Affiliation:
1. Applied College in Abqaiq, King Faisal University, P.O. Box 400, Al-Ahsa 31982, Saudi Arabia
2. Al-Neelain University, Sudan
3. Faculty of Computer Science and Information Technology, Al Baha University, Saudi Arabia
Abstract
The emergence of many strains of the coronavirus, including the latest omicron strain, which is spreading at a very high speed, is leading to the World Health Organization’s (WHO) concern about the creation of this new mutation. Therefore, there is a strong motivation for modeling and predicting COVID-19 to control the number of cases of the disease. The proposed system for predicting the number of cases of COVID-19 can help governments take precautions to prevent the spread of the disease. In this paper, a statistical logistic growth model was employed to predict the spread of COVID-19 in Australia and Brazil. The datasets were collected from the surveillance systems in Australia and Brazil from March 13, 2020, to December 12, 2021, for 641 days. This proposed method used a tested logistic growth model for the complex spread of COVID-19 and forecasted future values within a time interval of six days. The results of the predicted, cumulative, confirmed cases indicate the robustness and effectiveness of the proposed system, which was categorized by time-dependent dynamics. The coefficient of determination (
) metric was used to evaluate the model to predict COVID-19, and the proposed system scored the highest correlation (
). The proposed system has the potential to contribute to public health by making decisions about how to prevent the spread of COVID-19.
Funder
Deanship of Scientific Research at King Faisal University
Subject
Biomedical Engineering,Bioengineering,Medicine (miscellaneous),Biotechnology
Cited by
4 articles.
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