Open AccessCOVID‑19 mathematical forecasting in the Russian Federation
Author(s) -
И. А. Лакман,
Aleksandr Agapitov,
Л. Ф. Садикова,
Olena Chernenko,
С. В. Новиков,
Denis Popov,
В. Н. Павлов,
Diana Gareeva,
Bulat Idrisov,
А. Р. Билялов,
Н. Ш. Загидуллин
Publication year - 2020
Publication title -
arterialʹnaâ gipertenziâ
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.126
H-Index - 5
eISSN - 2411-8524
pISSN - 1607-419X
DOI - 10.18705/1607-419x-2020-26-3-288-294
Subject(s) - russian federation , autoregressive integrated moving average , exponential smoothing , covid-19 , lagging , operations research , econometrics , computer science , geography , disease , regional science , medicine , statistics , mathematics , infectious disease (medical specialty) , time series , machine learning , pathology
A new coronavirus infection (CVI) is a challenge to the medical system of the Russian Federation and requires precise flow forecasting to take the necessary measures on time. The article provides an overview of modern mathematical tools for predicting the course of CVI in the world. The created CVI forecasting project office allowed to determine the most effective analysis tools in the Russian Federation — the ARIMA, SIRD and Holt–Winters exponential smoothing models. Implementation of these models allows for prediction of short-term morbidity, mortality and survival of patients with an accuracy of 99 % both in the Russian Federation in general and in the regions. In addition, the distribution of CVI was characterized. Particularly, Moscow and Moscow region have the maximum spread of infection, and other regions are lagging behind in the dynamics of the incidence by 1–3 weeks. The obtained models allow us to predict the course of the disease in the regions successfully and take the necessary measures in a timely manner.