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.
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| http://dx.doi.org/10.1063/1.2008613 | DOI Listing |
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