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

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.