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Deep-Data-Driven Neural Networks for COVID-19 Vaccine Efficacy
Thomas K Torku1, Abdul Q M Khaliq2, Khaled M Furati3
1Department of University Studies, Middle Tennessee State University, Murfreesboro, TN 37132, USA.
Epidemiologia (Basel, Switzerland)
|November 23, 2022
Summary
Developing effective COVID-19 vaccination strategies is crucial. This study shows that higher vaccination rates and efficacy significantly reduce disease spread and transmission, aiding a return to normalcy.
Area of Science:
- Epidemiology
- Computational Biology
- Public Health
Background:
- Vaccination strategies are essential for public health and economic recovery.
- Modeling disease spread, like COVID-19, informs policy decisions.
Purpose of the Study:
- To develop and analyze a COVID-19 vaccination model incorporating efficacy.
- To accurately predict daily COVID-19 cases using machine learning.
- To evaluate the impact of vaccination rates and efficacy on disease transmission.
Main Methods:
- A COVID-19 vaccination model was developed and analyzed.
- Epidemiological parameters were learned using a feed-forward neural network.
- A hybrid recurrent neural network (RNN) and residual neural network (ResNet) approach was implemented for case prediction.
- Model performance was validated using error metrics and k-fold cross-validation.
Main Results:
- The hybrid ResNet-GRU architecture demonstrated superior accuracy in predicting daily COVID-19 cases.
- Data-driven simulations confirmed that increased vaccination rates and higher efficacy reduce infectiousness and the basic reproduction number.
- The model was applied to COVID-19 data from Tennessee, USA.
Conclusions:
- A data-driven modeling approach using hybrid neural networks can accurately predict disease spread.
- Higher vaccination efficacy is a key factor in mitigating disease transmission and achieving normalcy.
- This research provides valuable insights for public health policy and vaccination strategies.
