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| Global influenza surveillance with Laplacian multidimensional scaling |
| Xi-chuan Zhou, Fang Tang, Qin Li, Sheng-dong Hu, Guo-jun Li, Yun-jian Jia, Xin-ke Li, Yu-jie Feng |
| College of Communications Engineering, Chongqing University, Chongqing 400044, China; MOE Key Laboratory of Dependable Service Computing in Cyber Physical Society, Chongqing 400044, China; Chongqing Engineering Laboratory of High Performance Integrated Circuits, Chongqing University, Chongqing 400044, China; Centers for Disease Control and Prevention of Chongqing, Chongqing 400042, China; Chongqing Communication Institute, Chongqing 400030, China; Third Military Medical University, Southwest Hospital, Chongqing 400030, China |
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Abstract 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.
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Received: 16 October 2015
Published: 04 May 2016
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基于多维尺度拉普拉斯分析方法的全球流感疫情监测
目的：实现全世界范围的流感疫情监测，重点是对部分WHO缺失数据的非洲国家地区的监测。
创新点：利用相同传染区传染病的传播具有相似性的特点，实现了对数据缺失的国家地区流感疫情数据的补足和预测，以及全世界范围的流感疫情监测。
方法：收集全世界范围内WHO历年来的监测数据，包括8种不同类型流感的每周感染人数。根据全世界范围内不同地区传染模式的多样性和相同地区传染模式的相似性建立模型。建立了传播相似性矩阵。根据同一传播区国家的相似性便可以得到数据缺失国家地区的流感疫情状态。
结论：针对不同流感传播区的国家地区建立了一个空间相关的流感疫情监测系统。该系统可以有效监测一些无WHO数据的非洲国家地区的疫情风险。
关键词：
监测缺口,
流感,
空间传染模型
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