Research Article
Probable Forecasting of Epidemic COVID-19 in Using COCUDE Model
@ARTICLE{10.4108/eai.3-2-2021.168601, author={Prasannavenkatesan Theerthagiri}, title={Probable Forecasting of Epidemic COVID-19 in Using COCUDE Model}, journal={EAI Endorsed Transactions on Pervasive Health and Technology}, volume={7}, number={26}, publisher={EAI}, journal_a={PHAT}, year={2021}, month={2}, keywords={COVID-19, future prediction, infection rate, COCUDE model, decease rate}, doi={10.4108/eai.3-2-2021.168601} }- Prasannavenkatesan Theerthagiri
Year: 2021
Probable Forecasting of Epidemic COVID-19 in Using COCUDE Model
PHAT
EAI
DOI: 10.4108/eai.3-2-2021.168601
Abstract
INTRODUCTION: The world has been struck due to the dangerous human threat called Corona Virus Disease 2019. This research work proposes a methodology to encounter the future infection rate, curing rate, and decease rate.
OBJECTIVES: This uses the artificial intelligence algorithm to design and develop the proposed confirmed, cured, deceased (COCUDE) model.
METHODS: A nonlinear auto-regressive model has been developed with several iterations to design the proposed COCUDE model. The Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), and Correlated Akaike Information criterion (AICc) metrics are analyzed to check the stationary and quality for the proposed COCUDE model.
RESULTS: The prediction results are evaluated by the performance error metrics such as mean square error (MSE) and root mean square error (RMSE), in which the errors are lower for the proposed model. Thus, the prediction results indicate the proposed COCUDE model might accurately predict future COVID-19 infection rates with reduced errors.
CONCLUSION: It might support the corresponding authorities to take precautious action on the required necessities for the medical and clinical infrastructures and equipment.
Copyright © 2021 Prasannavenkatesan Theerthagiri, licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution license, which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.
