## Introduction

Let’s come to know about the COVID-19 status in India by the Sopher Index and Weighted Index analysis. Coronavirus, technically COVID-19 gradually increasing its vigorous harmful effects not only in Indian socioeconomic structure but also worldwide pandemic sociology-cultural-economic destruction. Our human civilization is at the limiting point of the trench. Yet we will overcome the bad patch of time very soon with our endless efforts of the invention in medical science. I will discuss here the present spreading status of COVID-19 in Indian states with the help of geographic analytical tools i.e. Sopher Index and Weighted Index.

Is West Bengal being the most COVID-19 vulnerable state in India? Which States are the pioneer in the queue of COVID-19 affected states in India at present? Which States are not in danger of COVID-19? Is COVID-19 gradually increase or decrease its spreading effect on the Indian States? You will get the answer to all these types of questions in this article if you go through it. Its analytical result will expose all the quarries regarding the spreading status of COVID-19 in the different states of India.

**Assumption:**

Before going to analysis of the COVID – 19 status in India by the Sopher Index and Weighted Index, I assure my readers that based on the following consideration I will proceed and conclude the research outcome.

- The entire data sources are completely secondary in nature. So the result of the analysis completely depending upon it. The main URL’s from where I collected and compiled the data, are https://www.covid19india.org/, https://www.mygov.in/covid-19, https://www.who.int, https://www.worldometers.info/coronavirus/country/india/, apart from these sources I also collected the data of different sources like state press bulletins, official (CM, Health M) handles, PBI, Press Trust of India, ANI reports. Considering the equitability of the data I proceed for cross-checking and after it for analysis.
- I use the geographical tools mainly Weighted Index analysis, Sopher Disparity Index, and some statistical tools for the analysis. I use the computerized analytical tools like XLSTAT and others to achieve accuracy in the analytic result. Any unwanted mistakes are considered as manual error.
- I use the QGIS platform to represent the map. The entire mapping procedure and analytic attributes carefully handled. In spite of this if any unwanted distortion or mismanagement in mapping and analyzing may be treated as an execution error.
- The analysis is completely for higher studies in geographical planning not for blowing any massage of provincialism.
- COVID -19 confirm, active and deceased population are the three main spreading effects of coronavirus, and hence they used for multivariable analysis to imposed weighted value of each.
- COVID -19 confirm and recovered population are significant for the victory of the fight against coronavirus so I use their percentage value to prepare a recovery map by Sopher disparity Index.

**Methodology:**

I follow the step-wise methodology to analyze the COVID -19 status in India by the Sopher Index and Weighted Index, as follows.

- Sampling: I use a random sampling method to select three dates from 1st April 2020 to 10th May 2020. Finally, I select the three dates are 22nd April, 3rd May & 7th May to avoid the data bias.
- Weighted Index Analysis for COVID-19 impact: To measure the spreading effect of coronavirus I consider COVID -19 confirm, active and deceased population are important factors. I use these three main factors for multivariable analysis to imposed the weighted value of each and proceed to my calculation.
- Sopher Disparity Index for recovery mapping: I use the data of COVID -19 confirm and recovered population in percentage value. I ignore the total population of the states rather than emphasizing on COVID -19 confirm cases of the states to measure the recovery rate.
- Comparative Analysis of weighted value for determination of trend: I compare the weighted value of three selected dates and prepare the chart to understand the trend as a whole.

**Analysis:**

I use the variable index method to analyze the weight of three variables i.e. COVID -19 confirm, active and deceased population.

Hence I use the formula as per the Boudeville outline.

W= {(LOG(X1)*W1)+(LOG(X2)*W2)+(LOG(X3)*W3) / (W1+W2+W3)}

Where:

W=Weighted index,

X1= COVID -19 confirm population

X2= COVID -19 active population

X3= COVID -19 deceased population

W1= Ratio of Mean and Standard deviation of COVID -19 confirm population.

W2= Ratio of Mean and Standard deviation of COVID -19 active population.

W3= Ratio of Mean and Standard deviation of COVID -19 deceased population.

I calculate the W value of the particular dates and based on the last date i.e. 7^{th} May 2020 I prepare the vulnerability map to represent the COVID-19 effect on the different states.

I use another analytical tool to represent the recovery rate of the states. Here I use the Sopher Disparity index or simply Disparity Index based on the formula proposed by Sopher in 1974.

Hence I use the formula as:

DI = {Log (X2 /X1) + Log (100 – X1) / (100 – X2)}

DI = Disparity Index

X2 = Percentage of COVID -19 confirm population.

X1 = Percentage of COVID -19 recovered population. As the log value of zero is indefinite so it is always be remembered that the 1st variable is always greater than the 2nd variable i.e. X2 ≥ X1. The value of the Sopher index never is in the minus figure.

**Calculation of W value of 22nd April Data:**

Here is the calculation table of the 22^{nd} April 2020 data. I was unable to collect the deceased population of some states. So the Weighted Value (W) of that state is indefinite. For the convenience of representation, the value considers as zero.

