论文标题
基于传感器的流行资源在人类流动网络上的定位
Sensor-based localization of epidemic sources on human mobility networks
论文作者
论文摘要
我们研究流行病学中的源检测问题，这是控制流行病的最重要问题之一。从数学上讲，我们将问题重新制定为在多元高斯混合模型中识别相关组件之一。为了研究具有类似传播模式的霍乱和疾病，我们使用在随机的，空间上显式的流行病学模型中使用人类迁移率网络来校准混合物模型的参数。此外，我们采用了贝叶斯的观点，因此可以合并有关源位置的先前信息（例如，反映了当地条件的影响）。进行基于后验的推断，允许以各个位置或区域的形式进行估计。重要的是，我们的估计量仅需要推定的观察者的首次排放时间，通常仅位于一小部分节点。在南非夸祖鲁 - 纳塔尔省的2000 - 2002年霍乱爆发的背景下，提出了该方法。
We investigate the source detection problem in epidemiology, which is one of the most important issues for control of epidemics. Mathematically, we reformulate the problem as one of identifying the relevant component in a multivariate Gaussian mixture model. Focusing on the study of cholera and diseases with similar modes of transmission, we calibrate the parameters of our mixture model using human mobility networks within a stochastic, spatially explicit epidemiological model for waterborne disease. Furthermore, we adopt a Bayesian perspective, so that prior information on source location can be incorporated (e.g., reflecting the impact of local conditions). Posterior-based inference is performed, which permits estimates in the form of either individual locations or regions. Importantly, our estimator only requires first-arrival times of the epidemic by putative observers, typically located only at a small proportion of nodes. The proposed method is demonstrated within the context of the 2000-2002 cholera outbreak in the KwaZulu-Natal province of South Africa.
