Abstract
Recently a subject of COVID-19 pandemic-related predictions and models emerged as one of the crucial problems related to medicine and computer science. Acquired data carries the features of complex, difficult to analyze data. Moreover, often exponential growth of infected leads to the rapid growth of available data. Thus any approach related to cleaning and preprocessing data, as well as algorithm capable to deal with the prediction problem are crucial. It is especially important due to two main reasons: first of all, results acquired during the analysis and prediction could be used to contain the pandemic; moreover, such unprocessed, difficult data could be very important for different machine learning methods as a source of real-world, still-changing data. It should be clearly stated, that impact of the external factors, like capabilities of dealing with a pandemic, different country-dependent actions, and different virus mutations is an enormous challenge related to the data prediction.
In this article, we introduce an overview of different methods and approaches used in the context of the COVID-19 pandemic. Additionally, we estimate the quality of pandemic prediction on the basis of the polynomial regression. We investigate four different versions (dependent on the size of the train set) as well as test results on four different cases (situation in three different countries as well as the situation around the world).
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Hrabia, A., Kozak, J., Juszczuk, P. (2021). Machine Learning in the Context of COVID-19 Pandemic Data Analysis. In: Wojtkiewicz, K., Treur, J., Pimenidis, E., Maleszka, M. (eds) Advances in Computational Collective Intelligence. ICCCI 2021. Communications in Computer and Information Science, vol 1463. Springer, Cham. https://doi.org/10.1007/978-3-030-88113-9_29
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