Abstract
The COVID-19 pandemic is one of the biggest health crises of the twenty-first century, it has completely affected society’s daily life, and has impacted populations worldwide, both economically and socially. The use of machine learning algorithms to study data from the COVID-19 pandemic has been quite frequent in the most varied articles published in recent times. In this paper, we will analyze the impact of several variables (number of cases, temperature, people vaccinated, people fully vaccinated, number of vaccinations, and boosters) on the number of deaths caused by COVID-19 or SARS-CoV-2 in Portugal and find the most appropriate predictive model. Various algorithms were used, such as OLS, Ridge, LASSO, MLP, Gradient Boosting, and Random Forest. The method used for data processing was Cross- Industry Standard Process for Data Mining (CRISP-DM). The data was obtained from an open-access database.
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Acknowledgements
We gratefully acknowledge financial support from FCT—Fundação para a Ciência e a Tecnologia (Portugal), national funding through research grant UIDB/04521/2020.
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Arriaga, A., Costa, C.J. (2023). Modeling and Predicting Daily COVID-19 (SARS-CoV-2) Mortality in Portugal. In: Anwar, S., Ullah, A., Rocha, Á., Sousa, M.J. (eds) Proceedings of International Conference on Information Technology and Applications. Lecture Notes in Networks and Systems, vol 614. Springer, Singapore. https://doi.org/10.1007/978-981-19-9331-2_23
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