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COVID-19 Epidemic Analysis in India with Multi-Source State-Level Datasets.

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This study analyzed COVID-19 in India using machine learning. The Transformer model accurately predicted case spread, with Facebook mobility data being most effective for confirmed cases.

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Area of Science:

  • Epidemiology
  • Artificial Intelligence
  • Public Health

Background:

  • The COVID-19 pandemic presents a global health and economic crisis.
  • Research on COVID-19 in less developed countries, like India, is limited.
  • Artificial intelligence (AI) offers potential for disease diagnosis and prediction.

Purpose of the Study:

  • To analyze the COVID-19 epidemic in India.
  • To build and compare machine learning models for predicting COVID-19 spread.
  • To identify effective datasets for epidemiological modeling in India.

Main Methods:

  • Collected COVID-19 data from various sources specific to India.
  • Developed and evaluated multiple machine learning models, including the Transformer model.
  • Utilized different combinations of input features for model training and prediction.
  • Assessed the predictive power of the Facebook mobility dataset.

Main Results:

  • The Transformer model demonstrated the highest precision in predicting COVID-19 spread.
  • The Facebook mobility dataset proved most effective for predicting confirmed COVID-19 cases.
  • Existing datasets were found to be less effective for predicting COVID-19 related deaths.

Conclusions:

  • Machine learning, particularly the Transformer model, shows promise for predicting infectious disease outbreaks in India.
  • Mobility data is a crucial factor in forecasting confirmed cases.
  • Further research is needed to improve models for predicting mortality rates.