Identifying Spatial Invasion of Pandemics on Metapopulation Networks Via Anatomizing Arrival History.
Identifying Spatial Invasion of Pandemics on Metapopulation Networks Via Anatomizing Arrival History.
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DOI：
10.1109/tcyb.2015.2489702
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发表时间：
2016-12
影响因子：
11.8
通讯作者：
Li X
中科院分区：
文献类型：
--
作者：
Wang JB;Wang L;Li X
传染病在人群中通过人员流动的空间传播具有高度随机性和异质性。通过使用统计或机械模型，通常难以实现对传播过程的准确预测/挖掘。在这里，我们提出了一个新的反向问题，其目的是确定随机的空间传播过程本身从可观察到的信息在每个亚群中的传染病病例的到达历史。我们提出了一种基于动态规划的优化算法，该算法包括三个步骤：1）将所有亚种群之间的传播过程分解为不相交的组分斑块，2）通过最大似然估计来推断每个斑块下的最可能入侵途径，3）通过最大似然估计来确定每个斑块下的最可能入侵途径。以及3）通过迭代地组装每个补丁中的入侵路径来恢复整个过程，而无需参数校准和计算机模拟的负担。基于熵理论，我们引入了一个可识别性测度来评估入侵途径识别的难度。人工和经验集合种群网络的结果表明，在识别驱动大流行传播的实际入侵途径方面具有强大的性能。
Spatial spread of infectious diseases among populations via the mobility of humans is highly stochastic and heterogeneous. Accurate forecast/mining of the spread process is often hard to be achieved by using statistical or mechanical models. Here we propose a new reverse problem, which aims to identify the stochastically spatial spread process itself from observable information regarding the arrival history of infectious cases in each subpopulation. We solved the problem by developing an efficient optimization algorithm based on dynamical programming, which comprises three procedures: 1) anatomizing the whole spread process among all subpopulations into disjoint componential patches; 2) inferring the most probable invasion pathways underlying each patch via maximum likelihood estimation; and 3) recovering the whole process by assembling the invasion pathways in each patch iteratively, without burdens in parameter calibrations and computer simulations. Based on the entropy theory, we introduced an identifiability measure to assess the difficulty level that an invasion pathway can be identified. Results on both artificial and empirical metapopulation networks show the robust performance in identifying actual invasion pathways driving pandemic spread.