Application of Gaussian process regression as a surrogate modeling method to assess the dynamics of COVID-19 propagation.

In this research, we aimed to assess the possibility of using surrogate modeling methods to replace time-consuming calculations related to modeling of COVID-19 dynamics. The Gaussian process regression (GPR) was used as a surrogate to replace detailed simulations by a COVID-19 multiagent model. Experiments were conducted with various kernels, as a result, in accordance with the quality metrics of the models, kernels were identified in which the surrogate gives the most accurate result (Rational Quadratic kernel and Additive kernel).

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.

A Modeling Framework for Decision Support in Periprosthetic Joint Infection Treatment.

In this paper, we present a framework, which aims at facilitating the choice of the best strategy related to the treatment of periprosthetic joint infection (PJI). The framework includes two models: a detailed non-Markovian model based on the decision tree approach, and a general Markov model, which captures the most essential states of a patient under treatment. The application of the framework is demonstrated on the dataset provided by Russian Scientific Research Institute of Traumatology and Orthopedics "R.

Analyzing influenza outbreaks in Russia using an age-structured dynamic transmission model.

In this study, we addressed the ability of a minimalistic SEIR model to satisfactorily describe influenza outbreak dynamics in Russian settings. For that purpose, we calibrated an age-specific influenza dynamics model to Russian acute respiratory infection (ARI) incidence data over 2009-2016 and assessed the variability of proportion of non-immune individuals in the population depending on the regarded city, the non-epidemic indicence baseline, the contact structure considered and the used calibration method. The experiments demonstrated the importance of distinguishing characteristics of different age groups, such as contact intensities and background immunity levels.

Prediction of influenza peaks in Russian cities: Comparing the accuracy of two SEIR models.

This paper is dedicated to the application of two types of SEIR models to the influenza outbreak peak prediction in Russian cities. The first one is a continuous SEIR model described by a system of ordinary differential equations. The second one is a discrete model formulated as a set of difference equations, which was used in the Baroyan-Rvachev modeling framework for the influenza outbreak prediction in the Soviet Union.

A syringe-sharing model for the spread of HIV: application to Omsk, Western Siberia.

A system of two differential equations is used to model the transmission dynamics of human immunodeficiency virus between 'persons who inject drugs' (PWIDs) and their syringes. Our vector-borne disease model hinges on a metaphorical urn from which PWIDs draw syringes at random which may or may not be infected and may or may not result in one of the two agents becoming infected. The model's parameters are estimated with data mostly from the city of Omsk in Western Siberia.