Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/40693
Title: How contact patterns during the COVID-19 pandemic are related to pre-pandemic contact patterns and mobility trends
Authors: LAJOT, Adrien 
WAMBUA, James 
COLETTI, Pietro 
FRANCO, Nicolas 
Brondeel, Ruben
FAES, Christel 
HENS, Niel 
Issue Date: 2023
Publisher: BMC
Source: BMC INFECTIOUS DISEASES, 23 (1) (Art N° 410)
Abstract: BackgroundNon-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.MethodsIn 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.ResultsWe 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.ConclusionIn 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.
Notes: Lajot, A (corresponding author), Sciensano, Dept Epidemiol & Publ Hlth, Brussels, Belgium.; Lajot, A (corresponding author), Univ Hasselt, Data Sci Inst, I BioStat, Hasselt, Belgium.
adrien.lajot@sciensano.be
Keywords: COVID-19;SARS-CoV-2;Contact patterns;Mobility trends;Time-varying effect model
Document URI: http://hdl.handle.net/1942/40693
e-ISSN: 1471-2334
DOI: 10.1186/s12879-023-08369-8
ISI #: 001012372800004
Rights: The Author(s) 2023. 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/. The Creative Commons Public Domain Dedication waiver (http://creativeco mmons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
Category: A1
Type: Journal Contribution
Appears in Collections:Research publications

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