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How contact patterns during the COVID-19 pandemic are related to pre-pandemic contact patterns and mobility trends
BACKGROUND: Non-pharmaceutical interventions (NPIs) were adopted in Belgium in order to decrease social interactions between people and as such decrease viral transmission of SARS-CoV-2. With the aim to better evaluate the impact of NPIs on the evolution of the pandemic, an estimation of social cont...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10276431/ https://www.ncbi.nlm.nih.gov/pubmed/37328811 http://dx.doi.org/10.1186/s12879-023-08369-8 |
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author | Lajot, Adrien Wambua, James Coletti, Pietro Franco, Nicolas Brondeel, Ruben Faes, Christel Hens, Niel |
author_facet | Lajot, Adrien Wambua, James Coletti, Pietro Franco, Nicolas Brondeel, Ruben Faes, Christel Hens, Niel |
author_sort | Lajot, Adrien |
collection | PubMed |
description | BACKGROUND: Non-pharmaceutical interventions (NPIs) were adopted in Belgium in order to decrease social interactions between people and as such decrease viral transmission of SARS-CoV-2. With the aim to better evaluate the impact of NPIs on the evolution of the pandemic, an estimation of social contact patterns during the pandemic is needed when social contact patterns are not available yet in real time. METHODS: In this paper we use a model-based approach allowing for time varying effects to evaluate whether mobility and pre-pandemic social contact patterns can be used to predict the social contact patterns observed during the COVID-19 pandemic between November 11, 2020 and July 4, 2022. RESULTS: We found that location-specific pre-pandemic social contact patterns are good indicators for estimating social contact patterns during the pandemic. However, the relationship between both changes with time. Considering a proxy for mobility, namely the change in the number of visitors to transit stations, in interaction with pre-pandemic contacts does not explain the time-varying nature of this relationship well. CONCLUSION: In a situation where data from social contact surveys conducted during the pandemic are not yet available, the use of a linear combination of pre-pandemic social contact patterns could prove valuable. However, translating the NPIs at a given time into appropriate coefficients remains the main challenge of such an approach. In this respect, the assumption that the time variation of the coefficients can somehow be related to aggregated mobility data seems unacceptable during our study period for estimating the number of contacts at a given time. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12879-023-08369-8. |
format | Online Article Text |
id | pubmed-10276431 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-102764312023-06-18 How contact patterns during the COVID-19 pandemic are related to pre-pandemic contact patterns and mobility trends Lajot, Adrien Wambua, James Coletti, Pietro Franco, Nicolas Brondeel, Ruben Faes, Christel Hens, Niel BMC Infect Dis Research BACKGROUND: Non-pharmaceutical interventions (NPIs) were adopted in Belgium in order to decrease social interactions between people and as such decrease viral transmission of SARS-CoV-2. With the aim to better evaluate the impact of NPIs on the evolution of the pandemic, an estimation of social contact patterns during the pandemic is needed when social contact patterns are not available yet in real time. METHODS: In this paper we use a model-based approach allowing for time varying effects to evaluate whether mobility and pre-pandemic social contact patterns can be used to predict the social contact patterns observed during the COVID-19 pandemic between November 11, 2020 and July 4, 2022. RESULTS: We found that location-specific pre-pandemic social contact patterns are good indicators for estimating social contact patterns during the pandemic. However, the relationship between both changes with time. Considering a proxy for mobility, namely the change in the number of visitors to transit stations, in interaction with pre-pandemic contacts does not explain the time-varying nature of this relationship well. CONCLUSION: In a situation where data from social contact surveys conducted during the pandemic are not yet available, the use of a linear combination of pre-pandemic social contact patterns could prove valuable. However, translating the NPIs at a given time into appropriate coefficients remains the main challenge of such an approach. In this respect, the assumption that the time variation of the coefficients can somehow be related to aggregated mobility data seems unacceptable during our study period for estimating the number of contacts at a given time. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12879-023-08369-8. BioMed Central 2023-06-16 /pmc/articles/PMC10276431/ /pubmed/37328811 http://dx.doi.org/10.1186/s12879-023-08369-8 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This 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 Lajot, Adrien Wambua, James Coletti, Pietro Franco, Nicolas Brondeel, Ruben Faes, Christel Hens, Niel How contact patterns during the COVID-19 pandemic are related to pre-pandemic contact patterns and mobility trends |
title | How contact patterns during the COVID-19 pandemic are related to pre-pandemic contact patterns and mobility trends |
title_full | How contact patterns during the COVID-19 pandemic are related to pre-pandemic contact patterns and mobility trends |
title_fullStr | How contact patterns during the COVID-19 pandemic are related to pre-pandemic contact patterns and mobility trends |
title_full_unstemmed | How contact patterns during the COVID-19 pandemic are related to pre-pandemic contact patterns and mobility trends |
title_short | How contact patterns during the COVID-19 pandemic are related to pre-pandemic contact patterns and mobility trends |
title_sort | how contact patterns during the covid-19 pandemic are related to pre-pandemic contact patterns and mobility trends |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10276431/ https://www.ncbi.nlm.nih.gov/pubmed/37328811 http://dx.doi.org/10.1186/s12879-023-08369-8 |
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