A1470
Title: Inferring age-stratified social networks from contact data with Gaussian mixtures
Authors: Luke Murray Kearney - University of Warwick (United Kingdom) [presenting]
Emma Davis - University of Warwick (United Kingdom)
Matt J Keeling - University of Warwick (United Kingdom)
Abstract: Capturing the structure of a population and characterising contacts within the population are key to reliable projections of infectious diseases. Two main elements of population structure, contact heterogeneity and age, have been repeatedly demonstrated to be key in infection dynamics, yet are rarely combined. While there are a few key examples of contact networks being measured explicitly, in general, there is a need to construct the appropriate surrogate networks from individual-level data. Using data from open-source social contact surveys, an algorithm is developed to generate an extrapolated network that preserves both age-structured mixing and heterogeneity in the number of contacts. The spread of infection is then simulated through the population, constrained to have a given basic reproduction number, R0. Given the over-dominant role that highly connected nodes (`superspreaders') would otherwise play in early dynamics, transmission is scaled by the duration of contacts, providing a better match to surveillance data for numbers of secondary cases. Showing that for COVID-like parameters, including both heterogeneity and age-structure, reduces epidemic size. A robust methodology, therefore, allows for the inclusion of the full wealth of data commonly collected by surveys but frequently overlooked to be incorporated into more realistic transmission models of infectious diseases.
