Introduction

Severe acute respiratory syndrome (SARS) coronavirus (SARS-CoV-2) is a novel virus that was recognized at the end of December 2019 around the globe. [Das, 2022]. India is one of the largest Asian countries that has suffered from COVID-19. COVID gives an uncertain break in every stream of every nation. India is suffering from economic difficulties during the COVID-19 pandemic.

For India biggest problem is not only the high infection rate but also the vaccination of the high populated country. Vaccination provides significant relief from the COVID-19 pandemic. India conducts the vaccination process in three phases (Dose-1, Dose-2, and booster).

On 19 March, 2020, the first COVID-19 case was recorded in Chhattisgarh among an international traveler. Lockdown announced on 25 March 2020. Covid cases increase with respect to time. The first and second waves of the COVID-19 pandemic created a situation of chaos. Chhattisgarh has 10.08 lakh confirmed cases, active cases 841, number of deaths 13,600, recovered 9.94 lakh, Covid tests 1.50 CR, daily positive rate 0.75%, Positivity Rate 6.73%, Death Rate 1.35%. (Covid tracking).

For the government, it is easy to obtain information or classify any set of data. It provides additional information about the source dataset. For data related to Covid- 19 have extra information like confirmed new cases, new death cases, and daily.

Vaccination cases, previous health issues, and the COVID-19 phases help us better understand COVID-19.

Model Data: 

The timeline of data is from 01.04.2020 to 05.05.2023 of Chhattisgarh. The data is collected from the website https://covidtracking.in/coronavirus-cases/chhattisgarh visited at 10.05.2023.

Study Area

This study is geographically based in Chhattisgarh, a state of India. According to the 2011 Census, Chhattisgarh has a population of 2.42 crore, constituting 2.11% of the country's total population. The study area covers 1,35,191 square kilometres, accounting for 4.4% of India’s total geographical area. The state has an urban population of 23.24% and a sex ratio of 991 (Census, 2011).

Geographically, Chhattisgarh is bordered by seven states: Uttar Pradesh to the north, Madhya Pradesh to the north-west, Maharashtra to the south-west, Jharkhand to the north-east, Odisha to the east, and Andhra Pradesh and Telangana to the south. The state has a tropical climate, with summer temperatures reaching up to 49°C.

Materials and Methods

For predicting deaths, we use linear regression with one independent variable, and for two or more independent variables, we use multiple linear regression.

In simple linear regression: - One dependent variable Y and one independent variable X.

For multiple Regression modelling: -

Where:  Y    = Dependent variable.

  = Independent variables.

α    = Constant.

  = Regression coefficient of the variable .

Model Implementation

For the COVID-19 prediction model, we use multiple regression with three stages of independent variables. In the first stage, we predict death based on recovery in that time. In the Second stage, based on recovered and confirmed cases, we predict COVID-19 deaths. Simultaneously, in the third stage, we predict deaths based on recoveries, confirmed cases, and vaccinations as independent variables.

Model stage-1: This model is based on Recover as independent variable.

Table 01:  Analysis of model stage-1

Figure 1: Predicted death against recover

Result:  Graph shows that their red line is actual death and the green dots are our model prediction. There was a high fluctuation between the model and the actual data.

Model stage – 2: This model is based on Recover and confirm case as independent variables.

Table 02:  Analysis of model stage-2

Figure 2: Predicted deaths against recovered and confirm case

Result: Graph shows that their red line is actual death and the green dots are our model prediction. There was an average fluctuation between the model and the actual data. But compared to recovering data, this is a significant model in the context of curve fitting.

Model stage – 3: This model is based on Recover, confirm case and vaccination as independent variable. 

Table 03:  Analysis of model stage-3

Graph 3: Predicated death against recovery, confirmed cases, and vaccination

Result: The graph shows that the red line represents the actual deaths, and the green dots are our model's predictions. There was a minor fluctuation between the model and the actual data. Curve fitting shows that the model predicts the same as the actual COVID deaths. The stage-3 model is better than the other model.

Software: Many software and programming tools are used for model analysis and prediction. Our model was analyzed using multiple linear regression in the GRETL software. The model essentially uses the least-squares method (OLS) to estimate the effect of vaccination. In the OLS method, our model predicts a value by minimizing the model's error.

Conclusion: Model measurement- Model performance is measured by R2 and fitting in a graph. R² is the coefficient of determination found for all 3 stages is based on R² and curve fitting, we conclude that the vaccination variable supports the predictive model value because vaccination plays a major role in COVID-19 vitals.

Models before vaccination are getting weaker and have inconclusive results. We say that vaccination affects the prediction models; we need to include it as a parameter for better prediction.

Conflict of interest Author declares that there is no conflict of interest.

Funding information not applicable.

Ethical approval not applicable.

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