Full Text:   <1378>

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CLC number: TP391.4

On-line Access: 2016-05-04

Received: 2015-10-16

Revision Accepted: 2016-02-17

Crosschecked: 2016-04-28

Cited: 0

Clicked: 3203

Citations:  Bibtex RefMan EndNote GB/T7714


Xi-chuan Zhou


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Frontiers of Information Technology & Electronic Engineering  2016 Vol.17 No.5 P.413-421


Global influenza surveillance with Laplacian multidimensional scaling

Author(s):  Xi-chuan Zhou, Fang Tang, Qin Li, Sheng-dong Hu, Guo-jun Li, Yun-jian Jia, Xin-ke Li, Yu-jie Feng

Affiliation(s):  College of Communications Engineering, Chongqing University, Chongqing 400044, China; more

Corresponding email(s):   zxc@cqu.edu.cn

Key Words:  Surveillance gap, Influenza, Spatial-transmission model

Xi-chuan Zhou, Fang Tang, Qin Li, Sheng-dong Hu, Guo-jun Li, Yun-jian Jia, Xin-ke Li, Yu-jie Feng. Global influenza surveillance with Laplacian multidimensional scaling[J]. Frontiers of Information Technology & Electronic Engineering, 2016, 17(5): 413-421.

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journal="Frontiers of Information Technology & Electronic Engineering",
publisher="Zhejiang University Press & Springer",

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%A Xi-chuan Zhou
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T1 - Global influenza surveillance with Laplacian multidimensional scaling
A1 - Xi-chuan Zhou
A1 - Fang Tang
A1 - Qin Li
A1 - Sheng-dong Hu
A1 - Guo-jun Li
A1 - Yun-jian Jia
A1 - Xin-ke Li
A1 - Yu-jie Feng
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DOI - 10.1631/FITEE.1500356

The Global influenza Surveillance Network is crucial for monitoring epidemic risk in participating countries. However, at present, the network has notable gaps in the developing world, principally in Africa and Asia where laboratory capabilities are limited. Moreover, for the last few years, various influenza viruses have been continuously emerging in the resource-limited countries, making these surveillance gaps a more imminent challenge. We present a spatial-transmission model to estimate epidemic risks in the countries where only partial or even no surveillance data are available. Motivated by the observation that countries in the same influenza transmission zone divided by the World Health Organization had similar transmission patterns, we propose to estimate the influenza epidemic risk of an unmonitored country by incorporating the surveillance data reported by countries of the same transmission zone. Experiments show that the risk estimates are highly correlated with the actual influenza morbidity trends for African and Asian countries. The proposed method may provide the much-needed capability to detect, assess, and notify potential influenza epidemics to the developing world.

This manuscript proposes a spatial transmission model based on Laplacian Multidimensional Scaling to estimate the epidemic risk in countries (or regions) with limited surveillance data. The estimate of epidemic risk in an unmonitored country (or region) is obtained by incorporating the surveillance data reported in countries (or regions) in the same zone divided by the WHO. This method is useful not only for influenza, in which data are lacking in certain countries (or regions), it could also be applied to other infectious diseases. The paper is well written.




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