Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed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)

How to cite: Malinzi, J.; Gwebu, S.; Motsa, S. Determining COVID-19 Dynamics Using Physics Informed Neural Networks. Preprints 2021, 2021110555 (doi: 10.20944/preprints202111.0555.v1). Malinzi, J.; Gwebu, S.; Motsa, S. Determining COVID-19 Dynamics Using Physics Informed Neural Networks. Preprints 2021, 2021110555 (doi: 10.20944/preprints202111.0555.v1).

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

MATHEMATICS & COMPUTER SCIENCE, Computational Mathematics

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