Progression of COVID-19 Pandemic in India: A Linear Functional Concurrent Regression Analysis Approach

  • Aalok Ranjan Chaurasia President, MLC Foundation, Bhopal, Madhya Pradesh, India.
  • Brijesh P Singh Professor, Department of Statistics, Institute of Science, Banaras Hindu University, Varanasi, Uttar Pradesh, India.
  • Ravendra Singh Additional Director General (Retired), Central Statistics Office, Ministry of Statistics and Programme Implementation, Government of India, New Delhi, India.
Keywords: COVID-19, India, Functional Concurrent Regression Analysis, Estimation

Abstract

Background: COVID-19 is a disasterous pandemic that the world has ever faced. It is affecting the global health system irrespective of race, ethnicity, environment, and economic status. This study is conducted with the aim of assessing the progression of the COVID-19 pandemic in India.

Methods: This article uses the functional concurrent regression analysis approach to describe the pattern of daily reported confirmed cases of COVID-19 in India. The approach provides an excellent fit to the daily reported confirmed cases of the disease. The data used in this study have been taken from covid19india.org.

Results: Estimated value of the parameter Bk of the model is highly volatile. During the first phase of the pandemic which last up to 31st March 2020, value was very high. During 31st March to 19th July 2020 except for a few exceptions. Its value again increased rapidly from 17th February 2021 to 16th April 2021 and started decreasing after mid-March, 2021 and continued decreasing till present.

Conclusion: The data-driven approach used in this study is purely empirical and does not make any assumption about the progression of the pandemic or about the data. The article suggests that based on the parameter of the model, an early warning system may be developed and institutionalised to undertake the necessary measures to control the spread of the disease, thereby controlling the pandemic.

How to cite this article:
Chaurasia AR, Singh BP, Singh R. Progression of COVID-19 Pandemic in India: A Linear Functional Concurrent Regression Analysis Approach. J Commun Dis. 2021;53(4):15-22.

DOI: https://doi.org/10.24321/0019.5138.202169

References

Kumar P, Erturk VS. A case study of Covid-19 epidemic in India via generalised Caputo type fractional derivatives. Math Methods Appl Sci. 2021 Feb;10.1002/mma.7284. [PubMed] [Google Scholar]

Banerjee R, Bhattacharjee S, Varadwaj PK. Analyses and forecasts for COVID-19 in India. MedRxiv.2020. [Google Scholar]

Biswas SK, Ghosh JK, Sarkar S, Ghosh U. COVID-19 pandemic in India: a mathematical model study. NonlinearDyn.2020 Sep;102:537-53. [PubMed] [Google Scholar]

Smith D, Moore L [Internet]. The SIR model for spread of disease: The differential equation model. Convergence; 2004. Available from: https://www.maa.org/press/periodicals/loci/joma/the-sir-model-for-spread-of-disease-the-differential-equation-model. [Google Scholar]

Allen LJ, Brauer F, Van den Driessche P, Wu J. Mathematical epidemiology. Vol. 1945. Springer;2008. [Google Scholar]

Kermack WO, McKendrick AG. A contribution to the mathematical theory of epidemics. Proc Roy Soc Lond. 1927;115:700-21. [Google Scholar]

Fraser C, Donnelly CA, Cauchemez S, Hanage WP, Van Kerkhove MD, Hollingsworth TD. Pandemic potential of a strain of influenza A (H1N1): early findings. Science. 2009;324(5934):1557-61. [Google Scholar]

Milligan GN, Barrett AD. Vaccinology: an essential guide. John Wiley & Sons;2014. [Google Scholar]

Chen YC, Lu PE, Chang CS, Liu TH. A time-dependent SIR model for COVID-19 with undetectable infected persons. IEEE Trans Net Sci Eng. 2020;7(4):3279-94. [Google Scholar]

Wu JT, Leung K, Leung GM. Now casting and forecasting the potential domestic and international spread of the 2019-nCoV outbreak originating in Wuhan, China: a modelling study. Lancet. 2020 Feb;395(10225):689-97. [PubMed] [Google Scholar]

Batista M. Estimation of the final size of the second phase of the Corona virus COVID 19 epidemic by the logistic model. MedRxiv.2020. [Google Scholar]

