Document Type

Journal Article

Department/Unit

Department of Computer Science

Title

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

Language

English

Abstract

© 2015 Yu et al. 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.

Publication Date

2015

Source Publication Title

PLoS ONE

Volume

10

Issue

2

Start Page

e0118085

Publisher

Public Library of Science

DOI

10.1371/journal.pone.0118085

ISSN (print)

19326203

ISSN (electronic)

19326203

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