Cohort-based smoothing methods for age-specific contact rates

Author:

Vandendijck Yannick1,Gressani Oswaldo1ORCID,Faes Christel1,Camarda Carlo G2,Hens Niel3ORCID

Affiliation:

1. Data Science Institute, Hasselt University Interuniversity Institute for Biostatistics and Statistical Bioinformatics (I-BioStat), , Hasselt, Belgium

2. French Institute for Demographic Studies (INED), Aubervilliers, France

3. Data Science Institute, Hasselt University Interuniversity Institute for Biostatistics and Statistical Bioinformatics (I-BioStat), , Hasselt, Belgium and Centre for Health Economics Research and Modelling Infectious Diseases, Vaxinfectio, University of Antwerp, Antwerp, Belgium

Abstract

SummaryThe use of social contact rates is widespread in infectious disease modeling since it has been shown that they are key driving forces of important epidemiological parameters. Quantification of contact patterns is crucial to parameterize dynamic transmission models and to provide insights on the (basic) reproduction number. Information on social interactions can be obtained from population-based contact surveys, such as the European Commission project POLYMOD. Estimation of age-specific contact rates from these studies is often done using a piecewise constant approach or bivariate smoothing techniques. For the latter, typically, smoothness is introduced in the dimensions of the respondent’s and contact’s age (i.e., the rows and columns of the social contact matrix). We propose a smoothing constrained approach—taking into account the reciprocal nature of contacts—introducing smoothness over the diagonal (including all subdiagonals) of the social contact matrix. This modeling approach is justified assuming that when people age their contact behavior changes smoothly. We call this smoothing from a cohort perspective. Two approaches that allow for smoothing over social contact matrix diagonals are proposed, namely (i) reordering of the diagonal components of the contact matrix and (ii) reordering of the penalty matrix ensuring smoothness over the contact matrix diagonals. Parameter estimation is done in the likelihood framework by using constrained penalized iterative reweighted least squares. A simulation study underlines the benefits of cohort-based smoothing. Finally, the proposed methods are illustrated on the Belgian POLYMOD data of 2006. Code to reproduce the results of the article can be downloaded on this GitHub repository https://github.com/oswaldogressani/Cohort_smoothing.

Funder

European Research Council

European Union’s Horizon 2020 research and innovation programme

Publisher

Oxford University Press (OUP)

Subject

Statistics, Probability and Uncertainty,General Medicine,Statistics and Probability

Reference31 articles.

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