Open Access

ARTICLE

Attention-Based and Time Series Models for Short-Term Forecasting of COVID-19 Spread

by Jurgita Markevičiūtė^1,*, Jolita Bernatavičienė², Rūta Levulienė¹, Viktor Medvedev², Povilas Treigys², Julius Venskus²

1 Institute of Applied Mathematics, Vilnius University, Vilnius, 03225, Lithuania
2 Institute of Data Science and Digital Technologies, Vilnius University, Vilnius, 08412, Lithuania

* Corresponding Author: Jurgita Markevičiūtė. Email:

Computers, Materials & Continua 2022, 70(1), 695-714. https://doi.org/10.32604/cmc.2022.018735

Received 19 March 2021; Accepted 02 May 2021; Issue published 07 September 2021

Abstract

The growing number of COVID-19 cases puts pressure on healthcare services and public institutions worldwide. The pandemic has brought much uncertainty to the global economy and the situation in general. Forecasting methods and modeling techniques are important tools for governments to manage critical situations caused by pandemics, which have negative impact on public health. The main purpose of this study is to obtain short-term forecasts of disease epidemiology that could be useful for policymakers and public institutions to make necessary short-term decisions. To evaluate the effectiveness of the proposed attention-based method combining certain data mining algorithms and the classical ARIMA model for short-term forecasts, data on the spread of the COVID-19 virus in Lithuania is used, the forecasts of epidemic dynamics were examined, and the results were presented in the study. Nevertheless, the approach presented might be applied to any country and other pandemic situations. The COVID-19 outbreak started at different times in different countries, hence some countries have a longer history of the disease with more historical data than others. The paper proposes a novel approach to data registration and machine learning-based analysis using data from attention-based countries for forecast validation to predict trends of the spread of COVID-19 and assess risks.

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Keywords

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