ArticleEpidemics2019
Estimating age-mixing patterns relevant for the transmission of airborne infections.
Article in Epidemics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
5 citing papers in PubMed.
- Quantifying H5N1 outbreak potential and control effectiveness in high-risk agricultural populations.PLOS global public health · 2025Article
- Exploring human mixing patterns based on time use and social contact data and their implications for infectious disease transmission models.BMC infectious diseases · 2022Article
- Improving Estimates of Social Contact Patterns for Airborne Transmission of Respiratory Pathogens.Emerging infectious diseases · 2022Article
- Updating age-specific contact structures to match evolving demography in a dynamic mathematical model of tuberculosis vaccination.PLoS computational biology · 2022Article
- Prevalence and Risk Factors forOpen forum infectious diseases · 2021Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
introductionAge-mixing patterns can have substantial effects on infectious disease dynamics and intervention effects. Data on close contacts (people spoken to and/or touched) are often used to estimate age-mixing. These are not the only relevant contacts for airborne infections such as tuberculosis, where transmission can occur between anybody 'sharing air' indoors. Directly collecting data on age-mixing patterns between casual contacts (shared indoor space, but not 'close') is difficult however. We demonstrate a method for indirectly estimating age-mixing patterns between casual indoor contacts from social contact data.
methodsWe estimated age-mixing patterns between close, casual, and all contacts using data from a social contact survey in South Africa. The age distribution of casual contacts in different types of location was estimated from the reported time spent in the location type by respondents in each age group.
resultsPatterns of age-mixing calculated from contact numbers were similar between close and all contacts, however patterns of age-mixing calculated from contact time were more age-assortative in all contacts than in close contacts. There was also more variation by age group in total numbers of casual and all contacts, than in total numbers of close contacts. Estimates were robust to sensitivity analyses.
conclusionsPatterns of age-mixing can be estimated for all contacts using data that can be easily collected as part of social contact surveys or time-use surveys, and may differ from patterns between close contacts.
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