Determining COVID-19 Dynamics Using Physics Informed Neural Networks

Joseph Malinzi; Simanga Gwebu; Sandile Motsa

doi:10.20944/preprints202111.0555.v1

Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Version 1 : Received: 29 November 2021 / Approved: 30 November 2021 / Online: 30 November 2021 (10:41:39 CET)

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.

Physics Informed Neural Networks; Mathematical modeling; COVID-19

MATHEMATICS & COMPUTER SCIENCE, Computational Mathematics

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Determining COVID-19 Dynamics Using Physics Informed Neural Networks

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