Development, study, and comparison of models of cross-immunity to the influenza virus using statistical methods and machine learning.

Introduction: The World Health Organization considers the values of antibody titers in the hemagglutination inhibition assay as one of the most important criteria for assessing successful vaccination. Mathematical modeling of cross-immunity allows for identification on a real-time basis of new antigenic variants, which is of paramount importance for human health.

Materials And Methods: This study uses statistical methods and machine learning techniques from simple to complex: logistic regression model, random forest method, and gradient boosting.

[Mathematical model for assessing the level of cross-immunity between strains of influenza virus subtype HN].

Introduction: The WHO regularly updates influenza vaccine recommendations to maximize their match with circulating strains. Nevertheless, the effectiveness of the influenza A vaccine, specifically its H3N2 component, has been low for several seasons. The aim of the study is to develop a mathematical model of cross-immunity based on the array of published WHO hemagglutination inhibition assay (HAI) data.

Variability of nonpathogenic influenza virus H5N3 under immune pressure.

Mutations arising in influenza viruses that have undergone immune pressure may promote a successful spread of mutants in nature. In order to evaluate the variability of nonpathogenic influenza virus A/duck/Moscow/4182-C/2010(H5N3) and to determine the common epitopes between it and highly pathogenic H5N1 avian influenza viruses (HPAIV), a set of escape mutants was selected due to action of MABs specific against A/chicken/Pennsylvania/8125/83(H5N2), A/Vietnam/1203/04(H5N1) and A/duck/Novosibirsk/56/05(H5N1) viruses. The complete genomes of escape mutants were sequenced and amino acid point mutations were determined in HA, NA, PA, PB1, PB2, M1, M2, and NP proteins.

Predicting the Evolutionary Variability of the Influenza A Virus.

The influenza A virus remains one of the most common and dangerous human health concerns due to its rapid evolutionary dynamics. Since the evolutionary changes of influenza A viruses can be traced in real time, the last decade has seen a surge in research on influenza A viruses due to an increase in experimental data (selection of escape mutants followed by examination of their phenotypic characteristics and generation of viruses with desired mutations using reverse genetics). Moreover, the advances in our understanding are also attributable to the development of new computational methods based on a phylogenetic analysis of influenza virus strains and mathematical (integro-differential equations, statistical methods, probability-theory-based methods) and simulation modeling.