Back to Search
Start Over
Advanced Computing Approach for Modeling and Prediction COVID-19 Pandemic.
- Source :
- Applied Bionics & Biomechanics; 4/14/2022, p1-8, 8p
- Publication Year :
- 2022
-
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 (R) metric was used to evaluate the model to predict COVID-19, and the proposed system scored the highest correlation ( R 2 = 99 %). The proposed system has the potential to contribute to public health by making decisions about how to prevent the spread of COVID-19. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 11762322
- Database :
- Complementary Index
- Journal :
- Applied Bionics & Biomechanics
- Publication Type :
- Academic Journal
- Accession number :
- 156317682
- Full Text :
- https://doi.org/10.1155/2022/6056574