Koo JR, Cook AR, Park M, Sun Y, Sun H, Lim JT, Tam C, Dickens BL.Interventions to mitigate early spread of SARS-CoV-2 in Singapore: a modelling study. Lancet Infect Dis. 2020 Jun;20:678-88. [PubMed] [Google Scholar]

Kucharski AJ, Russell TW, Diamond C, Liu Y, Edmunds J, Funk S, Eggo RM; Centre for Mathematical Modelling of Infectious Diseases COVID-19 working group.Early dynamics of transmission and control of COVID-19: a mathematical modelling study. Lancet Infect Dis. 2020;20(5):553-8. [PubMed] [Google Scholar]

Tuite AR, Fisman DN. Reporting, epidemic growth, and reproduction numbers for the 2019 novel Corona virus (2019-nCoV) epidemic. Ann Intern Med. 2020 Apr;172(8):567-68. [PubMed] [Google Scholar]

Zhao S, Lin Q, Ran J, Musa SS, Yang G, Wang W, Lou Y, Gao D, Yang L, He D, Wang MH.Preliminary estimation of the basic reproduction number of novel Corona virus (2019-nCoV) in China, from 2019 to 2020: a data-driven analysis in the early phase of the outbreak. Int J Infect Dis. 2020 Mar;92:214-7. [PubMed] [Google Scholar]

Roy TK, Singh BP. A data driven model for prediction of COVID-19 outbreak in Bangladesh. Bangladesh J Infect Dis.2020;7(1):22-6. [Google Scholar]

Shen CY. A logistic growth model for COVID-19 proliferation: experiences from China and international implications in infectious diseases. IntJ Infect Dis.2020;96:582-9.

Singh R, Adhikari R. Age-structured impact of social distancing on the COVID-19 epidemic in India. arXiv. 2020;12055. [Google Scholar]

Rao Srinivasa ASR, Krantz Steven G, Thomas K, Bhat R. Model-based retrospective estimates for COVID-19 or Corona virus in India: continued efforts required to contain the virus spread. Curr Sci. 2020;118(7):1023-25. [Google Scholar]

Chang SL, Harding N, Zachreson C, Cliff OM, Prokopenko M. Modelling transmission and control of the COVID-19 pandemic in Australia. Nat Commun.2020 Nov;11(1):1-13. [PubMed] [Google Scholar]

Chaurasia AR, Singh BP. COVID-19 Trend and forecast in India: ajoin point regression analysis. Demog India. 2020;49(Special Issue):15-26. [Google Scholar]

Singh BP. Modelling and forecasting the spread of COVID-19 pandemic in India and significance of lockdown: A mathematical outlook. Handbook of Statistics. 2021:257-89. [Google Scholar]

Singh BP, Singh G. Modelling tempo of COVID-19 pandemic in India and significance of lockdown. J Public Aff. 2020 Aug;20(4):e2257.[PubMed] [Google Scholar]

Huang C, Wang Y, Li X, Ren L, Zhao J, Hu Y, Zhang L, Fan G, Xu J, Gu X, Cheng Z, Yu T, Xia J, Wei Y, Wu W, Xie X, Yin W, Li H, Liu M, Xiao Y, Gao H, Guo L, Xie J, Wang G, Jiang R, Gao Z, Jin Q, Wang J, Cao B.Clinical features of patients infected with 2019 novel corona virus in Wuhan, China. Lancet. 2020 Feb;395(10223):497-506. [PubMed] [Google Scholar]

Hui DS, I Azhar E, Madani TA, Ntoumi F, Kock R, Dar O, Ippolito G, Mchugh TD, Memish ZA, Drosten C, Zumla A, Petersen E. The continuing 2019-ncov epidemic threat of novel Corona viruses to global health-the latest 2019 novel corona virus outbreak in Wuhan, China. IntJ Infect Dis. 2020 Feb;91:264. [PubMed] [Google Scholar]

Corman VM, Landt O, Kaiser M, Molenkamp R, Meijer A, Chu DK, Bleicker T, Brünink S, Schneider J, Schmidt ML, Mulders DG, Haagmans BL, van der Veer B, van den Brink S, Wijsman L, Goderski G, Romette JL, Ellis J, Zambon M, Peiris M, Goossens H, Reusken C, Koopmans MP, Drosten C.Detection of 2019 novel Corona virus (2019-ncov) by real time RT-PCR. Eurosurveillance. 2020 Jan;25(3):2000045. [PubMed] [Google Scholar]

