Abstract

Airborne infectious diseases such as influenza are primarily transmitted from human to human by means of social contacts, and thus easily spread within households. Epidemic models, used to gain insight into infectious disease spread and control, typically rely on the assumption of random mixing within households. Until now, there has been no direct empirical evidence to support this assumption. Here, we present the first social contact survey specifically designed to study contact networks within households. The survey was conducted in Belgium (Flanders and Brussels) from 2010 to 2011. We analysed data from 318 households totalling 1266 individuals with household sizes ranging from two to seven members. Exponential-family random graph models (ERGMs) were fitted to the within-household contact networks to reveal the processes driving contact between household members, both on weekdays and weekends. The ERGMs showed a high degree of clustering and, specifically on weekdays, decreasing connectedness with increasing household size. Furthermore, we found that the odds of a contact between older siblings and between father and child are smaller than for any other pair. The epidemic simulation results suggest that within-household contact density is the main driver of differences in epidemic spread between complete and empirical-based household contact networks. The homogeneous mixing assumption may therefore be an adequate characterization of the within-household contact structure for the purpose of epidemic simulations. However, ignoring the contact density when inferring based on an epidemic model will result in biased estimates of within-household transmission rates. Further research regarding the implementation of within-household contact networks in epidemic models is necessary.

Highlights

  • Households are crucial units in the epidemiology of airborne infectious diseases such as influenza, smallpox and SARS

  • The former estimates were based on partially observed within-household contact networks and likely underestimated the true proportion of complete networks

  • We analysed the household network data using Exponentialfamily random graph models (ERGMs) to assess the effect of factors such as role in the household, gender, children’s age and household size on physical contacts within households

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Summary

Introduction

Households are crucial units in the epidemiology of airborne infectious diseases such as influenza, smallpox and SARS. Relations between household members are typically characterized by frequent and intimate contacts, allowing for rapid disease spread within the household upon introduction of an infectious case. As stated by Ferguson et al [1], ‘being a member of a household containing an influenza case is the largest single risk factor for being infected oneself’ 450, citing [2,3]). Households with children have a bridging function, allowing for an infection to spread from schools to workplaces, and vice versa. Inference from household final size data revealed that children play a key role in bringing influenza infection into the household.

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