Investigating the effect of vaccinated population on the COVID-19 prediction using FA and ABC-based feed-forward neural networks
Since 2019, the coronavirus outbreak has caused many catastrophic events all over the world. At the current time, the massive vaccination has been considered as the most efficient way to fight against the pandemic. This study schemes to explain and model COVID-19 cases by considering the vaccination...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-02-01
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| Series: | Heliyon |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844023008794 |
| _version_ | 1811161689153863680 |
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| author | Ebrahim Noroozi-Ghaleini Mohammad Javad Shaibani |
| author_facet | Ebrahim Noroozi-Ghaleini Mohammad Javad Shaibani |
| author_sort | Ebrahim Noroozi-Ghaleini |
| collection | DOAJ |
| description | Since 2019, the coronavirus outbreak has caused many catastrophic events all over the world. At the current time, the massive vaccination has been considered as the most efficient way to fight against the pandemic. This study schemes to explain and model COVID-19 cases by considering the vaccination rate. We utilized an amalgamation of neural network (NN) with two powerful optimization algorithms, i.e., firefly algorithm and artificial bee colony. For validating the models, we employed the COVID-19 datasets regarding the vaccination rate and the total confirmed cases for 51 states since the beginning of vaccination in the US. The numerical experiment indicated that by considering the vaccinated population, the accuracy of NN increases exponentially when compared with the same NN in the absence of the vaccinated population. During the next stage, the NN with vaccinated input data is elected for firefly and bee optimizing. Based upon the firefly optimizing, 93.75% of COVID-19 cases can be explained in all states. According to the bee optimizing, 92.3% of COVID-19 cases is explained since the massive vaccination. Overall, it can be concluded that the massive vaccination is the key predictor of COVID-19 cases on a grand scale. |
| first_indexed | 2024-04-10T06:19:27Z |
| format | Article |
| id | doaj.art-5635ed087e554ea6a19d1f7f80fa2b0c |
| institution | Directory Open Access Journal |
| issn | 2405-8440 |
| language | English |
| last_indexed | 2024-04-10T06:19:27Z |
| publishDate | 2023-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj.art-5635ed087e554ea6a19d1f7f80fa2b0c2023-03-02T05:02:26ZengElsevierHeliyon2405-84402023-02-0192e13672Investigating the effect of vaccinated population on the COVID-19 prediction using FA and ABC-based feed-forward neural networksEbrahim Noroozi-Ghaleini0Mohammad Javad Shaibani1Mining Engineering Department, Faculty of Engineering, Tarbiat Modares University, Tehran, Iran; Corresponding author.Department of Health Management and Economics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran; Corresponding author.Since 2019, the coronavirus outbreak has caused many catastrophic events all over the world. At the current time, the massive vaccination has been considered as the most efficient way to fight against the pandemic. This study schemes to explain and model COVID-19 cases by considering the vaccination rate. We utilized an amalgamation of neural network (NN) with two powerful optimization algorithms, i.e., firefly algorithm and artificial bee colony. For validating the models, we employed the COVID-19 datasets regarding the vaccination rate and the total confirmed cases for 51 states since the beginning of vaccination in the US. The numerical experiment indicated that by considering the vaccinated population, the accuracy of NN increases exponentially when compared with the same NN in the absence of the vaccinated population. During the next stage, the NN with vaccinated input data is elected for firefly and bee optimizing. Based upon the firefly optimizing, 93.75% of COVID-19 cases can be explained in all states. According to the bee optimizing, 92.3% of COVID-19 cases is explained since the massive vaccination. Overall, it can be concluded that the massive vaccination is the key predictor of COVID-19 cases on a grand scale.http://www.sciencedirect.com/science/article/pii/S2405844023008794COVID-19Vaccinated populationForecastingNeural networksModeling |
| spellingShingle | Ebrahim Noroozi-Ghaleini Mohammad Javad Shaibani Investigating the effect of vaccinated population on the COVID-19 prediction using FA and ABC-based feed-forward neural networks Heliyon COVID-19 Vaccinated population Forecasting Neural networks Modeling |
| title | Investigating the effect of vaccinated population on the COVID-19 prediction using FA and ABC-based feed-forward neural networks |
| title_full | Investigating the effect of vaccinated population on the COVID-19 prediction using FA and ABC-based feed-forward neural networks |
| title_fullStr | Investigating the effect of vaccinated population on the COVID-19 prediction using FA and ABC-based feed-forward neural networks |
| title_full_unstemmed | Investigating the effect of vaccinated population on the COVID-19 prediction using FA and ABC-based feed-forward neural networks |
| title_short | Investigating the effect of vaccinated population on the COVID-19 prediction using FA and ABC-based feed-forward neural networks |
| title_sort | investigating the effect of vaccinated population on the covid 19 prediction using fa and abc based feed forward neural networks |
| topic | COVID-19 Vaccinated population Forecasting Neural networks Modeling |
| url | http://www.sciencedirect.com/science/article/pii/S2405844023008794 |
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