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Deep-Data-Driven Neural Networks for COVID-19 Vaccine Efficacy
- Source :
- Epidemiologia, Vol 2, Iss 4, Pp 564-586 (2021)
- Publication Year :
- 2021
- Publisher :
- MDPI AG, 2021.
-
Abstract
- 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.
Details
- Language :
- English
- ISSN :
- 26733986
- Volume :
- 2
- Issue :
- 4
- Database :
- Directory of Open Access Journals
- Journal :
- Epidemiologia
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.542ae332d3f14e3481b1d92eec513c37
- Document Type :
- article
- Full Text :
- https://doi.org/10.3390/epidemiologia2040039