SUPERCOMPUTER SIMULATION OF CRITICAL PHENOMENA IN COMPLEX SOCIAL SYSTEMS

The paper describes a problem of computer simulation of critical phenomena in complex social systems on a petascale computing systems in frames of complex networks approach. The three-layer system of nested models of complex networks is proposed including aggregated analytical model to identify critical phenomena, detailed model of individualized network dynamics and model to adjust a topological structure of a complex network. The scalable parallel algorithm covering all layers of complex networks simulation is proposed. Performance of the algorithm is studied on different supercomputing systems. The issues of software and information infrastructure of complex networks simulation are discussed including organization of distributed calculations, crawling the data in social networks and results visualization. The applications of developed methods and technologies are considered including simulation of criminal networks disruption, fast rumors spreading in social networks, evolution of financial networks and epidemics spreading

Modeling the dynamics of population immunity to influenza in Russian cities

Prediction models which will explicitly include the immunity levels of the population are required to plan effective measures for the containment of seasonal epidemics of influenza. The aim of the current work is to develop an approach to herd immunity dynamics modeling, with the long–term goal of employing it as a part of multicomponent model of influenza incidence dynamics. Based on serological studies performed for 52 Russian cities and 2 virus strains (A(H1N1)pdm09, A(H3N2)) in 11 years period, we propose statistical models which allow to analyze and predict the strain–specific immunity dynamics

Analyzing the spatial distribution of individuals predisposed to arterial hypertension in Saint Petersburg using synthetic populations

Arterial hypertension (AH) is one of the most common cardiovascular diseases, and it can lead to serious complications. To optimize the delivery of patients exposed to AH to medical institutions and thus to curtail mortality in Russian cities caused by the consequences of hypertension, it is necessary to estimate the number of potential patients, along with their spatial distribution. This paper presents a method which uses synthetic population data to assess the spatial distribution of individuals potentially prone to arterial hypertension. The risk of arterial hypertension of an individual is calculated based on its demographic characteristics (age and gender). Using Saint Petersburg as a case study, we demonstrate that the mentioned approach makes it possible to perform predictions of AH cases distribution in absence of real data on hypertension status of the individuals. The results of the study will be used to assess the input flows of patients to healthcare facilities and optimize their workflow

A Decision Support Framework for Periprosthetic Joint Infection Treatment: A Cost-Effectiveness Analysis Using Two Modeling Approaches

Today, periprosthetic joint infection (PJI) is one of the leading indications for revision surgery and the most ominous complication in artificial joint patients. The current state of the art for treating PJI requires the development of methods for planning the costs at different scales to facilitate the selection of the best treatment methods. In this paper, we perform a cost-effectiveness assessment for strategies related to the treatment of PJI using a composite decision support modeling framework. Within the framework, two models are implemented: a detailed discrete-event probabilistic model based on the decision tree approach and a dynamic Markov model with generalized states. The application of the framework is demonstrated on the dataset which was provided by the Russian Scientific Research Institute of Traumatology and Orthopedics named after R.R. Vreden. The analyzed dataset contains 600 patient records divided into two groups (retrospective group, based on old records, and prospective group, based on real-time follow-up). The cost-effectiveness of treatment methods was compared based on associated costs and QALY units gained, with the mentioned two indicators calculated using two models independently from each other. As a result, two comparative rankings of cost-effectiveness of PJI treatment methods were presented based on the model output

PREDICTION OF FLU EPIDEMIC PEAKS IN ST. PETERSBURG THROUGH POPULATION-BASED MATHEMATICAL MODELS

The paper presents two methods of predicting the peak of influenza epidemics using population-based mathematical models: Baroyan-Rvachev and modified Kermack-McKendrick model, proposed by the authors. We perform the comparison of the prediction accuracy of time and the value of epidemics peaks on long-term data of ARI incidence in the city of St. Petersburg. The methodology of comparison is based on three criteria of accuracy conventionally named as "square", "vertical stripe" and "horizontal stripe", and two variants of the model parameters estimation. In the first variant we calibrate the model on the data of the first city impacted by the epidemic, and use these parameters in the future for the other cities, that allows taking into account the spatial characteristics of the epidemic in the country. In the second case, we only use historical data available at the time of the prediction for a given city. The advantage of this approach is the lack of need for additional, not always available, external data to predict the epidemic. The results of test calculations have demonstrated that the first method shows good results in the case of significant delays between the peaks of epidemics in different cities. If the outbreak in St. Petersburg started soon after the registration of the first outbreaks in the other cities of the Russian Federation, the second method shows comparable results to an accuracy of 90% to predict the peak of the epidemic. In most cases, it is sufficient for the use of the results of calculations for planning antiviral activities. The lead time of the peak prediction is still at a relatively low level, that seems to be associated with a variety of patterns of virus spread and permanent changes in transport communications within the country