We propose a new way to model HIV infection spreading through the use of dynamic complex networks. The heterogeneous population of HIV exposure groups is described through a unique network degree probability distribution. The time evolution of the network nodes is modelled by a Markov process and gives insight in HIV disease progression. The results are validated against historical data of AIDS cases in the USA as recorded by the Center of Disease Control. We find a remarkably good correspondence between the number of simulated and registered HIV cases, indicating that our approach to modelling the dynamics of HIV spreading through a sexual network is a valid approach that opens up completely new ways of reasoning about various medication scenarios.

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In this paper we present a computational algorithm aimed at fitting a SEIR populational model to the influenza outbreaks incidence in Russian cities. The input data are derived from the long-term records on the incidence of acute respiratory diseases in Moscow, St. Petersburg, and Novosibirsk. It is shown that the classical SEIR model could provide a satisfactory fit for the majority of employed influenza outbreak incidence data sets (

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