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

Yu Z, Liu J, Zhu X - PLoS ONE (2015)

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: School of Computer Science and Engineering, South China University of Technology, Guangzhou, Guangdong, China.

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.

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Hierarchical district structure in Hong Kong.
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pone.0118085.g002: Hierarchical district structure in Hong Kong.

Mentions: The process of generating synthetic populations in our work is more complicated than that in [20], as we account for the synthetic population based on the hierarchical district structure as shown in Fig. 1, cross-district synthetic population and synthetic population corresponding to both district and age. Fig. 2 illustrates the hierarchical district structure in Hong Kong. Hong Kong (level 0) contains three big districts (level 1), Hong Kong Island, the Kowloon Peninsula and the New Territories, and each of which contains several smaller districts (level 2). Using the detailed social census data, we calculate the number of households in each district, the household compositions, the age of each household member, the activity status of each member, the place of study for persons attending full-time courses, school type in relation to age, working population in relation to place of work, work type in relation to age and working population in relation to occupation. For example, we use the census data related to households by district, household composition and size, households by the sex and age group of the head of the household, and population by district and age to partition individuals into households in different districts. The census data related to working population by district, place of work in relation to age group, persons attending full-time courses by district and places of study in relation to age group can be used to identify individuals who work or study in the same district or across different districts. Statistical data related to population by district, age group and economic activity status allow us to distribute individuals based on activities such as studying, working and staying at home. We use additional census data to generate synthetic populations, especially cross-district synthetic populations.


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

Yu Z, Liu J, Zhu X - PLoS ONE (2015)

Hierarchical district structure in Hong Kong.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4356714&req=5

pone.0118085.g002: Hierarchical district structure in Hong Kong.
Mentions: The process of generating synthetic populations in our work is more complicated than that in [20], as we account for the synthetic population based on the hierarchical district structure as shown in Fig. 1, cross-district synthetic population and synthetic population corresponding to both district and age. Fig. 2 illustrates the hierarchical district structure in Hong Kong. Hong Kong (level 0) contains three big districts (level 1), Hong Kong Island, the Kowloon Peninsula and the New Territories, and each of which contains several smaller districts (level 2). Using the detailed social census data, we calculate the number of households in each district, the household compositions, the age of each household member, the activity status of each member, the place of study for persons attending full-time courses, school type in relation to age, working population in relation to place of work, work type in relation to age and working population in relation to occupation. For example, we use the census data related to households by district, household composition and size, households by the sex and age group of the head of the household, and population by district and age to partition individuals into households in different districts. The census data related to working population by district, place of work in relation to age group, persons attending full-time courses by district and places of study in relation to age group can be used to identify individuals who work or study in the same district or across different districts. Statistical data related to population by district, age group and economic activity status allow us to distribute individuals based on activities such as studying, working and staying at home. We use additional census data to generate synthetic populations, especially cross-district synthetic populations.

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: School of Computer Science and Engineering, South China University of Technology, Guangzhou, Guangdong, China.

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.

Show MeSH
Related in: MedlinePlus