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. 2022 May 10:1-10.
doi: 10.1007/s11063-022-10834-5. Online ahead of print.

COVID-19 Variants and Transfer Learning for the Emerging Stringency Indices

Ayesha Sohail  1 , Zhenhua Yu  2 , Alessandro Nutini  3
Affiliations

COVID-19 Variants and Transfer Learning for the Emerging Stringency Indices

Ayesha Sohail et al. Neural Process Lett. .

Abstract

The pandemics in the history of world health organization have always left memorable hallmarks, on the health care systems and on the economy of highly effected areas. The ongoing pandemic is one of the most harmful pandemics and is threatening due to its transformation to more contiguous variants. Here in this manuscript, we will first outline the variants and then their impact on the associated health issues. The deep learning algorithms are useful in developing models, from a higher dimensional problem/ dataset, but these algorithms fail to provide insight during the training process and do not generalize the conditions. Transfer learning, a new subfield of machine learning has acquired fame due to its ability to exploit the information/learning gained from a previous process to improve generalization for the next. In short, transfer learning is the optimization of the stored knowledge. With the aid of transfer learning, we will show that the stringency index and cardiovascular death rates were the most important and appropriate predictors to develop the model for the forecasting of the COVID-19 death rates.

Keywords: Artificial intelligence; COVID-19 socioeconomic problems; Stringency index; Transfer learning.

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Conflict of interest statement

Conflict of interestThe authors declare that there is no conflict of interest.

Figures

Fig. 1
Fig. 1
COVID-19 data analysis flow chart with deep learning and transfer learning
Fig. 2
Fig. 2
Stringency index over period of 55 weeks Feb 2020 till Feb 2021. Group A with SI< 50; group B with 50 ≤SI<80; and group C with 80 ≤SI≤ 130
See this image and copyright information in PMC

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