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

Jurgita Markevičiūtė1,*, 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: email

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

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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APA Style
Markevičiūtė, J., Bernatavičienė, J., Levulienė, R., Medvedev, V., Treigys, P. et al. (2022). Attention-based and time series models for short-term forecasting of COVID-19 spread. Computers, Materials & Continua, 70(1), 695-714. https://doi.org/10.32604/cmc.2022.018735
Vancouver Style
Markevičiūtė J, Bernatavičienė J, Levulienė R, Medvedev V, Treigys P, Venskus J. Attention-based and time series models for short-term forecasting of COVID-19 spread. Comput Mater Contin. 2022;70(1):695-714 https://doi.org/10.32604/cmc.2022.018735
IEEE Style
J. Markevičiūtė, J. Bernatavičienė, R. Levulienė, V. Medvedev, P. Treigys, and J. Venskus "Attention-Based and Time Series Models for Short-Term Forecasting of COVID-19 Spread," Comput. Mater. Contin., vol. 70, no. 1, pp. 695-714. 2022. https://doi.org/10.32604/cmc.2022.018735



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