Impact of COVID-19 Vaccinations in India - A Statewise Analysis (preprint)

Objectives: The aim of this paper is to perform a Statewise Analysis of the Second Covid Wave experienced by India using the Gompertz Curves and to assess the role played by vaccinations in reshaping the trajectory of Covid Infections for India. A total of 21 prominent states are chosen for the analysis encompassing 97\% of the Indian population. Since the vaccination program in India was rolled out after the First wave of infections had almost subsided, the current analysis is only relevant for the Second Wave. Methods: We will try to explore how the different properties of the Gompertz Curves can be used as a convenient tool to study the COVID-19 outbreak in India. The impact of vaccinations has also been studied at the state level to assess the extent to which the roll out of vaccination program has augmented the covid scenario of India. Vaccinations have been incorporated in the analysis by taking the daily cumulative number of individuals having the first and second shots of vaccine in each state as the explanatory variables. Results: The preliminary question that the paper tries to investigate is whether the vaccines capable of checking infection growth. Out of the chosen 21 states, 16 states show positive outcomes with some observable ambiguity for the states of Telangana, West Bengal, Tamil Nadu, Rajasthan and Kerala. Conclusions: Our analysis found that most of the states (16 out of 21 states) has positive impact of vaccination in reducing the Covid-19 cases.

Prediction of Number of Covid-19 Affected Persons by Using Gompertz Curves (preprint)

The aim of this paper is to analyze the First and the Second Covid Waves experienced by India using the modified versions of Gompertz Curve (MGC) and to estimate the maximum number of affected individuals for each wave with the best possible accuracy. The time period of collected data is from 30th January 2020 to 11th July 2021. The entire dataset is segregated into two parts, i.e., for the First and the Second Waves and then modelled individually by the MGC. The robustness of the fits is checked, and then residuals are further modelled successively to improve the accuracy of the estimates. A key highlight of this paper is that our model can be implemented taking a smaller dataset with reasonable accuracy. Finally, a comparative analysis of the results has been performed with the Logistic Model and the ARIMA Models.

Long Run Predictions Using Gompertz Curves- A State Wise Analysis of COVID -19 Infections in India (preprint)

The aim of this paper is to perform a State wise Analysis of the First and the Second Covid Waves experienced by India using the Gompertz Curves and to estimate the maximum number of affected individuals for each wave with the best possible accuracy. A total of 21 large States are chosen for the analysis encompassing 97% of the Indian population. Data on cumulative number of cases is available till 31st October 2021. The entire dataset is segregated into two parts, i.e., the First and the Second Waves and then modelled individually by the Gompertz Curves with some generalizations. The predicted maximum cumulative numbers of Covid-19 affected individuals are found to be quite accurate. Besides, it is found to be possible to give a methodology how one can predict these numbers with a much smaller dataset. This is important as it can help the authorities in taking an informed decision on the efficient allocation of the limited health care resources. JEL Classification: E0, C32, C50, C53