Rothe C, Schunk M, Sothmann P, Bretzel G, Froeschl G, Wallrauch C, Zimmer T, Thiel V, Janke C, Guggemos W, Seilmaier M, Drosten C, Vollmar P, Zwirglmaier K, Zange S, Wölfel R, Hoelscher M.Transmission of 2019-ncov infection from an asymptomatic contact in Germany. N Engl J Med. 2020;382(10):970-1. [PubMed] [Google Scholar]

Anastassopoulou C, Russo L, Tsakris A, Siettos C. Data-based analysis, modelling and forecasting of the COVID-19 outbreak. PLoS ONE.2020 Mar;15(3):e0230405. [PubMed] [Google Scholar]

Gamero J, Tamayo JA,Martinez-Roman JA. Forecast of the evolution of the contagious disease caused by novel corona virus (2019-ncov) in China. arXiv.2020;2002:04739. [Google Scholar]

Junling M, Dushoff J, Bolker BM, Earn DJ. Estimating initial epidemic growth rates. Bull Math Biol. 2014 Jan;76(1):245-60. [PubMed] [Google Scholar]

Chatterjee K, ChatterjeeK, Kumar A, Shankar S. Healthcare impact of COVID-19 epidemic in India: astochastic mathematical model. Med J Armed Forces India. 2020 Apr;76(2):147-55. [PubMed] [Google Scholar]

Batista M. Estimation of the final size of the coronavirus epidemic by the SIR model. University of Ljubljana, Slovenia;2020. [Google Scholar]

Sharma VK, Nigam U. Modelling and forecasting of COVID-19 growth curve in India. Trans Indian Natl Acad Eng. 2020;5:697-710. [Google Scholar]

Swain PK, Tripathy MR, Jena D, Fenta HM, Zike DT. Modelling and forecasting of COVID-19 cases in Odisha and India. Demog India. 2020;49(Special Issue):66-75. [Google Scholar]

Newtonraj A, Mani M. Autoregressive integrated moving average model for forecasting COVID-19 in India. J Postgrad Med. 2020;54(3):122-5. [Google Scholar]

Mishra P, Al Khatib AMG, Sardar I, Mohammed J, Ray M, Kumar M, Rawat D, Pandey SA, Dubey A, Feys J, Rono K. Modelling and forecasting of COVID-19 in India. J InfectDis Epi. 2020;6:162. [Google Scholar]

Bedi P, Dhiman S, Gole P, Gupta N, Jindal V. Prediction of COVID-19 trend in India and its four worst-affected states using modified SEIRD and LSTM models. SN Comput Sci.2021;2:224. [PubMed] [Google Scholar]

World Health Organization[Internet]. Report of the WHO-China Joint Mission on Corona virus Disease 2019 (COVID-19) 16-24 February 2020. Geneva: World Health Organization; 2020. Available from: https://www.who.int/docs/default-source/Corona viruse/who-china-joint-mission-on-covid-19-finalreport.pdf

World Health Organization [Internet]. Criteria for releasing COVID-19 patients from isolation. Geneva: World Health Organization; 2020. Available from: https://www.who.int/news-room/commentaries/detail/criteria-for-releasing-covid-19-patients-from-isolation.

COVID-19 India [Internet]; 2021.Available from: https://www.covid19india.org

Fan J, Zhang W. Statistical estimation in varying coefficient models. Ann Statist. 1999;27:1491-518. [Google Scholar]

Fan J, Zhang W. Statistical methods with varying coefficient models. Stat Interface. 2008;1(1):179-95. [PubMed] [Google Scholar]

Şentürk D, Müller HG. Functional varying coefficient models for longitudinal data. J Am Stat Assoc.2010;105(491):1256-64. [Google Scholar]

Eubank RL, Huang C, Mu˜noz Maldonado Y, Wang N, Wang S, Buchanan RJ. Smoothing spline estimation in varying-coefficient models. JR Stat Soc Series B Stat Methodol.2004;66:653-67. [Google Scholar]

Huang JZ, Wu CO, Zhou L. Varying-coefficient models and basis function approximations for the analysis of repeated measurements. Biometrika. 2002;89:111-28. [Google Scholar]

Ramsay JO, Silverman BW. Applied functional data analysis: methods and case studies. New York: Springer;2007. [Google Scholar]

Published
2021-12-31