Possibilities of exponential or Sigmoid growth of Covid19 data in different states of India

Abstract

We have attempted to understand existing covid19 data of India, where growth of total and new cases with time in different states are kept as focal points. Identifying the last trend of exponential growth, mainly noticed in month of March, we have zoomed in its disaster possibilities by straight forward extrapolation of exponential growth. As a hopeful extrapolation, the existing data might be considered low time-axis values of Sigmoid-type function, whose growth might be saturated to values of 10 4 or 10 5 . To fulfill this expectation, a turning from increasing to decreasing trend in new case data should be noticed around April-May, which definitely demand extension of present lock-down with additional interventions.

Non-Linear Fitting of Sigmoidal Growth Curves to predict a maximum limit to the total number of COVID-19 cases in the United States

“…Bliznashki (2020) applied a Bayesian Logistic Growth model to the COVID-19 data cases in the period between 4 th of March and 31 th of March and found that the data in this interval have small number of available data points without upper asymptote to can be well fitted by this model, but it will be useful with more actualizated data. Mondal and Ghosh (2020) studied the scenarios of the exponential and sigmoid growth of COVID-19 total cases data for 15 states of India considering a initial exponential growth and a extrapolation with a sigmoid-type profile. Peirlinck et al (2020) estimated to the United States the nationwide peak of the outbreak on May 10, 2020 with 3 million infections across the United States.…”

Temporal Analysis of COVID-19 Peak Outbreak

“…However, there are several difficulties associated with creating ABMs such as integration with too many features, choice of model parameters, model results being either trivial or too complex [19]. The spread of COVID-19 in India has been investigated in many researches including [20][21] [22][23], but they laid little emphasis on post-model validation for peak COVID-19 timeline forecast. With this in mind, SIR model is explored in current research to forecast peak COVID-19 outbreak over a large population in India.…”

Searching the Sigmoid-type trend in Lock Down period covid19 data of India and its different states

“…To mark the lock down period, we have drawn two vertical red dotted lines at 25th March and 3rd May. Now, we will attempt to fit the total cases data from 25th March to onwards via different possible Sigmoid functions with standard form [7,8,9,3,4]…”

“…(4), 10 more states, having ignorable impact of imported data, are discussed. In both sections (3) and (4), the Sigmoid trend of data are analyzed and based on that pattern, we classified the 15 states into 3 gross categories. At the end, we have summarized the study with extracting interpretation in Sec.…”