Power law approximations of movement network data for modeling infectious disease spread
Power law approximations of movement network data for modeling infectious disease spread
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DOI：
10.1002/bimj.201200262
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发表时间：
2014-05-01
影响因子：
1.7
通讯作者：
Godtliebsen, Fred
中科院分区：
文献类型：
--
作者：
Geilhufe, Marc;Held, Leonhard;Godtliebsen, Fred
全球化和个人流动性的增加使人与人之间传播的传染病能够更快地传播到世界各地的遥远地方，使传播的良好模式变得越来越重要。我们研究了在挪威北部的偏远地区和人口稀少的地区在不同的模型与固定，季节性和随机效应的传播的时空模式。这些模型应用于甲型流感计数，使用来自阳性微生物学实验室检测的数据作为潜在疾病发病率的代理。与当地的航空，公路和海上交通数据的人类出行模式被纳入以及幂律近似，无论是准泊松回归和相关市政当局的邻接结构的基础上。我们调查模型扩展使用的信息，积极的实验室检测的比例，从挪威北部以外的移民数据，并通过连接人口的运动网络。此外，我们对非成人和成人进行了两项单独的分析，因为儿童是甲型流感的重要驱动因素。一步预测的比较通常会产生更好的或可比的结果使用幂律近似。
Globalization and increased mobility of individuals enable person-to-person transmitted infectious diseases to spread faster to distant places around the world, making good models for the spread increasingly important. We study the spatiotemporal pattern of spread in the remotely located and sparsely populated region of North Norway in various models with fixed, seasonal, and random effects. The models are applied to influenza A counts using data from positive microbiology laboratory tests as proxy for the underlying disease incidence. Human travel patterns with local air, road, and sea traffic data are incorporated as well as power law approximations thereof, both with quasi-Poisson regression and based on the adjacency structure of the relevant municipalities. We investigate model extensions using information about the proportion of positive laboratory tests, data on immigration from outside North Norway and by connecting population to the movement network. Furthermore, we perform two separate analyses for nonadults and adults as children are an important driver for influenza A. Comparisons of one-step-ahead predictions generally yield better or comparable results using power law approximations.