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COVID-19 hotspots through clusters analysis in France (may–October 2020): where should we track the virus to mitigate the spread?
BACKGROUND: In France, the lifting of the lockdown implemented to control the COVID-19 first wave in 2020 was followed by a reinforced contact-tracing (CT) strategy for the early detection of cases and transmission chains. We developed a reporting system of clusters defined as at least three COVID-1...
Autores principales: | , , , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8503705/ https://www.ncbi.nlm.nih.gov/pubmed/34635085 http://dx.doi.org/10.1186/s12889-021-11857-8 |
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author | Spaccaferri, Guillaume Calba, Clémentine Vilain, Pascal Garras, Loïc Durand, Cécile Pilorget, Corinne Atiki, Nahida Bernillon, Pascale Bosc, Laëtitia Fougère, Erica Hanon, Jean-Baptiste Henry, Valérie Huchet-Kervella, Caroline Martel, Mélanie Pontiès, Valérie Mouly, Damien Rolland du Roscoat, Enguerrand Le Vu, Stéphane Desenclos, Jean-Claude Laporte, Anne Rolland, Patrick |
author_facet | Spaccaferri, Guillaume Calba, Clémentine Vilain, Pascal Garras, Loïc Durand, Cécile Pilorget, Corinne Atiki, Nahida Bernillon, Pascale Bosc, Laëtitia Fougère, Erica Hanon, Jean-Baptiste Henry, Valérie Huchet-Kervella, Caroline Martel, Mélanie Pontiès, Valérie Mouly, Damien Rolland du Roscoat, Enguerrand Le Vu, Stéphane Desenclos, Jean-Claude Laporte, Anne Rolland, Patrick |
author_sort | Spaccaferri, Guillaume |
collection | PubMed |
description | BACKGROUND: In France, the lifting of the lockdown implemented to control the COVID-19 first wave in 2020 was followed by a reinforced contact-tracing (CT) strategy for the early detection of cases and transmission chains. We developed a reporting system of clusters defined as at least three COVID-19 cases, within seven days and belonging to the same community or having participated in the same gathering, whether they know each other or not. The aim of this study was to describe the typology and criticality of clusters reported between the two lockdowns in France to guide future action prioritisation. METHODS: In this study we describe the typology and criticality of COVID-19 clusters between the two lockdowns implemented in France (between May and end of October 2020). Clusters were registered in a national database named “MONIC” (MONItoring des Clusters), established in May 2020. This surveillance system identified the most affected communities in a timely manner. A level of criticality was defined for each cluster to take into consideration the risk of spreading within and outside the community of occurrence, and the health impact within the community. We compared the level of criticality according to the type of community in which the cluster occurred using Pearson’s chi-square tests. RESULTS: A total of 7236 clusters were reported over the study period, particularly in occupational environment (25.1%, n = 1813), elderly care structures (21.9%, n = 1586), and educational establishments (15.9%, n = 1154). We show a shift over time of the most affected communities in terms of number of clusters. Clusters reported in occupational environment and the personal sphere had increased during summer while clusters reported in educational environment increased after the start of the school year. This trend mirrors change of transmission pattern overtime according to social contacts. Among all reported clusters, 43.1% had a high level of criticality with significant differences between communities (p < 0.0001). A majority of clusters had a high level of criticality in elderly care structures (82.2%), in disability care centres (56.6%), and health care facilities (51.7%). CONCLUSION: These results highlight the importance of targeting public health action based on timely sustained investigations, testing capacity and targeted awareness campaigns. The emergence of new SARS-CoV-2 variants strengthen these public health recommendations and the need for rapid and prioritise vaccination campaigns. |
format | Online Article Text |
