Inferring a district-based hierarchical structure of social contacts from census data.

Zhiwen Yu,Jiming Liu,Xianjun Zhu,Frederic Amblard

doi:10.1371/journal.pone.0118085

Zhiwen Yu, Jiming Liu + Show 2 more

Open Access

https://doi.org/10.1371/journal.pone.0118085

Copy DOI

Abstract

Researchers have recently paid attention to social contact patterns among individuals due to their useful applications in such areas as epidemic evaluation and control, public health decisions, chronic disease research and social network research. Although some studies have estimated social contact patterns from social networks and surveys, few have considered how to infer the hierarchical structure of social contacts directly from census data. In this paper, we focus on inferring an individual’s social contact patterns from detailed census data, and generate various types of social contact patterns such as hierarchical-district-structure-based, cross-district and age-district-based patterns. We evaluate newly generated contact patterns derived from detailed 2011 Hong Kong census data by incorporating them into a model and simulation of the 2009 Hong Kong H1N1 epidemic. We then compare the newly generated social contact patterns with the mixing patterns that are often used in the literature, and draw the following conclusions. First, the generation of social contact patterns based on a hierarchical district structure allows for simulations at different district levels. Second, the newly generated social contact patterns reflect individuals social contacts. Third, the newly generated social contact patterns improve the accuracy of the SEIR-based epidemic model.

Highlights

Researchers have recently paid attention to capturing individuals social contacts and social network structures due to their useful applications in the social sciences, ecology, health care, communications, economic sociology [2] [1] [9] [5] [3] [10] and especially epidemic models for infectious disease transmission [17] [16] [15] [4]
We propose a new third-category statistical approach to model a citys population; identify the social contact patterns of the hierarchical social structure at the population level, which are inferred from the detailed census data; and determine hierarchical-districtstructure-based, cross-district and age-district-based social contact patterns
We evaluate the hierarchical structure of social contacts in a simulation of the 2009 Hong Kong H1N1 epidemic to improve the accuracy of the epidemic model

Summary

Introduction

Researchers have recently paid attention to capturing individuals social contacts and social network structures due to their useful applications in the social sciences, ecology, health care, communications, economic sociology [2] [1] [9] [5] [3] [10] and especially epidemic models for infectious disease transmission [17] [16] [15] [4]. Many studies have examined the relationships among individuals and social network structures, few [20] have considered how to infer the social contact patterns of a hierarchical social structure at the population level directly from census data using a statistical approach. Every kind of social demographical profile contains a hierarchical structure of social contacts and cross-district contact patterns. Inferring a Hierarchical Structure of Social Contacts from Census Data (project No S2013050014677), a grant from the key lab of cloud computing and big data in Guangzhou (Project No.SITGZ[2013]268-6), A grant from Key Enterprises and Innovation Organizations in Nanshan District in Shenzhen (Project No KC2013ZDZJ0007A), and Hong Kong Baptist University (Project No RGC/HKBU211212). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

Methods

Results

Conclusion