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COVID-19 in India: Statewise Analysis and Prediction.

Palash Ghosh1,2, Rik Ghosh1, Bibhas Chakraborty3,4,5

  • 1Department of Mathematics, Indian Institute of Technology, Guwahati, India.

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|August 9, 2020
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Summary

This study analyzed COVID-19 spread in Indian states, categorizing them by infection severity. Maharashtra, Delhi, and Gujarat are severe, while Kerala shows controlled spread, guiding resource allocation.

Keywords:
30-day predictionCOVID-19SIS modeldaily infection ratedisease modelingexponential modellogistic model

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Area of Science:

  • Epidemiology
  • Public Health
  • Data Science

Background:

  • The COVID-19 pandemic, originating in Wuhan, China, rapidly spread globally, reaching India in January 2020.
  • India reported over 37,000 COVID-19 cases by May 3, 2020, with a rapidly increasing trend.

Purpose of the Study:

  • To analyze state-wise COVID-19 infection data in India.
  • To predict future infection numbers for each state over the next 30 days.
  • To aid state governments in optimizing healthcare resource allocation.

Main Methods:

  • Utilized logistic, exponential, and susceptible-infectious-susceptible models for prediction.
  • Developed an ensemble model combining logistic and exponential predictions.
  • Employed the maximum daily infection rate (DIR) over two weeks as a weighting factor and trend indicator.

Main Results:

  • Categorized Indian states into severe (e.g., Maharashtra, Delhi, Gujarat), moderate (e.g., Tamil Nadu, Rajasthan), and controlled (e.g., Kerala, Haryana) infection levels.
  • Achieved R-squared values above 0.90 for logistic and exponential models, indicating good fit.
  • Provided a web application for regularly updated COVID-19 forecasts.

Conclusions:

  • States with non-decreasing daily infection rates require intensified preventive measures.
  • States with decreasing daily infection rates can maintain current measures, aiming for sustained negative rates to declare the pandemic's end.