基于自治计算的流行病传播网络建模与推断

现有的传播网络结构推断方法大都面向信息传播过程,所能处理的数据与可获得的流行病监控数据形式和特性均不相同,不适合处理具有粗粒度、时空多尺度和数据缺失等特性的流行病监控数据.针对该问题,提出了基于自治计算的流行病传播网络建模方法和网络结构推断方法.该方法采用多自治体建模传播网络结构和流行病传播过程,采用蒙特卡罗模拟结合群智能优化的反馈过程调节系统参数,以缩小模拟系统涌现行为与真实监控数据间差异为目标,改变自治体的行为,促使模拟系统向真实系统逐步演化,以此方式推断出传播网络结构及与流行病相关的主要生物学参数.采用2009年H1N1猪流感在香港爆发的真实监控数据分析验证了所提出的模型与方法的有效性和适用情况,并以香港地区流行病风险评估为例介绍了流行病传播网络推断的一种应用模式.

Modeling and Inferring Epidemic Networks Based on Autonomy Oriented Computing

In previous research, most related works on inferring the structures of diffusion networks are designed for recovering the process of information propagation. The learning data adopted by these works is distinct in terms of both format and features from the available surveillance data of epidemics. Therefore, the existing methods are not competent when dealing with epidemic surveillance data with some intractable properties such as coarse granularity, spatial and temporal multi-scale, and incompleteness. To address this issue, an AOC (autonomy oriented computing) based method is proposed to model epidemic networks, as well as to infer their structures from epidemic surveillance data. In this method, the structure of an epidemic network and the process of disease spread are modeled by an autonomous multi-agent system named D-AOC, and the parameters of the system are automatically estimated by a self-discovery process. During this process, the parameters are adjusted and thereafter, the behaviors of agents are updated by a feedback mechanism which combines the Monte Carlo simulation and swarm intelligence. The objective is to reduce the difference between emergent behavior of the D-AOC and observed surveillance data. Regulated by the feedback mechanism, it is expected that the D-AOC will keep evolving toward the real system to be simulated. In this way, the structure of epidemic network and main biological features related to the epidemic will finally be recovered. The effectiveness and applicability of the proposed method have been validated and discussed by analyzing the real surveillance data of the H1N1 swine-flu in Hong Kong during 2009. Moreover, one scenario of applying epidemic network inference is also demonstrated by a case study of epidemic risk assessment in Hong Kong.