Related Experiment Video
Updated: Aug 29, 2025

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
Published on: December 9, 2015
Forecasting of COVID-19 incidence in Ukraine using the method of exponential smoothing
Nina Malysh1, Alla Podavalenko2, Olga Kuzmenko3
1Department of Infectious Diseases with Epidemiology, Sumy State University, Rymskogo-Korsakova 2, Sumy, Ukraine. malysh.ng@gmail.com.
Abstract:
Coronavirus infection (COVID-19) is a highly infectious disease of viral etiology. SARS-CoV-2 virus was first identified during the investigation of the outbreak of respiratory disease in Wuhan, China in December 2019. And already on March 11, 2020 COVID-19 in the world was characterized by the WHO as a pandemic. In Ukraine the situation with incidence COVID-19 remains difficult. The purpose of this study is to to develop a mathematical forecasting model for COVID-19 incidence in Ukraine using an exponential smoothing method. The article analyzes reports on basic COVID-19 incidence rates from 29.02.2019 to 01.10.2021. In order to determine the forecast levels of statistical indicators that characterize the epidemic process of COVID-19 the method of exponential smoothing was used. It is expected that from 29.02.2019 to 01.10.2021 the epidemic situation of COVID-19 incidence will stabilize. The indicator of "active patients" will range from 159.04 to 353.63 per 100 thousand people. The indicator of "hospitalized patients" can reach 15.43 and "fatalities" ‒ 1.87. The use of the method of exponential smoothing based on time series models for modeling the dynamics of COVID-19 incidence allows to develop and implement scientifically sound methods in order to prevent, quickly prepare health care institutions for hospitalization.
Related Concept Videos
Steps in Outbreak Investigation
Residuals and Least-Squares Property
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
Microsoft Excel: Regression Analysis
To perform regression...
Prediction Intervals
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
Estimating Population Mean with Unknown Standard Deviation
William S. Gosset (1876–1937) of the...
Statistical Methods for Analyzing Epidemiological Data

