title: ÐÐµÑÐ¾ÑÑÐ½Ð¾ÑÑÐ½Ð¾Ðµ Ð¼Ð¾Ð´ÐµÐ»Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ðµ ÑÐ°Ð·Ð²Ð¸ÑÐ¸Ñ Ð¿Ð°Ð½Ð´ÐµÐ¼Ð¸Ð¸ ÑÑÐµÐ´ÑÑÐ²Ð°Ð¼Ð¸ Ð¸Ð½ÑÐµÐ»Ð»ÐµÐºÑÑÐ°Ð»ÑÐ½Ð¾Ð³Ð¾ Ð°Ð½Ð°Ð»Ð¸Ð·Ð° Ð´Ð°Ð½Ð½ÑÑ
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð¼Ð°Ð³Ð¸ÑÑÑÐ°
titleType: Subtitle
title: Probabilistic forecasting of the evolution of a pandemic based on intelligent data analysis
Creators: name: ÐÑÑÐ´Ð¸Ð½Ð°, ÐÐ°ÑÐ¸Ñ
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2023
Subjects: ÐÑÐºÑÑÑÑÐ²ÐµÐ½Ð½ÑÐ¹ Ð¸Ð½ÑÐµÐ»Ð»ÐµÐºÑ
ÐÐ°ÑÐ¸Ð½Ð½Ð¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
ÐÐ°ÑÐµÐ¼Ð°ÑÐ¸ÑÐµÑÐºÐ¾Ðµ Ð¼Ð¾Ð´ÐµÐ»Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ðµ
Ð°Ð½Ð°Ð»Ð¸Ð· Ð´Ð°Ð½Ð½ÑÑ
ÑÑÐ°ÑÐ¸ÑÑÐ¸ÑÐµÑÐºÐ¸Ðµ Ð¼Ð¾Ð´ÐµÐ»Ð¸
Ð¿ÑÐ¾Ð³Ð½Ð¾Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ðµ Ð²ÑÐµÐ¼ÐµÐ½Ð½ÑÑ ÑÑÐ´Ð¾Ð²
Ð²ÐµÑÐ¾ÑÑÐ½Ð¾ÑÑÐ½Ð¾Ðµ Ð¿ÑÐ¾Ð³Ð½Ð¾Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ðµ
Ð¿Ð°Ð½Ð´ÐµÐ¼Ð¸Ñ
data analysis
statistical models
time series forecasting
probabilistic forecasting
pandemic

description: ÐÐ°Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð¿Ð¾ÑÐ²ÑÑÐµÐ½Ð° Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ñ Ð²Ð¾Ð·Ð¼Ð¾Ð¶Ð½Ð¾ÑÑÐ¸ Ð¿ÑÐ¸Ð¼ÐµÐ½ÐµÐ½Ð¸Ñ Ð²ÐµÑÐ¾ÑÑÐ½Ð¾ÑÑÐ½Ð¾Ð³Ð¾ Ð¼Ð¾Ð´ÐµÐ»Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ Ð´Ð»Ñ Ð¿ÑÐ¾Ð³Ð½Ð¾Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ ÑÐ°Ð·Ð²Ð¸ÑÐ¸Ñ Ð¿Ð°Ð½Ð´ÐµÐ¼Ð¸Ð¸ Covid-19 Ð¸ ÑÐ¾Ð·Ð´Ð°Ð½Ð¸Ñ Ð¸Ð½ÑÑÑÑÐ¼ÐµÐ½ÑÐ° Ð´Ð»Ñ Ð¿ÑÐµÐ´Ð²Ð°ÑÐ¸ÑÐµÐ»ÑÐ½Ð¾Ð¹ Ð¾Ð±ÑÐ°Ð±Ð¾ÑÐºÐ¸ Ð´Ð°Ð½Ð½ÑÑ Ð¸ Ð¿ÑÐ¾Ð³Ð½Ð¾Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ Ð²ÑÐµÐ¼ÐµÐ½Ð½ÑÑ ÑÑÐ´Ð¾Ð² Ð½Ð° ÑÐ·ÑÐºÐµ Python. ÐÐ»Ñ Ð¿Ð¾Ð²ÑÑÐµÐ½Ð¸Ñ ÑÐ¾ÑÐ½Ð¾ÑÑÐ¸ Ð¿ÑÐ¾Ð³Ð½Ð¾Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ Ð°Ð½Ð°Ð»Ð¸Ð·Ð¸ÑÑÐµÑÑÑ Ð²Ð¾Ð·Ð¼Ð¾Ð¶Ð½Ð¾ÑÑÑ Ð¿ÑÐ¸Ð¼ÐµÐ½ÐµÐ½Ð¸Ñ ÑÑÐ°ÑÐ¸ÑÑÐ¸ÐºÐ¸ Ð¿Ð¾ Ð²Ð°ÐºÑÐ¸Ð½Ð°ÑÐ¸Ð¸ Ð² ÐºÐ°ÑÐµÑÑÐ²Ðµ Ð´Ð¾Ð¿Ð¾Ð»Ð½Ð¸ÑÐµÐ»ÑÐ½Ð¾Ð³Ð¾ Ð¿ÑÐµÐ´Ð¸ÐºÑÐ¾ÑÐ° Ð² Ð¿ÑÐ¾Ð³Ð½Ð¾Ð·Ð½Ð¾Ð¹ Ð¼Ð¾Ð´ÐµÐ»Ð¸ Ñ Ð¿Ð¾Ð¼Ð¾ÑÑÑ Ð¾ÑÐµÐ½ÐºÐ¸ Ð²Ð»Ð¸ÑÐ½Ð¸Ñ Ð²Ð°ÐºÑÐ¸Ð½Ð°ÑÐ¸Ð¸ Ð½Ð° Ð·Ð°Ð±Ð¾Ð»ÐµÐ²Ð°ÐµÐ¼Ð¾ÑÑÑ. Ð¢Ð°ÐºÐ¶Ðµ Ð² Ð´Ð°Ð½Ð½Ð¾Ð¹ ÑÐ°Ð±Ð¾ÑÐµ Ð¿ÑÐ¾Ð¸Ð·Ð²Ð¾Ð´Ð¸ÑÑÑ ÑÑÐ°Ð²Ð½ÐµÐ½Ð¸Ðµ Ð´Ð²ÑÑ ÑÑÐ°ÑÐ¸ÑÑÐ¸ÑÐµÑÐºÐ¸Ñ Ð¼Ð¾Ð´ÐµÐ»ÐµÐ¹ Ð¸ ÑÑÐµÑ Ð¼Ð¾Ð´ÐµÐ»ÐµÐ¹ Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ð´Ð»Ñ Ð¿ÑÐµÐ´ÑÐºÐ°Ð·Ð°Ð½Ð¸Ñ Ð·Ð°Ð±Ð¾Ð»ÐµÐ²Ð°ÐµÐ¼Ð¾ÑÑÐ¸ ÐºÐ¾ÑÐ¾Ð½Ð°Ð²Ð¸ÑÑÑÐ¾Ð¼. Ð Ð°ÑÑÐ¼Ð°ÑÑÐ¸Ð²Ð°ÑÑÑÑ Ð¼ÐµÑÐ¾Ð´Ñ ÑÐºÑÐ¿Ð¾Ð½ÐµÐ½ÑÐ¸Ð°Ð»ÑÐ½Ð¾Ð³Ð¾ ÑÐ³Ð»Ð°Ð¶Ð¸Ð²Ð°Ð½Ð¸Ñ Ð¸ ARIMA Ð² ÐºÐ°ÑÐµÑÑÐ²Ðµ ÑÑÐ°ÑÐ¸ÑÑÐ¸ÑÐµÑÐºÐ¸Ñ Ð¿Ð¾Ð´ÑÐ¾Ð´Ð¾Ð² Ðº Ð¿ÑÐ¾Ð³Ð½Ð¾Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ Ð¸ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ñ Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ: k-Ð±Ð»Ð¸Ð¶Ð°Ð¹ÑÐ¸Ñ ÑÐ¾ÑÐµÐ´ÐµÐ¹, ÑÐ»ÑÑÐ°Ð¹Ð½ÑÐ¹ Ð»ÐµÑ, Ð³ÑÐ°Ð´Ð¸ÐµÐ½ÑÐ½ÑÐ¹ Ð±ÑÑÑÐ¸Ð½Ð³. ÐÑÐ±Ð¾Ñ Ð¾Ð¿ÑÐ¸Ð¼Ð°Ð»ÑÐ½Ð¾Ð³Ð¾ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð° Ð¿ÑÐ¾Ð¸Ð·Ð²Ð¾Ð´Ð¸ÑÑÑ Ð½Ð° Ð¾ÑÐ½Ð¾Ð²Ðµ Ð¾ÑÐ¸Ð±ÐºÐ¸ MAPE, Ð¿Ð¾Ð»ÑÑÐµÐ½Ð½Ð¾Ð¹ Ð¿ÑÐ¸ Ð¿ÑÐ¾Ð³Ð½Ð¾Ð·Ðµ Ð½Ð° ÑÐµÑÑÐ¾Ð²Ð¾Ð¹ Ð²ÑÐ±Ð¾ÑÐºÐµ, Ð½Ð°Ð¸Ð±Ð¾Ð»ÐµÐµ ÑÐ¾ÑÐ½Ð¾Ð³Ð¾ ÑÐ¾Ð¾ÑÐ²ÐµÑÑÑÐ²Ð¸Ñ ÑÐ°ÑÐ°ÐºÑÐµÑÑ Ð¸ÑÑÐ¾Ð´Ð½Ð¾Ð³Ð¾ Ð²ÑÐµÐ¼ÐµÐ½Ð½Ð¾Ð³Ð¾ ÑÑÐ´Ð° Ð¸ ÑÐºÐ¾ÑÐ¾ÑÑÐ¸ ÑÐ°Ð±Ð¾ÑÑ. Ð ÐµÐ·ÑÐ»ÑÑÐ°ÑÑ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ñ Ð¿Ð¾ÐºÐ°Ð·ÑÐ²Ð°ÑÑ, ÑÑÐ¾ Ð¾Ð¿ÑÐ¸Ð¼Ð°Ð»ÑÐ½ÑÐ¼ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð¾Ð¼ Ð´Ð»Ñ Ð¿ÑÐ¾Ð³Ð½Ð¾Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ ÑÐ²Ð»ÑÐµÑÑÑ Ð³ÑÐ°Ð´Ð¸ÐµÐ½ÑÐ½ÑÐ¹ Ð±ÑÑÑÐ¸Ð½Ð³. Ð ÐµÐ·ÑÐ»ÑÑÐ°ÑÑ Ð´Ð°Ð½Ð½Ð¾Ð¹ ÑÐ°Ð±Ð¾ÑÑ Ð°ÐºÑÑÐ°Ð»ÑÐ½Ñ, Ñ.