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Influences on influenza transmission within terminal based on hierarchical structure of personal contact network

Overview of attention for article published in BMC Public Health, March 2015
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Title
Influences on influenza transmission within terminal based on hierarchical structure of personal contact network
Published in
BMC Public Health, March 2015
DOI 10.1186/s12889-015-1536-5
Pubmed ID
Authors

Quan Shao, Meng Jia

Abstract

Since the outbreak of pandemics, influenza has caused extensive attention in the field of public health. It is actually hard to distinguish what is the most effective method to control the influenza transmission within airport terminal. The purpose of this study was to quantitatively evaluate the influences of passenger source, immunity difference and social relation structure on the influenza transmission in terminal. A method combining hierarchical structure of personal contact network with agent-based SEIR model was proposed to analyze the characteristics of influenza diffusion within terminal. Based on the spatial distance between individuals, the hierarchical structure of personal contact network was defined to construct a complex relationship of passengers in the real world. Moreover, the agent-based SEIR model was improved by considering the individual level of influenza spread characteristics. To evaluate the method, this process was fused in simulation based on the constructed personal contact network. In the terminal we investigated, personal contact network was defined by following four layers: social relation structure, procedure partition, procedure area, and the whole terminal. With the growing of layer, the degree distribution curves move right. The value of degree distribution p(k) reached a peak at a specific value, and then back down. Besides, with the increase of layer α, the clustering coefficients presented a tendency to exponential decay. Based on the influenza transmission experiments, the main infected areas were concluded when considering different factors. Moreover, partition of passenger sources was found to impact a lot in departure, while social relation structure imposed a great influence in arrival. Besides, immunity difference exerted no obvious effect on the spread of influenza in the transmission process both in departure and arrival. The proposed method is efficient to reproduce the evolution process of influenza transmission, and exhibits various roles of each factor in different processes, also better reflects the effect of passenger topological character on influenza spread. It contributes to proposing effective influenza measures by airport relevant department and improving the efficiency and ability of epidemic prevention on the public health.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 29 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 7%
Indonesia 1 3%
Switzerland 1 3%
Unknown 25 86%

Demographic breakdown

Readers by professional status Count As %
Researcher 7 24%
Student > Ph. D. Student 6 21%
Student > Master 5 17%
Student > Postgraduate 2 7%
Other 2 7%
Other 4 14%
Unknown 3 10%
Readers by discipline Count As %
Medicine and Dentistry 6 21%
Biochemistry, Genetics and Molecular Biology 2 7%
Veterinary Science and Veterinary Medicine 2 7%
Psychology 2 7%
Agricultural and Biological Sciences 2 7%
Other 8 28%
Unknown 7 24%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 08 April 2015.
All research outputs
#15,391,100
of 19,214,062 outputs
Outputs from BMC Public Health
#10,951
of 12,665 outputs
Outputs of similar age
#167,746
of 234,549 outputs
Outputs of similar age from BMC Public Health
#1
of 1 outputs
Altmetric has tracked 19,214,062 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 12,665 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 13.4. This one is in the 6th percentile – i.e., 6% of its peers scored the same or lower than it.
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