Maeno Yoshiharu
Social Design Group, 2-32-11 Sengoku, 112-0011 Bunkyo-ku, Tokyo, Japan.
Physica A. 2011 Oct 1;390(20):3412-3426. doi: 10.1016/j.physa.2011.05.005. Epub 2011 May 12.
This study presents a method to discover an outbreak of an infectious disease in a region for which data are missing, but which is at work as a disease spreader. Node discovery for the spread of an infectious disease is defined as discriminating between the nodes which are neighboring to a missing disease spreader node, and the rest, given a dataset on the number of cases. The spread is described by stochastic differential equations. A perturbation theory quantifies the impact of the missing spreader on the moments of the number of cases. Statistical discriminators examine the mid-body or tail-ends of the probability density function, and search for the disturbance from the missing spreader. They are tested with computationally synthesized datasets, and applied to the SARS outbreak and flu pandemic.
本研究提出了一种方法，用于在数据缺失但作为疾病传播源的地区发现传染病疫情。传染病传播的节点发现被定义为，在给定病例数数据集的情况下，区分与缺失的疾病传播源节点相邻的节点和其他节点。传播情况由随机微分方程描述。一种微扰理论量化了缺失传播源对病例数矩的影响。统计判别器检查概率密度函数的中间部分或尾部，并搜索来自缺失传播源的干扰。它们通过计算合成数据集进行测试，并应用于非典疫情和流感大流行。
