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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We present a decision support system for flood early warning and disaster management. It includes the models for datadriven meteorological predictions, for simulation of atmospheric pressure, wind, long sea waves and seiches; a module for optimization of flood barrier gates operation; models for stability assessment of levees and embankments, for simulation of city inundation dynamics and citizens evacuation scenarios. The novelty of this paper is a coupled distributed simulation of surface and subsurface flows that can predict inundation of low-lying inland zones far from the submerged waterfront areas, as observed in St. Petersburg city during the floods. All the models are wrapped as software services in the CLAVIRE platform for urgent computing, which provides workflow management and resource orchestration.

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