**Outcomes of W value of 22nd April Data:**

The figure represents the outcome of the analysis of the 22nd April 2020 data. Here mean (X-) of Weighted Value (W) is 2.99 and Standard Deviation (SD=Ϭ) is 2.12. Most of the Indian States belong within the Mean plus-minus one Standard Deviation (X- ± 1 Ϭ). It can be concluded that the COVID-19 vulnerability of these states is not too extant but predictable for future increases. Hence the Group of those states is considered as a non-vulnerable group of states.

The states Maharashtra, Gujarat, Rajasthan, Delhi, and Madhya Pradesh lies above the mean plus-minus one Standard Deviation (X- ± 1 Ϭ). The COVID-19 vulnerability of these states is too extant. Hence the Group of those states is considered a vulnerable group of states.

The states Chandigarh, Meghalaya, Chhattisgarh, Andaman and Nicobar Islands, Ladakh, Puducherry, Tripura, and Mizoram lie below the mean plus-minus one Standard Deviation (X– ± 1 Ϭ). It is also clear that the COVID-19 vulnerability of these states is almost zero. Hence the Group of those states is considered as zero vulnerable groups of states.

**Calculation of W value of 3rd May Data:**

Here is the calculation table in which I tabulate the Weighted Value (W) of three variables of the data on 3^{rd} May 2020. I searched the data for the deceased population of sum states but failed to collect except the state Chhattisgarh. So the Weighted (W) values of those states are indefinite. For the convenience of representation the value I consider the value of those states as zero.

**Outcomes of W value of 3**^{rd}** May Data:**

^{rd}

The following figure represents the outcome of the Weighted Index analysis of the 3rd May 2020 data. Here mean (X-) of Weighted Value (W) is 3.11 and Standard Deviation (SD=Ϭ) is 2.11. Most of the Indian States belong in between the value of mean to one Standard Deviation in both sides of the X-axis (X- ± 1 Ϭ). It can be concluded that the COVID-19 vulnerability of these states is not too extant but predictable for future increases. Hence the group of states may be considered as nonvulnerable states.

We are seeing almost the same figure as previous data here. The states Maharashtra, Gujarat, Rajasthan, Delhi, and Madhya Pradesh lies above the mean plus-minus one Standard Deviation (X^{–} ± 1 Ϭ). The COVID-19 vulnerability of these states is too extant.

Only the state of Chhattisgarh increases its deceased rate from zero to two, so the state changes its location from a zero vulnerable group of states to a nonvulnerable group of states. The states Chandigarh, Meghalaya, Chhattisgarh, Andaman and Nicobar Islands, Ladakh, Puducherry, Tripura, and Mizoram lie below the mean plus-minus one Standard Deviation (X^{–} ± 1 Ϭ). It is also clear that the COVID-19 vulnerability of these states is almost zero.

**Calculation of W value of 7th May Data:**

Here is the calculation table in which I tabulate the Weighted Value (W) of three variables of the 7^{th} May 2020 data. I searched the data for the deceased population of sum states in different sources but failed to collect except the state Puducherry. So the Weighted (W) values of those states are indefinite. For the convenience of representation of the value, I consider the value of those states as zero.

**Outcomes of W value of 7**^{th}** May Data:**

^{th}

The following figure represents the outcome of the Weighted Index analysis of the 7^{th} May 2020 data. Here mean (X^{–}) of Weighted Value (W) is 3.35 and Standard Deviation (SD=Ϭ) is 2.03. Most of the Indian States belong in between the value of mean to one Standard Deviation in both sides of the X-axis (X- ± 1 Ϭ). It can be concluded that the COVID-19 vulnerability of these states is not too extant but predictable for future increases. The group of States is presently nonvulnerable but the situation may be changed at any time.

We are seeing slight changes figure of previous data here. The state Maharashtra, Gujarat, Rajasthan, and Delhi lies above the mean plus-minus one Standard Deviation (X- ± 1 Ϭ). The State Madhya Pradesh changes its location from this group to a nonvulnerable group of states.

The states Chandigarh, Andaman and Nicobar Islands, Ladakh, Puducherry, Tripura, and Mizoram lie below the mean plus-minus one Standard Deviation (X^{–} ± 1 Ϭ). It is also clear that the COVID-19 vulnerability of these states is almost zero.

## Video:

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## Conclusion

From the analysis of Sopher’s Disparity Index for the Covid-19 cases in Indian states of above mentioned three different times, it is clear that the states Maharashtra, Gujarat, Rajasthan, and Delhi were worst affected. The states are most vulnerable in terms of spreading Covid-19 cases. We can suggest the concerned authority take the necessary steps immediately for those particular states. Otherwise, it will be spread in neighboring stets rapidly.

The first part of the topic (**COVID-19 status in India by Sopher and Weighted Index analysis**) ends here. Make sure to go through the full research article. Check the second part of the research article Sopher and Weighted Index analysis part 2. For any kind of doubts about the research paper fill free to comment down below.