Using wearable proximity sensors to characterize social contact patterns in a village of rural Malawi

Abstract

Measuring close proximity interactions between individuals can provide key information on social contacts in human communities and related behaviours. This is even more essential in rural settings in low- and middle-income countries where there is a need to understand contact patterns for the implementation of strategies for social protection interventions. We report the quantitative assessment of contact patterns in a village in rural Malawi, based on proximity sensors technology that allows for high-resolution measurements of social contacts. Our results revealed that the community structure of the village was highly correlated with the household membership of the individuals, thus confirming the importance of the family ties within the village. Social contacts within households occurred mainly between adults and children, and adults and adolescents and most of the inter-household social relationships occurred among adults and among adolescents. At the individual level, age and gender social assortment were observed in the inter-household network, and age disassortativity was instead observed in intra-household networks. Moreover, we obtained a clear trend of the daily contact activity of the village. Family members congregated in the early morning, during lunch time and dinner time. In contrast, inter-household contact activity displayed a growth from the morning, reaching a maximum in the afternoon.The proximity sensors technology used in this study provided high resolution temporal data characterized by timescales comparable with those intrinsic to social dynamics and it thus allowed to have access to the level of information needed to understand the social context of the village.

Correlations of network trajectories

“…Empirical networks -To round off, we now apply our methodology to a range of different empirical temporal networks that characterize evolving interaction patterns in different systems, including online (email networks [31]) and offline social interaction in different settings (proximity networks in a university [25], a hospital ward [29], primary [26] and high school [27], interactions in a village [28]), transportation networks (NY subway, US air traffic [32]), and biological systems (protein interactions [30]), see appendix for details. Across these systems we find a wide range of emerging stylized correlation patterns that match the prototypical structures found in the synthetic models, from pure periodicitywhich highlights temporally pulsating networks-to both short-range and long-range correlation structures.…”

Complex systems for the most vulnerable

“…Countless studies have used the data to study human behavior and to develop models for the transmission of infectious diseases. More recently, this approach has been used to measure household contacts in rural Africa [94,95].…”

Inferring Tie Strength in Temporal Networks

“…We use the following real-world data sets: Malawi [36], Copresence [10], and Primary [43] are human contact networks. Enron [24] and Yahoo [40] are communication networks.…”