Estimating age patterns and grouped temporal trends in human contact patterns with Bayesian P-splines

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Abstract This paper presents a smoothing method to estimate age-specific human contact patterns and their variations over different periods. Specifically, it examines how age-specific contact patterns shift under varying conditions, such as holiday periods and levels of public health intervention. The method uses Bayesian P-splines to smooth age-specific contact rates and leverages Laplace approximations for fast Bayesian inference, significantly reducing computational complexity. The proposed methodology is applied to the CoMix data from Belgium, a social contact survey collected during the COVID-19 pandemic. Results indicate significantly reduced contacts during periods in which strict social policies were in place, particularly among adults, and notable reductions among young individuals during holidays. This research advances our understanding of how human contact adapts in response to varying social and policy conditions, which can guide more realistic and adaptive infectious disease transmission models.

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