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

Authors :
Ozella, Laura
Paolotti, Daniela
Lichand, Guilherme
Rodríguez, Jorge P.
Haenni, Simon
Phuka, John
Leal-Neto, Onicio B.
Cattuto, Ciro
Source :
EPJ Data Science; 9/8/2021, Vol. 10 Issue 1, p1-17, 17p
Publication Year :
2021

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. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
21931127
Volume :
10
Issue :
1
Database :
Complementary Index
Journal :
EPJ Data Science
Publication Type :
Academic Journal
Accession number :
152351293
Full Text :
https://doi.org/10.1140/epjds/s13688-021-00302-w