Статья

Problems of creating predictive models of the COVID19 coronavirus pandemic

E. Levkova, Е. Левкова, R. Sepiashvili, Р. Сепиашвили, S. Savin, С. Савин,
2021

Relevance. The article is devoted to creating prognostic models based on epidemiological and immunological data. Objective: to study the comparative dynamic epidemiological and immunological characteristics of patients with COVID-19. Materials and methods. Methodological approaches to the use of system analysis of epidemiological and immunological characteristics of patients with COVID-19 using multivariate analysis are described. The used technologies of computer-aided analysis systems, algorithms for recognizing, measuring and identifying the condition of patients, and methods of statistical data processing made it possible to create a universal information predictive model for calculating the dynamics of infectious diseases prone to generalization (pandemics), as well as to understand in which groups these new infectious diseases are most dangerous. Results and discussion. Using the methods of system analysis, the epidemiological and immunological aspects of predictive models of the coronavirus pandemic were evaluated using the most objective international data, which increased the information content of the analysis. Conclusions . Creating predictive epidemiological and immunological models of the pandemic is an urgent and promising task to combat the medical and social consequences of the spread of coronavirus infection in Russia.

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Документы

Версии

  • 1. Version of Record от 2021-12-15

Метаданные

Об авторах
  • E. Levkova
    Peoples’ Friendship University of Russia (RUDN University)
  • Е. Левкова
    Российский университет дружбы народов
  • R. Sepiashvili
    Peoples’ Friendship University of Russia (RUDN University)
  • Р. Сепиашвили
    Российский университет дружбы народов
  • S. Savin
    Pacific National University
  • С. Савин
    Тихоокеанский государственный университет
Название журнала
  • RUDN Journal of Medicine
Том
  • 25
Выпуск
  • 1
Страницы
  • 31-38
Издатель
  • Peoples' Friendship University of Russia
Тип документа
  • journal article
Тип лицензии Creative Commons
  • CC BY
Правовой статус документа
  • Свободная лицензия
Источник
  • dimensions