Stollenwerk Nico
Mathematics Department, Porto University, Portugal NIC, Research Center Jülich, Germany.
School of Biological Sciences, Royal Holloway University London, UK.
AIP Conf Proc. 2005 Jul 20;779(1):191-194. doi: 10.1063/1.2008613.
As opposed to most sociological fields, data are available in good quality for human epidemiology, describing the interaction between individuals being susceptible to or infected by a disease. Mathematically, the modelling of such systems is done on the level of stochastic master equations, giving likelihood functions for real live data. We show in a case study of meningococcal disease, that the observed large fluctuations of outbreaks of disease among the human population can be explained by the , leading the system towards a critical state, characterized by power laws in outbreak distributions. In order to make the extremely difficult parameter estimation close to a critical state with absorbing boundary possible, we investigate new algorithms for simulation of the disease dynamics on the basis of strategies, and combine them with previously developed parameter estimation schemes.
与大多社会学领域不同，人类流行病学有高质量的数据，这些数据描述了易感染某种疾病或已感染该疾病的个体之间的相互作用。从数学角度看，此类系统的建模是在随机主方程层面进行的，从而给出实际数据的似然函数。我们在一项关于脑膜炎球菌病的案例研究中表明，人群中观察到的疾病爆发的大幅波动可以用 来解释，这使得系统趋向于一个临界状态，其特征是爆发分布呈现幂律。为了使在具有吸收边界的临界状态附近进行极其困难的参数估计成为可能，我们研究了基于 策略的疾病动态模拟新算法，并将它们与先前开发的参数估计方案相结合。
