Estimating fine age structure and time trends in human contact patterns from coarse contact data: The Bayesian rate consistency model

Author:

Dan ShozenORCID,Chen YuORCID,Chen Yining,Monod Melodie,Jaeger Veronika K.ORCID,Bhatt Samir,Karch André,Ratmann OliverORCID,

Abstract

Since the emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), large-scale social contact surveys are now longitudinally measuring the fundamental changes in human interactions in the face of the pandemic and non-pharmaceutical interventions. Here, we present a model-based Bayesian approach that can reconstruct contact patterns at 1-year resolution even when the age of the contacts is reported coarsely by 5 or 10-year age bands. This innovation is rooted in population-level consistency constraints in how contacts between groups must add up, which prompts us to call the approach presented here the Bayesian rate consistency model. The model can also quantify time trends and adjust for reporting fatigue emerging in longitudinal surveys through the use of computationally efficient Hilbert Space Gaussian process priors. We illustrate estimation accuracy on simulated data as well as social contact data from Europe and Africa for which the exact age of contacts is reported, and then apply the model to social contact data with coarse information on the age of contacts that were collected in Germany during the COVID-19 pandemic from April to June 2020 across five longitudinal survey waves. We estimate the fine age structure in social contacts during the early stages of the pandemic and demonstrate that social contact intensities rebounded in an age-structured, non-homogeneous manner. The Bayesian rate consistency model provides a model-based, non-parametric, computationally tractable approach for estimating the fine structure and longitudinal trends in social contacts and is applicable to contemporary survey data with coarsely reported age of contacts as long as the exact age of survey participants is reported.

Funder

Imperial President’s PhD Scholarships

EPSRC Centre for Doctoral Training in Modern Statistics and Statistical Machine Learning at Imperial and Oxford

Bill and Melinda Gates Foundation

Medical Research Council

MRC Centre for Global Infectious Disease Analysis

Foreign, Commonwealth and Development Office

European Union

Novo Nordisk Foundation

Danish National Research Foundation

The Eric and Wendy Schmidt Fund For Strategic Innovation

National Institute of Health Research

Institute of Epidemiology and Social Medicine, University of Munster

Institute of Medical Epidemiology, Biometry and Informatics, Martin Luther University Halle-Wittenberg

Robert Koch Institute

Helmholtz-Gemeinschaft Deutscher Forschungszentren e.V.

Saxonian COVID-19 Research Consortium SaxoCOV

Deutsche Forschungsgemeinschaft

Bundesministerium für Bildung und Forschung

Network University Medicine

Publisher

Public Library of Science (PLoS)

Subject

Computational Theory and Mathematics,Cellular and Molecular Neuroscience,Genetics,Molecular Biology,Ecology,Modeling and Simulation,Ecology, Evolution, Behavior and Systematics

Reference43 articles.

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