pre-existing lineages. In many countries, their containment required the intervention of public health authorities and the impo...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Royal Society
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10645511/ https://www.ncbi.nlm.nih.gov/pubmed/37963560 http://dx.doi.org/10.1098/rsif.2023.0410 |
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author | Cavallaro, Massimo Dyson, Louise Tildesley, Michael J. Todkill, Dan Keeling, Matt J. |
author_facet | Cavallaro, Massimo Dyson, Louise Tildesley, Michael J. Todkill, Dan Keeling, Matt J. |
author_sort | Cavallaro, Massimo |
collection | PubMed |
description | The SARS-CoV-2 pandemic has been characterized by the repeated emergence of genetically distinct virus variants of increased transmissibility and immune evasion compared to pre-existing lineages. In many countries, their containment required the intervention of public health authorities and the imposition of control measures. While the primary role of testing is to identify infection, target treatment, and limit spread (through isolation and contact tracing), a secondary benefit is in terms of surveillance and the early detection of new variants. Here we study the spatial invasion and early spread of the Alpha, Delta and Omicron (BA.1 and BA.2) variants in England from September 2020 to February 2022 using the random neighbourhood covering (RaNCover) method. This is a statistical technique for the detection of aberrations in spatial point processes, which we tailored here to community PCR (polymerase-chain-reaction) test data where the TaqPath kit provides a proxy measure of the switch between variants. Retrospectively, RaNCover detected the earliest signals associated with the four novel variants that led to large infection waves in England. With suitable data our method therefore has the potential to rapidly detect outbreaks of future SARS-CoV-2 variants, thus helping to inform targeted public health interventions. |
format | Online Article Text |
id | pubmed-10645511 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | The Royal Society |
record_format | MEDLINE/PubMed |
spelling | pubmed-106455112023-11-15 Spatio-temporal surveillance and early detection of SARS-CoV-2 variants of concern: a retrospective analysis Cavallaro, Massimo Dyson, Louise Tildesley, Michael J. Todkill, Dan Keeling, Matt J. J R Soc Interface Life Sciences–Mathematics interface The SARS-CoV-2 pandemic has been characterized by the repeated emergence of genetically distinct virus variants of increased transmissibility and immune evasion compared to pre-existing lineages. In many countries, their containment required the intervention of public health authorities and the imposition of control measures. While the primary role of testing is to identify infection, target treatment, and limit spread (through isolation and contact tracing), a secondary benefit is in terms of surveillance and the early detection of new variants. Here we study the spatial invasion and early spread of the Alpha, Delta and Omicron (BA.1 and BA.2) variants in England from September 2020 to February 2022 using the random neighbourhood covering (RaNCover) method. This is a statistical technique for the detection of aberrations in spatial point processes, which we tailored here to community PCR (polymerase-chain-reaction) test data where the TaqPath kit provides a proxy measure of the switch between variants. Retrospectively, RaNCover detected the earliest signals associated with the four novel variants that led to large infection waves in England. With suitable data our method therefore has the potential to rapidly detect outbreaks of future SARS-CoV-2 variants, thus helping to inform targeted public health interventions. The Royal Society 2023-11-15 /pmc/articles/PMC10645511/ /pubmed/37963560 http://dx.doi.org/10.1098/rsif.2023.0410 Text en © 2023 The Authors. https://creativecommons.org/licenses/by/4.0/Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, provided the original author and source are credited. |
spellingShingle | Life Sciences–Mathematics interface Cavallaro, Massimo Dyson, Louise Tildesley, Michael J. Todkill, Dan Keeling, Matt J. Spatio-temporal surveillance and early detection of SARS-CoV-2 variants of concern: a retrospective analysis |
title | Spatio-temporal surveillance and early detection of SARS-CoV-2 variants of concern: a retrospective analysis |
title_full | Spatio-temporal surveillance and early detection of SARS-CoV-2 variants of concern: a retrospective analysis |
title_fullStr | Spatio-temporal surveillance and early detection of SARS-CoV-2 variants of concern: a retrospective analysis |
title_full_unstemmed | Spatio-temporal surveillance and early detection of SARS-CoV-2 variants of concern: a retrospective analysis |
title_short | Spatio-temporal surveillance and early detection of SARS-CoV-2 variants of concern: a retrospective analysis |
title_sort | spatio-temporal surveillance and early detection of sars-cov-2 variants of concern: a retrospective analysis |
topic | Life Sciences–Mathematics interface |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10645511/ https://www.ncbi.nlm.nih.gov/pubmed/37963560 http://dx.doi.org/10.1098/rsif.2023.0410 |
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