Brenner Frank, Marwan Norbert, Hoffmann Peter
Potsdam Institute for Climate Impact Research, Potsdam, Germany.
Eur Phys J Spec Top. 2017;226(9):1845-1856. doi: 10.1140/epjst/e2017-70028-2. Epub 2017 Jun 21.
In this study we combined a wide range of data sets to simulate the outbreak of an airborne infectious disease that is directly transmitted from human to human. The basis is a complex network whose structures are inspired by global air traffic data (from openflights.org) containing information about airports, airport locations, direct flight connections and airplane types. Disease spreading inside every node is realized with a Susceptible-Exposed-Infected-Recovered (SEIR) compartmental model. Disease transmission rates in our model are depending on the climate environment and therefore vary in time and from node to node. To implement the correlation between water vapor pressure and influenza transmission rate [J. Shaman, M. Kohn, Proc. Natl. Acad. Sci. , 3243 (2009)], we use global available climate reanalysis data (WATCH-Forcing-Data-ERA-Interim, WFDEI). During our sensitivity analysis we found that disease spreading dynamics are strongly depending on network properties, the climatic environment of the epidemic outbreak location, and the season during the year in which the outbreak is happening.
在本研究中,我们整合了大量数据集,以模拟一种在人与人之间直接传播的空气传播传染病的爆发。其基础是一个复杂网络,其结构受全球空中交通数据(来自openflights.org)启发,该数据包含有关机场、机场位置、直飞航班连接和飞机类型的信息。每个节点内的疾病传播通过易感-暴露-感染-康复(SEIR)分区模型实现。我们模型中的疾病传播率取决于气候环境,因此会随时间和节点的不同而变化。为了体现水汽压力与流感传播率之间的相关性[J. 沙曼,M. 科恩,《美国国家科学院院刊》,3243(2009)],我们使用了全球可用的气候再分析数据(全球气候观测网强迫数据-欧洲中期天气预报中心再分析数据,WFDEI)。在敏感性分析中,我们发现疾病传播动态在很大程度上取决于网络属性、疫情爆发地点的气候环境以及爆发发生的年份季节。