How contact patterns during the COVID-19 pandemic are related to pre-pandemic contact patterns and mobility trends

Adrien Lajot, James Wambua, Pietro Coletti, Nicolas Franco, Ruben Brondeel, Christel Faes, Niel Hens

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Abstract

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.

Original languageEnglish
Article number410
JournalBMC Infectious Diseases
Volume23
Issue number1
DOIs
Publication statusPublished - Dec 2023

Keywords

  • Contact patterns
  • COVID-19
  • Mobility trends
  • SARS-CoV-2
  • Time-varying effect model

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