Augmenting contact matrices with time-use data for fine-grained intervention modelling of disease dynamics: A modelling analysis

  1. Edwin van Leeuwen
  2. PHE Joint modelling group
  3. Frank Sandmann

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Abstract

Social distancing is an important public health intervention to reduce or interrupt the sustained community transmission of emerging infectious pathogens, such as severe acute respiratory syndrome-coronavirus-2 during the coronavirus disease 2019 pandemic. Contact matrices are typically used when evaluating such public health interventions to account for the heterogeneity in social mixing of individuals, but the surveys used to obtain the number of contacts often lack detailed information on the time individuals spend on daily activities. The present work addresses this problem by combining the large-scale empirical data of a social contact survey and a time-use survey to estimate contact matrices by age group (0--15, 16--24, 25–44, 45–64, 65+ years) and daily activity (work, schooling, transportation, and four leisure activities: social visits, bar/cafe/restaurant visits, park visits, and non-essential shopping). This augmentation allows exploring the impact of fewer contacts when individuals reduce the time they spend on selected daily activities as well as when lifting such restrictions again. For illustration, the derived matrices were then applied to an age-structured dynamic-transmission model of coronavirus disease 2019. Findings show how contact matrices can be successfully augmented with time-use data to inform the relative reductions in contacts by activity, which allows for more fine-grained mixing patterns and infectious disease modelling.

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  1. Version published to 10.1177/09622802211037078
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    SciScore for 10.1101/2020.06.03.20067793: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    No key resources detected.

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    Strengths and Limitations: Our analysis combined the empirical data of two large-scale, representative surveys, and we successfully applied the resulting contact matrices in a dynamic-transmission model to explore the impact of social distancing measures adopted during the COVID-19 pandemic [3,11,12]. Augmenting the social contact matrices with time-use data has enriched the frequency of contacts with the duration of exposure in the derived matrices, which allows for more fine-grained mixing patterns than conventionally used [6,13,34]. These findings from the augmented contact matrices will be more broadly applicable to newly emerging infectious pathogens whose spread is highly dependent on the social contact mixing patterns of communities, including influenza pandemics. Our application to infections with SARS-CoV-2 causing COVID-19 illustrated the impact of reducing social contacts, and when lifting these restrictions again. Future pandemics, however, will require suitably adapted models that are tailored to the epidemiology of that pandemic pathogen and the disease it causes. Although additional information is elicited in both surveys, we considered them the most robust for their main purpose: social contacts and the time-use per day. Furthermore, a significant amount of transport is linked in the data to school and work activities, and reductions will thus likewise impact transportation. We cannot rule out that some activities were misclassified, however; for instance, the...

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
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    • No protocol registration statement was detected.

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  3. Version published to 10.1101/2020.06.03.20067793v1 on medRxiv