Projecting social contact matrices in 152 countries using contact surveys and demographic data
By Kiesha Prem, Alex R. Cook, Mark Jit. PLoS Comput. Biol., 2017
897 citations recorded by the source corpus.
Heterogeneities in contact networks have a major effect in determining whether a pathogen can become epidemic or persist at endemic levels. Epidemic models that determine which interventions can successfully prevent an outbreak need to account for social structure and mixing patterns. Contact patterns vary across age and locations (e.g. home, work, and school), and including them as predictors in transmission dynamic models of pathogens that spread socially will improve the models’ realism. Data from population-based contact diaries in eight European countries from the POLYMOD study were projected to 144 other countries using a Bayesian hierarchical model that estimated the proclivity of age-and-location-specific contact patterns for the countries, using Markov chain Monte Carlo simulation. Household level data from the Demographic and Health Surveys for nine lower-income countries and socio-demographic factors from several on-line databases for 152 countries were used to quantify similarity of countries to estimate contact patterns in the home, work, school and other locations for countries for which no contact data are available, accounting for demographic structure, household structure where known, and a variety of metrics including workforce participation and school enrolment. Contacts are highly assortative with age across all countries considered, but pronounced regional differences in the age-specific contacts at home were noticeable, with more inter-generational contacts in Asian countries than in other settings. Moreover, there were variations in contact patterns by location, with work-place contacts being least assortative. These variations led to differences in the effect of social distancing measures in an age structured epidemic model. Contacts have an important role in transmission dynamic models that use contact rates to characterize the spread of contact-transmissible diseases. This study provides estimates of mixing patterns for societies for which contact data such as POLYMOD are not yet available.
Subfields: Agronomy and Crop Science, Epidemiology, Modeling and Simulation
View Paper: https://www.semanticscholar.org/paper/aa5d877d9a2f1ef7a8c9da901f047cc7763cad39
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Citations in the corpus
- Changes in contact patterns shape the dynamics of the COVID-19 outbreak in China (2020, background): “As lockdown measures are put in place in other locations, human mixing patterns in the outbreak period could be captured by data on within-household contacts, which are available for several countries around the world (5) (6) (7) (23) (24) (25) . Moving forward, it will be particularly important to ”
- The effect of control strategies to reduce social mixing on outcomes of the COVID-19 epidemic in Wuhan, China: a modelling study (2020, positive): “13 To examine how these changes in population mixing have affected the outbreak progression, we used synthetic location-specific contact patterns in Wuhan and adapted these in the presence of school closures, extended workplace closures, and reduction in mixing in the general community.”
- Covasim: An agent-based model of COVID-19 dynamics and interventions (2020, neutral): “Age-specific contact matrices (such as those in Prem et al., 2017) are then used to generate individuals and their expected contacts in a multilayer network framework.”