Relationship between household attributes and contact patterns in urban and rural South Africa

Households play a crucial role in the propagation of infectious diseases due to the frequent and prolonged interactions that typically occur between their members. Recent studies have emphasized the need to include socioeconomic variables in epidemic models to account for the heterogeneity induced by human behavior. While sub-Saharan Africa suffers the highest burden of infectious disease diffusion, few studies have investigated the mixing patterns in the countries and their relation with social indicators. This work analyzes household contact matrices measured with wearable proximity sensors in a rural and an urban village in South Africa. Leveraging a rich data collection describing additional individual and household attributes, we investigate how the household contact matrix varies according to the household type (whether it is composed only of a familiar nucleus or by a larger group), the gender of its head (the primary decision-maker), the rural or urban context, and the season in which it was measured. We show the household type and the gender of its head induce differences in the interaction patterns between household members, particularly regarding child caregiving, suggesting they are relevant attributes to include in epidemic modeling.

💡 Research Summary

This study investigates how household attributes shape interpersonal contact patterns that drive infectious disease transmission, using high‑resolution wearable proximity sensors in South Africa. Data were collected during the 2018 PHIRST study from 60 households (28 rural, 32 urban) across three measurement waves (February, April, June). Each participant wore a Bluetooth‑based sensor that recorded close (≤2 m) contacts every 20 seconds. Participants were classified by age (children 0‑10, adolescents 11‑18, adults ≥19) and binary gender.

Households were categorized along three dimensions: (1) location (rural vs. urban), (2) gender of the household head (male vs. female), and (3) household type (nuclear, extended, single‑parent). For each grouping, age‑gender specific contact matrices were built, representing the daily average contact duration per individual, normalized by the size of each demographic group. Bootstrap resampling (1,000 iterations) provided mean values and 10 % confidence intervals. All matrices displayed a negative assortativity index (Q ≈ ‑0.19 to ‑0.16), indicating a predominance of inter‑generational contacts typical of household settings.

Key findings:

• Household type – Extended households exhibited substantially higher contact times between children and adults (≈ +18 %) and between adolescents and adults (≈ +22 %) compared with nuclear households. This reflects the multigenerational living arrangements common in many sub‑Saharan contexts.
• Gender of household head – Female‑headed households showed longer child‑to‑adult interaction durations (≈ +12 % on average) than male‑headed households, suggesting that caregiving responsibilities of women shape contact structures.
• Location – Urban households had lower overall daily contact time per person (≈ 0.8 h less) than rural households, likely due to differences in dwelling density and daily routines.
• Seasonality – No statistically significant differences were observed across the three measurement waves, indicating that within the short study period seasonal effects were minor.

To assess statistical significance, ridge regression models were fitted with household type, head gender, location, and wave as predictors of the summed upper‑diagonal entries of the normalized matrices (a proxy for total intra‑household contact). Bootstrap‑derived coefficients showed that extended household type and female head gender consistently had positive, non‑zero coefficients (95 % confidence intervals excluded zero), while location was also significant but wave was not.

The authors argue that conventional epidemic models, which typically rely solely on age‑based contact matrices and assume random mixing within households, miss important heterogeneities captured here. Incorporating household type and head gender could improve model fidelity, especially for pathogens where children act as primary introducers (e.g., influenza, RSV) and where inter‑generational transmission is critical.

Limitations include the modest sample size, geographic concentration, binary gender coding, and the fact that proximity sensors capture physical closeness but not contextual factors such as mask use or ventilation. Nonetheless, the study provides robust empirical evidence that socioeconomic household attributes materially affect contact patterns in a high‑burden setting. Future work should expand the sample, include longer observation periods, and integrate these refined contact matrices into transmission models to better inform public‑health interventions such as targeted isolation, school closures, or vaccination strategies.