Advanced Computing Approach for Modeling and Prediction COVID-19 Pandemic
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 COV...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Applied Bionics and Biomechanics |
| Online Access: | http://dx.doi.org/10.1155/2022/6056574 |
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| author | Sami A. Morsi Mohammad Eid Alzahrani |
| author_facet | Sami A. Morsi Mohammad Eid Alzahrani |
| author_sort | Sami A. Morsi |
| collection | DOAJ |
| description | 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 (R2=99%). The proposed system has the potential to contribute to public health by making decisions about how to prevent the spread of COVID-19. |
| format | Article |
| id | doaj-art-de9f42eaeee1455095d132923c162c58 |
| institution | Kabale University |
| issn | 1754-2103 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Applied Bionics and Biomechanics |
| spelling | doaj-art-de9f42eaeee1455095d132923c162c582025-02-03T01:22:46ZengWileyApplied Bionics and Biomechanics1754-21032022-01-01202210.1155/2022/6056574Advanced Computing Approach for Modeling and Prediction COVID-19 PandemicSami A. Morsi0Mohammad Eid Alzahrani1Applied College in AbqaiqFaculty of Computer Science and Information TechnologyThe 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 (R2=99%). The proposed system has the potential to contribute to public health by making decisions about how to prevent the spread of COVID-19.http://dx.doi.org/10.1155/2022/6056574 |
| spellingShingle | Sami A. Morsi Mohammad Eid Alzahrani Advanced Computing Approach for Modeling and Prediction COVID-19 Pandemic Applied Bionics and Biomechanics |
| title | Advanced Computing Approach for Modeling and Prediction COVID-19 Pandemic |
| title_full | Advanced Computing Approach for Modeling and Prediction COVID-19 Pandemic |
| title_fullStr | Advanced Computing Approach for Modeling and Prediction COVID-19 Pandemic |
| title_full_unstemmed | Advanced Computing Approach for Modeling and Prediction COVID-19 Pandemic |
| title_short | Advanced Computing Approach for Modeling and Prediction COVID-19 Pandemic |
| title_sort | advanced computing approach for modeling and prediction covid 19 pandemic |
| url | http://dx.doi.org/10.1155/2022/6056574 |
| work_keys_str_mv | AT samiamorsi advancedcomputingapproachformodelingandpredictioncovid19pandemic AT mohammadeidalzahrani advancedcomputingapproachformodelingandpredictioncovid19pandemic |