Modelling development of epidemics with dynamic small-world networks.
We discuss the dynamics of a minimal model for spreading of infectious diseases, such as various types of influenza. The spreading takes place on a dynamic small-world network and can be viewed as comprising short- and long-range spreading processes. We derive approximate equations for the epidemic threshold as well as the spreading dynamics, and show that there is a good agreement with numerical discrete time-step simulations. We then analyse the dependence of the epidemic saturation time on the initial conditions, and outline a possible method of utilizing the model in predicting the development of epidemics based on early figures of infected. Finally, we compare time series calculated with our model to real-world data.
Saramäki
Jari
J
Laboratory of Computational Engineering, Helsinki University of Technology, P.O. Box 9203, FIN-02015 HUT, Finland. jsaramak@lce.hut.fi
Kaski
Kimmo
K
D003141:Communicable Diseases / Q000453:epidemiology* / Q000635:transmission; D004196:Disease Outbreaks; D006801:Humans; D007251:Influenza, Human / Q000453:epidemiology / Q000635:transmission; D015233:Models, Statistical; D016571:Neural Networks, Computer