Abstract
This paper analyses the possibilities of using Machine learning to develop a forecasting model for COVID-19 with a publicly available dataset from the Johns Hopkins University COVID-19 Data Repository and with the addition of a percentage of each variant from the GISAID Variant database. Genetic programming (GP), a symbolic regressor algorithm, is used for the estimation of new confirmed infected cases, hospitalized cases, cases in intensive care units (ICUs), and deceased cases. This metaheuristics method algorithm was used on a dataset for Austria and neighboring countries Czechia, Hungary, Slovenia, and Slovakia. Machine learning was done to create individual models for each country. Variance-based sensitivity analysis was initiated using the obtained mathematical models. This analysis showed us which input variables the output of the obtained models is sensitive to, like in the case of how much each covid variant affects the spread of the virus or the number of deceased cases. Individual short-term models have achieved very high R2 scores, while long-term predictions have achieved lower R2 scores.
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Acknowledgment
This research is supported by the project that has received funding from the European Union’s Horizon 2020 research and innovation programmes under grant agreement No 952603 (SGABU project). This article reflects only the author's view. The Commission is not responsible for any use that may be made of the information it contains.
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Benolić, L., Car, Z., Filipović, N. (2023). Mathematical Modeling of COVID-19 Spread Using Genetic Programming Algorithm. In: Filipovic, N. (eds) Applied Artificial Intelligence: Medicine, Biology, Chemistry, Financial, Games, Engineering. AAI 2022. Lecture Notes in Networks and Systems, vol 659. Springer, Cham. https://doi.org/10.1007/978-3-031-29717-5_19
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DOI: https://doi.org/10.1007/978-3-031-29717-5_19
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