An algorithm to build synthetic temporal contact networks based on close-proximity interactions data.

ABSTRACT: Small populations (e.g., hospitals, schools or workplaces) are characterised by high contact heterogeneity and stochasticity affecting pathogen transmission dynamics. Empirical individual contact data provide unprecedented information to characterize such heterogeneity and are increasingly available, but are usually collected over a limited period, and can suffer from observation bias. We propose an algorithm to stochastically reconstruct realistic temporal networks from individual contact data in healthcare settings (HCS) and test this approach using real data previously collected in a long-term care facility (LTCF). Our algorithm generates full networks from recorded close-proximity interactions, using hourly inter-individual contact rates and information on individuals' wards, the categ

SUBMITTER: Duval A 

PROVIDER: S-EPMC11207132 | biostudies-literature | 2024 Jun

REPOSITORIES: biostudies-literature