| Autor: |
Thomas K. Torku, Abdul Q. M. Khaliq, Khaled M. Furati |
| Jazyk: |
angličtina |
| Rok vydání: |
2021 |
| Předmět: |
|
| Zdroj: |
Epidemiologia, Vol 2, Iss 4, Pp 564-586 (2021) |
| Druh dokumentu: |
article |
| ISSN: |
2673-3986 |
| DOI: |
10.3390/epidemiologia2040039 |
| Popis: |
Vaccination strategies to lessen the impact of the spread of a disease are fundamental to public health authorities and policy makers. The socio-economic benefit of full return to normalcy is the core of such strategies. In this paper, a COVID-19 vaccination model with efficacy rate is developed and analyzed. The epidemiological parameters of the model are learned via a feed-forward neural network. A hybrid approach that combines residual neural network with variants of recurrent neural network is implemented and analyzed for reliable and accurate prediction of daily cases. The error metrics and a k-fold cross validation with random splitting reveal that a particular type of hybrid approach called residual neural network with gated recurrent unit is the best hybrid neural network architecture. The data-driven simulations confirm the fact that the vaccination rate with higher efficacy lowers the infectiousness and basic reproduction number. As a study case, COVID-19 data for the state of Tennessee in USA is used. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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