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

M. N. Anandaram, N. G. Puttaswamy
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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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印度2019冠状病毒感染模型分析及2021年每日病例预测
在本文中,使用高斯、偏高斯、莫法特和voigt模型函数,对从2020年3月1日开始的9个月期间,与印度Covid-19(又名冠状病毒)大流行感染的上升和下降有关的每日确诊新病例数据进行了非线性最小二乘拟合分析。然后使用Python软件包LMFIT确定的拟合参数对2021年covid -19大流行的预测缓解时间进行比较。研究发现,高斯、偏高斯和莫法特模型预测的水平大约在2021年3月/ 4月;Voigt和其他模型预测,达到同样的流行水平需要更长的时间。
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