Networks of face-to-face social contacts in Niakhar, Senegal.

Gail E Potter,Jonathan Sugimoto,Aldiouma Diallo,Jimmy Wong,Kathleen Neuzil,M Elizabeth Halloran,John C Victor

doi:10.1371/journal.pone.0220443

Abstract

We present the first analysis of face-to-face contact network data from Niakhar, Senegal. Participants in a cluster-randomized influenza vaccine trial were interviewed about their contact patterns when they reported symptoms during their weekly household surveillance visit. We employ a negative binomial model to estimate effects of covariates on contact degree. We estimate the mean contact degree for asymptomatic Niakhar residents to be 16.5 (95% C.I. 14.3, 18.7) in the morning and 14.8 in the afternoon (95% C.I. 12.7, 16.9). We estimate that symptomatic people make 10% fewer contacts than asymptomatic people (95% C.I. 5%, 16%; p = 0.006), and those aged 0-5 make 33% fewer contacts than adults (95% C.I. 29%, 37%; p < 0.001). By explicitly modelling the partial rounding pattern observed in our data, we make inference for both the underlying (true) distribution of contacts as well as for the reported distribution. We created an estimator for homophily by compound (household) membership and estimate that 48% of contacts by symptomatic people are made to their own compound members in the morning (95% CI, 45%, 52%) and 60% in the afternoon/evening (95% CI, 56%, 64%). We did not find a significant effect of symptom status on compound homophily. We compare our findings to those from other countries and make design recommendations for future surveys.

Highlights

Social contacts occurring in close proximity are transmission pathways for respiratory infections such as influenza [1]
After GEP joined the Emmes Company, the sole support from Emmes for this social network analysis manuscript was in the form of salary support for GEP
We have summarized the degree distribution, homophily by compound membership, and location distribution of face-to-face social contacts in Niakhar, Senegal, based on two consecutive days of survey reports from 3,758 participants over a period of 6 months

Summary

Introduction

Social contacts occurring in close proximity are transmission pathways for respiratory infections such as influenza [1]. These contacts form social networks over which the disease may spread, and estimation of network structures can help improve estimates of transmission parameters, predict disease spread, and refine social distancing strategies. The Emmes Company was contracted to perform data cleaning and data analysis of the clinical trial data (but not the social contact network data) for this study before GEP joined the Emmes Company (in October 2015). The other funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

Methods

Results

Conclusion