Article
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Preserved in Portico This version is not peer-reviewed
Determining COVID-19 Dynamics Using Physics Informed Neural Networks
Version 1
: Received: 29 November 2021 / Approved: 30 November 2021 / Online: 30 November 2021 (10:41:39 CET)
A peer-reviewed article of this Preprint also exists.
Malinzi, J.; Gwebu, S.; Motsa, S. Determining COVID-19 Dynamics Using Physics Informed Neural Networks. Axioms 2022, 11, 121. Malinzi, J.; Gwebu, S.; Motsa, S. Determining COVID-19 Dynamics Using Physics Informed Neural Networks. Axioms 2022, 11, 121.
Abstract
The Physics Informed Neural Networks framework is applied to the understanding of the dynamics of Coronavirus of 2019. To provide the governing system of equations used by the framework, the Susceptible-Infected-Recovered-Death mathematical model is used. The study focused on finding the patterns of the dynamics of the disease which involves predicting the infection rate, recovery rate and death rate; thus predicting the active infections, total recovered, susceptible and deceased at any required time. The study used data that was collected on the dynamics of COVID-19 from the Kingdom of Eswatini between March 2020 and September 2021. The obtained results showed less errors thus making highly accurate predictions.
Keywords
Physics Informed Neural Networks; Mathematical modeling; COVID-19
Subject
Computer Science and Mathematics, Computational Mathematics
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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