Ðº. Ð¼Ð¾Ð³ÑÑ Ð±ÑÑÑ Ð¿Ð¾Ð»ÐµÐ·Ð½Ñ Ð´Ð»Ñ Ð¼ÐµÐ´Ð¸ÑÐ¸Ð½ÑÐºÐ¸Ñ Ð¾ÑÐ³Ð°Ð½Ð¸Ð·Ð°ÑÐ¸Ð¹, Ð³Ð¾ÑÑÐ´Ð°ÑÑÑÐ²ÐµÐ½Ð½ÑÑ ÑÑÑÑÐºÑÑÑ Ð¿ÑÐ¸ Ð¿ÑÐ¸Ð½ÑÑÐ¸Ð¸ Ð¼ÐµÑ Ð´Ð»Ñ Ð¿ÑÐµÐ´Ð¾ÑÐ²ÑÐ°ÑÐµÐ½Ð¸Ñ ÑÐ°ÑÐ¿ÑÐ¾ÑÑÑÐ°Ð½ÐµÐ½Ð¸Ñ Ð±ÑÐ´ÑÑÐ¸Ñ Ð²ÑÐ¿ÑÑÐµÐº Ð²Ð¸ÑÑÑÐ½ÑÑ Ð·Ð°Ð±Ð¾Ð»ÐµÐ²Ð°Ð½Ð¸Ð¹.
description: This paper is devoted to the study of the possibility of using probabilistic modeling to predict the evolution of the Covid-19 pandemic and creating a tool for data pre-processing and time-series forecasting in Python. To improve forecasting accuracy, the possibility of using vaccination statistics as an exogenous variable of the forecast model is analyzed by estimating the effect of vaccination on incidence.Â  In this paper, two statistical models and three machine learning models for predicting the incidence of coronavirus are compared. Exponential smoothing and ARIMA methods are considered as statistical approaches for prediction and k-nearest neighbors, random forest, and gradient binning algorithms as machine learning methods. The choice of the optimal algorithm is made based on the MAPE obtained from the prediction on the test sample, as well as the best fit to the pattern of the original time series and the speed of the performance. The results of the study show that the optimal algorithm for prediction is the gradient boosting model. The results of this work are relevant, because it can be useful for medical organizations and the government in taking measures to prevent the spread of future epidemics of virus diseases.