Regression Analysis of COVID-19 Spread in India and its Different States

  1. Poonam Chauhan
  2. Ashok Kumar
  3. Pooja Jamdagni

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Abstract

Linear and polynomial regression model has been used to investigate the COVID-19 outbreak in India and its different states using time series epidemiological data up to 26 th May 2020. The data driven analysis shows that the case fatality rate (CFR) for India (3.14% with 95% confidence interval of 3.12% to 3.16%) is half of the global fatality rate, while higher than the CFR of the immediate neighbors i.e . Bangladesh, Pakistan and Sri Lanka. Among Indian states, CFR of West Bengal (8.70%, CI: 8.21–9.18%) and Gujrat (6.05%, CI: 4.90–7.19%) is estimated to be higher than national rate, whereas CFR of Bihar, Odisha and Tamil Nadu is less than 1%. The polynomial regression model for India and its different states is trained with data from 21 st March 2020 to 19 th May 2020 (60 days). The performance of the model is estimated using test data of 7 days from 20 th May 2020 to 26 th May 2020 by calculating RMSE and % error. The model is then used to predict number of patients in India and its different states up to 16 th June 2020 (21 days). Based on the polynomial regression analysis, Maharashtra, Gujrat, Delhi and Tamil Nadu are continue to remain most affected states in India.

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    SciScore for 10.1101/2020.05.29.20117069: (What is this?)

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  2. Version published to 10.1101/2020.05.29.20117069 on medRxiv