id | pubmed-8503705 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-85037052021-10-12 COVID-19 hotspots through clusters analysis in France (may–October 2020): where should we track the virus to mitigate the spread? Spaccaferri, Guillaume Calba, Clémentine Vilain, Pascal Garras, Loïc Durand, Cécile Pilorget, Corinne Atiki, Nahida Bernillon, Pascale Bosc, Laëtitia Fougère, Erica Hanon, Jean-Baptiste Henry, Valérie Huchet-Kervella, Caroline Martel, Mélanie Pontiès, Valérie Mouly, Damien Rolland du Roscoat, Enguerrand Le Vu, Stéphane Desenclos, Jean-Claude Laporte, Anne Rolland, Patrick BMC Public Health Research Article BACKGROUND: In France, the lifting of the lockdown implemented to control the COVID-19 first wave in 2020 was followed by a reinforced contact-tracing (CT) strategy for the early detection of cases and transmission chains. We developed a reporting system of clusters defined as at least three COVID-19 cases, within seven days and belonging to the same community or having participated in the same gathering, whether they know each other or not. The aim of this study was to describe the typology and criticality of clusters reported between the two lockdowns in France to guide future action prioritisation. METHODS: In this study we describe the typology and criticality of COVID-19 clusters between the two lockdowns implemented in France (between May and end of October 2020). Clusters were registered in a national database named “MONIC” (MONItoring des Clusters), established in May 2020. This surveillance system identified the most affected communities in a timely manner. A level of criticality was defined for each cluster to take into consideration the risk of spreading within and outside the community of occurrence, and the health impact within the community. We compared the level of criticality according to the type of community in which the cluster occurred using Pearson’s chi-square tests. RESULTS: A total of 7236 clusters were reported over the study period, particularly in occupational environment (25.1%, n = 1813), elderly care structures (21.9%, n = 1586), and educational establishments (15.9%, n = 1154). We show a shift over time of the most affected communities in terms of number of clusters. Clusters reported in occupational environment and the personal sphere had increased during summer while clusters reported in educational environment increased after the start of the school year. This trend mirrors change of transmission pattern overtime according to social contacts. Among all reported clusters, 43.1% had a high level of criticality with significant differences between communities (p < 0.0001). A majority of clusters had a high level of criticality in elderly care structures (82.2%), in disability care centres (56.6%), and health care facilities (51.7%). CONCLUSION: These results highlight the importance of targeting public health action based on timely sustained investigations, testing capacity and targeted awareness campaigns. The emergence of new SARS-CoV-2 variants strengthen these public health recommendations and the need for rapid and prioritise vaccination campaigns. BioMed Central 2021-10-11 /pmc/articles/PMC8503705/ /pubmed/34635085 http://dx.doi.org/10.1186/s12889-021-11857-8 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Article Spaccaferri, Guillaume Calba, Clémentine Vilain, Pascal Garras, Loïc Durand, Cécile Pilorget, Corinne Atiki, Nahida Bernillon, Pascale Bosc, Laëtitia Fougère, Erica Hanon, Jean-Baptiste Henry, Valérie Huchet-Kervella, Caroline Martel, Mélanie Pontiès, Valérie Mouly, Damien Rolland du Roscoat, Enguerrand Le Vu, Stéphane Desenclos, Jean-Claude Laporte, Anne Rolland, Patrick COVID-19 hotspots through clusters analysis in France (may–October 2020): where should we track the virus to mitigate the spread? |
title | COVID-19 hotspots through clusters analysis in France (may–October 2020): where should we track the virus to mitigate the spread? |
title_full | COVID-19 hotspots through clusters analysis in France (may–October 2020): where should we track the virus to mitigate the spread? |
title_fullStr | COVID-19 hotspots through clusters analysis in France (may–October 2020): where should we track the virus to mitigate the spread? |
title_full_unstemmed | COVID-19 hotspots through clusters analysis in France (may–October 2020): where should we track the virus to mitigate the spread? |
title_short | COVID-19 hotspots through clusters analysis in France (may–October 2020): where should we track the virus to mitigate the spread? |
title_sort | covid-19 hotspots through clusters analysis in france (may–october 2020): where should we track the virus to mitigate the spread? |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8503705/ https://www.ncbi.nlm.nih.gov/pubmed/34635085 http://dx.doi.org/10.1186/s12889-021-11857-8 |
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