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Memory-Dependent Forecasting of COVID-19: The Flexibility of Extrapolated Kernel Least Mean Square Algorithm

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preprint
posted on 01.03.2021, 23:23 by Noor Ahmad, Mohd Hafiz Mohd
The extrapolated kernel least mean square algorithm (extrap-KLMS) with memory is proposed for the forecasting of future trends of COVID-19. The extrap-KLMS is derived in the framework of data-driven modelling that attempts to describe the dynamics of infectious disease by reconstructing the phase-space of the state variables in a reproducing kernel Hilbert space (RKHS). Short-time forecasting is enabled via an extrapolation of the KLMS trained model using a forward euler step, along the direction of a memory-dependent gradient estimate. A user-defined memory averaging window allows users to incorporate prior knowledge of the history of the pandemic into the gradient estimate thus providing a spectrum of scenario-based estimates of futures trends. The performance of the extrap-KLMS method is validated using data set for Malaysia, Saudi Arabia and Italy in which we highlight the flexibility of the method in capturing persistent trends of the pandemic. A situational analysis of the Malaysian third wave further demonstrate the capabilities of our method

Funding

Ministry of Higher Education Malaysia under the FRGS grant (Acc no: 203.PMATHS.6711942)

History

Email Address of Submitting Author

nooratinah@usm.my

ORCID of Submitting Author

0000-0002-4249-7305

Submitting Author's Institution

Universiti Sains Malaysia

Submitting Author's Country

Malaysia