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

Year 2023, Volume: 15 Issue: 2, 45 - 58, 14.03.2024

Abhigayan Adhikary

Abstract

Abstract:
The aim of this paper is to perform a State wise Analysis of the First and the Second COVID-19 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.

Keywords

COVID-19, disease modeling, Gompertz Curve, Non-linear least squares, time series, Forecasting, Prediction

References

• Ghosh P., Ghosh R., Chakraborty B. (2020). COVID-19 in India: State wise Analysis and Prediction. JMIR Public Health Surveill 2020, Volume 6, Issue 3.
• Gujarati Damodar N, Porter Dawn C and Pal Manoranjan (2021), Basic Econometrics, McGraw Hill, Sixth Edition, Special Indian Edition, pp. 519=532.
• Gupta R., Kumar S. (2020).Trend Analysis and forecasting of COVID-19 outbreak in India. MedRxiv (2020).
• Jukić, D., Kralik, G., Scitovski, R.(2004). Least-squares fitting Gompertz curve. Journal of Computational and Applied Mathematics, 169(2), 359-375.
• Mazuruk J., Nenickova Z. (2020). Predicting the Number of total COVID-19 cases and deaths in USA by Gompertz Curves. Accessed: Jun, 23.
• Mendieta J.F, Cortes M, Cortes M (2020). COVID-19 Forecasts for Cuba using Logistic Regression and Gompertz Curves. MEDICC review, 22, pp.32-39.
• Mittal S. (2020). An Exploratory Data Analysis of COVID-19 in India”, International Journal of Engineering Research & Technology (IJERT), Vol. 9 Issue 04, April-2020.
• Ohnishi A., Namekawa Y., Fukui T. (2020). Universality in COVID-19 spread in view of the Gompertz Function. Prog. Theor. Exp. Phys. 2020, 123J01, Published by Oxford University Press on behalf of the Physical Society of Japan.
• Pal M and Adhikary A. (2022), Prediction of Number of Covid-19 Affected Persons by Using Gompertz Curves, Manuscript, Preprint.
• Pandey G., Chowdhury P., Gupta R. (2020). SEIR and Regression Model based COVID-19 outbreak predictions in India. arXiv preprint arXiv:2004.00958.
• Rypdal K., Rypdal M. (2020). A Parsimonious Description and cross-country Analysis of COVID-19 Epidemic Curves. International Journal of Environmental Research and Public Health, 17(18), p.6487.
• Winsor C.P (1932). The Gompertz Curve as a Growth Curve. Proceedings of the National Academy of Sciences, Volume 18, Number 1.
• The COVID-19 Dataset on India is available at: COVID-19 India API accessed on 29th January 2022. The updated dataset can be downloaded from: https://data.covid19india.org/.