Vol. 19 No. 3 (2020): Mapana Journal of Sciences
Research Articles

Modelling Analysis of Covid-19 Infections in India and Prediction of Daily Cases in 2021

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  • M N Anandaram,
  • N G Puttaswamy
M N Anandaram
Bangalore University
Bio
N G Puttaswamy
Bangalore University
Bio
DOI: https://doi.org/10.12723/mjs.54.5

Published 2021-09-07

Keywords

  • Covid - 19 Data,
  • Least square model fitting,
  • prediction of fall times in 2021

Copyright (c) 2020

Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Abstract

In this paper the data for dailyconfirmed new casesconcerning the rise and fall of the Covid-19 (aka, coronavirus) pandemic infection in India for the nine month period starting from the first March 2020 has been subjected to a non linear least square fitting analysis using Gaussian, Skewed-Gaussian, Moffat, andVoigt model functions.The fitting parameters determined by the Python software package LMFIT are then used to compare the predicted remission times of Covid-19pandemic during 2021. It is found that while the Gaussian, Skewed-Gaussian and Moffat models predictlowlevels byabout March/April 2021; Voigt and other models predict longertimes to reach samelow endemic levels.

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References

  1. https://ourworldindata.org/coronaviruss
  2. https://ourworldindata.org/coronavirus-data-explorer?zoomToSelection=true&time=2020-02-07..latest&country=~IND&region=World&casesMetric=true&interval=daily&hideControls=true&smoothing=0&pickerMetric=total_cases&pickerSort=desc
  3. https://lmfit.github.io/lmfit-py/index.html
  4. https://lmfit.github.io/lmfit-py/builtin_models.html
  5. https://en.wikipedia.org/wiki/Normal_distribution
  6. https://en.wikipedia.org/wiki/Skew_normal_distribution
  7. https://en.wikipedia.org/wiki/Moffat_distribution
  8. https://en.wikipedia.org/wiki/Voigt_profile
  9. https://lmfit-py.readthedocs.io/en/stable/fitting.html