COVID-19 Epidemiological Indicators POD Spatial Decomposition
https://doi.org/10.15514/ISPRAS-2024-36(2)-13
Abstract
The epidemiological indicators (prevalence, mortality, convalescence) for COVID-19 are investigated as a temporary-spatial dependencies. Proper Orthogonal Decomposition is applied for the first time to this type of data; the main modes and corresponding coefficients are obtained. Due to this method, it is shown that there are modes concentrated in the particular regions which means there are independent factors for disease spreading. Additionally, it is showed that Empirical Mode Decomposition can be successfully applied for noise-reduction and better understanding time dependencies. The exponential nature of the decomposition error decree shows the accuracy of the decomposition. Despite the ability of POD to reveal hidden dependencies, it requires rows of simultaneous data, which in fact may not be so. In the article the correction applied is discussed, however it may not be enough because of mistakes in raw data. The method is recommended to use unless the data it is applied to is inaccurate.
About the Author
Stepan Alekseevich ELISTRATOV
Institute for System Programming of the Russian Academy of Sciences, Sobolev Institute of Mathematics, Siberian Branch of the Russian Academy of Sciences
Russian Federation
Russian Federation
Employee of Technical Systems Digital Modelling Laboratory of the Institute for System Programming of the RAS since 2021. Research interests: numerical simulation, forecast models, reduced order methods.
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Review
For citations:
ELISTRATOV S.A. COVID-19 Epidemiological Indicators POD Spatial Decomposition. Proceedings of the Institute for System Programming of the RAS (Proceedings of ISP RAS). 2024;36(2):181-192. (In Russ.) https://doi.org/10.15514/ISPRAS-2024-36(2)-13
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