Abstract
Building a system to forecast Covid-19 infected cases is of great importance at the present time, so in this article, we present two systems to forecast cumulative Covid-19 infected cases. The first system (DLM-System) is based on deep learning models, which include both long short-term memory (LSTM), bidirectional long short-term memory (Bi-LSTM), and Gated recurrent unit (GRU). The second system is a (TS-System) based on time series models and neural networks, with a Prioritizer for models and weights for time series models acting as an ensemble between them. We did a comparison between them in order to choose the best system to forecast cumulative Covid-19 infected cases, using the example of 7 countries. As some of them have finished the second wave and others have finished the third wave of infections (Russia, the United States of America, France, Poland, Turkey, Italy, and Spain). The criterion for choosing the best model is MAPE. It is a percentage, not an absolute value. It was concluded that an ensemble method gave the smallest errors compared to the errors of the models in the (TS-System).
The work was supported by Act 211 Government of the Russian Federation, contract No. 02.A03.21.0011. The work was supported by the Ministry of Science and Higher Education of the Russian Federation (government order FENU-2020-0022).
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Makarovskikh, T., Abotaleb, M. (2021). Comparison Between Two Systems for Forecasting Covid-19 Infected Cases. In: Byrski, A., Czachórski, T., Gelenbe, E., Grochla, K., Murayama, Y. (eds) Computer Science Protecting Human Society Against Epidemics. ANTICOVID 2021. IFIP Advances in Information and Communication Technology, vol 616. Springer, Cham. https://doi.org/10.1007/978-3-030-86582-5_10
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DOI: https://doi.org/10.1007/978-3-030-86582-5_10
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