An Integrated Neural Network and SEIR Model to Predict COVID-19
A novel coronavirus (COVID-19), which has become a great concern for the world, was identified first in Wuhan city in China. The rapid spread throughout the world was accompanied by an alarming number of infected patients and increasing number of deaths gradually. If the number of infected cases can...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-03-01
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| Series: | Algorithms |
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| Online Access: | https://www.mdpi.com/1999-4893/14/3/94 |
| _version_ | 1797540799002443776 |
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| author | Sharif Noor Zisad Mohammad Shahadat Hossain Mohammed Sazzad Hossain Karl Andersson |
| author_facet | Sharif Noor Zisad Mohammad Shahadat Hossain Mohammed Sazzad Hossain Karl Andersson |
| author_sort | Sharif Noor Zisad |
| collection | DOAJ |
| description | A novel coronavirus (COVID-19), which has become a great concern for the world, was identified first in Wuhan city in China. The rapid spread throughout the world was accompanied by an alarming number of infected patients and increasing number of deaths gradually. If the number of infected cases can be predicted in advance, it would have a large contribution to controlling this pandemic in any area. Therefore, this study introduces an integrated model for predicting the number of confirmed cases from the perspective of Bangladesh. Moreover, the number of quarantined patients and the change in basic reproduction rate (the <i>R<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mrow></mrow><mn>0</mn></msub></semantics></math></inline-formula></i>-value) can also be evaluated using this model. This integrated model combines the SEIR (Susceptible, Exposed, Infected, Removed) epidemiological model and neural networks. The model was trained using available data from 250 days. The accuracy of the prediction of confirmed cases is almost between 90% and 99%. The performance of this integrated model was evaluated by showing the difference in accuracy between the integrated model and the general SEIR model. The result shows that the integrated model is more accurate than the general SEIR model while predicting the number of confirmed cases in Bangladesh. |
| first_indexed | 2024-03-10T13:06:06Z |
| format | Article |
| id | doaj.art-b65b0d23cd4f49829396c1e35270930c |
| institution | Directory Open Access Journal |
| issn | 1999-4893 |
| language | English |
| last_indexed | 2024-03-10T13:06:06Z |
| publishDate | 2021-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Algorithms |
| spelling | doaj.art-b65b0d23cd4f49829396c1e35270930c2023-11-21T11:07:43ZengMDPI AGAlgorithms1999-48932021-03-011439410.3390/a14030094An Integrated Neural Network and SEIR Model to Predict COVID-19Sharif Noor Zisad0Mohammad Shahadat Hossain1Mohammed Sazzad Hossain2Karl Andersson3Department of Computer Science and Engineering, University of Chittagong, Chittagong 4331, BangladeshDepartment of Computer Science and Engineering, University of Chittagong, Chittagong 4331, BangladeshDepartment of Computer Science and Engineering, University of Liberal Arts Bangladesh, Dhaka 1209, BangladeshDepartment of Computer Science, Electrical and Space Engineering, Luleå University of Technology, 93187 Skellefteå, SwedenA novel coronavirus (COVID-19), which has become a great concern for the world, was identified first in Wuhan city in China. The rapid spread throughout the world was accompanied by an alarming number of infected patients and increasing number of deaths gradually. If the number of infected cases can be predicted in advance, it would have a large contribution to controlling this pandemic in any area. Therefore, this study introduces an integrated model for predicting the number of confirmed cases from the perspective of Bangladesh. Moreover, the number of quarantined patients and the change in basic reproduction rate (the <i>R<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mrow></mrow><mn>0</mn></msub></semantics></math></inline-formula></i>-value) can also be evaluated using this model. This integrated model combines the SEIR (Susceptible, Exposed, Infected, Removed) epidemiological model and neural networks. The model was trained using available data from 250 days. The accuracy of the prediction of confirmed cases is almost between 90% and 99%. The performance of this integrated model was evaluated by showing the difference in accuracy between the integrated model and the general SEIR model. The result shows that the integrated model is more accurate than the general SEIR model while predicting the number of confirmed cases in Bangladesh.https://www.mdpi.com/1999-4893/14/3/94COVID-19coronavirusSARS-CoV-22019-nCoVSEIRneural network |
| spellingShingle | Sharif Noor Zisad Mohammad Shahadat Hossain Mohammed Sazzad Hossain Karl Andersson An Integrated Neural Network and SEIR Model to Predict COVID-19 Algorithms COVID-19 coronavirus SARS-CoV-2 2019-nCoV SEIR neural network |
| title | An Integrated Neural Network and SEIR Model to Predict COVID-19 |
| title_full | An Integrated Neural Network and SEIR Model to Predict COVID-19 |
| title_fullStr | An Integrated Neural Network and SEIR Model to Predict COVID-19 |
| title_full_unstemmed | An Integrated Neural Network and SEIR Model to Predict COVID-19 |
| title_short | An Integrated Neural Network and SEIR Model to Predict COVID-19 |
| title_sort | integrated neural network and seir model to predict covid 19 |
| topic | COVID-19 coronavirus SARS-CoV-2 2019-nCoV SEIR neural network |
| url | https://www.mdpi.com/1999-4893/14/3/94 |
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