The impact of household physical distancing and its timing on the transmission of SARS-CoV-2

ABSTRACT
Understanding how within-household contact networks form and evolve is essential for accurately modeling infectious disease transmission. In household studies, contact network data are typically collected from samples, yet this aspect is often not accounted for in statistical network models. In the first part of this talk, we introduce a flexible framework for jointly modeling multiple networks using Exponential Random Graph Models, specifically developed to properly incorporate and integrate information from sampled networks. We then apply this framework to analyze detailed contact patterns in280 U.S. households with SARS-CoV-2 index cases, combining longitudinal symptom and contact data and using ERGMs to better understand behavioral responses following infection awareness. By calibrating a two-level mixing model to observed infection data, we demonstrate that behavioral changes, particularly reducing physical contact at the onset of symptoms, can significantly limit household transmission, decreasing secondary infections by more than 25% in households of 4 to 5 members. This highlights how rigorous network modeling can improve predictive simulations and support targeted interventions at the household level.