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

Ayesha Sohail1, Zhenhua Yu2, Alessandro Nutini3

  • 1Department of Mathematics, Comsats University Islamabad, Lahore Campus, Lahore, Pakistan.

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|May 16, 2022
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Transfer learning effectively forecasts COVID-19 deaths by utilizing stringency index and cardiovascular death rates as key predictors. This machine learning approach optimizes knowledge for better generalization in public health crisis modeling.

Keywords:
Artificial intelligenceCOVID-19 socioeconomic problemsStringency indexTransfer learning

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Area of Science:

  • Public Health
  • Machine Learning
  • Epidemiology

Background:

  • Pandemics significantly impact global healthcare systems and economies.
  • Emerging infectious disease variants pose continuous threats to public health.
  • Deep learning models, while powerful, often lack interpretability and generalizability.

Purpose of the Study:

  • To explore the impact of pandemic variants on health issues.
  • To apply transfer learning for improved COVID-19 death rate forecasting.
  • To identify optimal predictors for pandemic modeling.

Main Methods:

  • Utilized transfer learning, a machine learning technique.
  • Developed predictive models for COVID-19 death rates.
  • Identified key predictor variables from available datasets.

Main Results:

  • Transfer learning demonstrated effectiveness in forecasting.
  • The stringency index was identified as a crucial predictor.
  • Cardiovascular death rates emerged as another significant predictor.

Conclusions:

  • Transfer learning offers a robust method for pandemic forecasting.
  • Stringency index and cardiovascular death rates are vital for accurate COVID-19 death rate prediction.
  • Optimized knowledge transfer enhances public health crisis management models.