Modelling Analysis of Covid-19 Infections in India and Prediction of Daily Cases in 2021
DOI:
https://doi.org/10.12723/mjs.54.5Keywords:
Covid - 19 Data, Least square model fitting, prediction of fall times in 2021Abstract
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.
References
https://ourworldindata.org/coronaviruss
https://lmfit.github.io/lmfit-py/index.html
https://lmfit.github.io/lmfit-py/builtin_models.html
https://en.wikipedia.org/wiki/Normal_distribution
https://en.wikipedia.org/wiki/Skew_normal_distribution
https://en.wikipedia.org/wiki/Moffat_distribution
Additional Files
Published
Issue
Section
License
Copyright (c) 2020 M N Anandaram, N G Puttaswamy
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
