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. 2023 Feb 11;9(2):e13672.
doi: 10.1016/j.heliyon.2023.e13672. eCollection 2023 Feb.

Investigating the effect of vaccinated population on the COVID-19 prediction using FA and ABC-based feed-forward neural networks

Ebrahim Noroozi-Ghaleini  1 , Mohammad Javad Shaibani  2
Affiliations

Investigating the effect of vaccinated population on the COVID-19 prediction using FA and ABC-based feed-forward neural networks

Ebrahim Noroozi-Ghaleini et al. Heliyon. .

Abstract

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.

Keywords: COVID-19; Forecasting; Modeling; Neural networks; Vaccinated population.

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Conflict of interest statement

We declare that we have no financial or non-financial interests to disclose.

Figures

Fig. 1
Fig. 1
The modeling flowchart used in this work.
Fig. 2
Fig. 2
The modeling flowchart of the ANN-FA.
Fig. 3
Fig. 3
The modeling flowchart of the ANN-ABC.
Fig. 4
Fig. 4
Total rank of ANN based on various number of hidden neurons.
Fig. 5
Fig. 5
ANN train (Panel I) and test (Panel II) results with LM algorithm.
Fig. 6
Fig. 6
Relative minimum pitfall in ANN.
Fig. 7
Fig. 7
The RMSE indicator for ANN-ABC (Panel I) and ANN-FA (panel II) with different swarm sizes up to 500 epochs.
Fig. 8
Fig. 8
Train and test results of for ANN-ABC (Panels I and II respectively) and ANN-FA (Panels III and IV respectively).
See this image and copyright information in PMC

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