Current Science

Open Access Open Access  Restricted Access Subscription Access

Characterization of the Second Wave of COVID-19 in India


Affiliations
1 Department of Aerospace Engineering, Indian Institute of Technology, Kanpur 208 016, India, India
2 Department of Physics, Indian Institute of Technology, Kanpur 208 016, India, India
 

The second wave of COVID-19, which began in India around 11 February 2021, has hit the country hard with daily cases reaching nearly triple the first peak value as on 19 April 2021. The epidemic evolution in India is complex due to regional inhomogeneities and the spread of several coronavirus mutants. In this study, we characterize the virus spread in the ongoing second wave in India and its states until 19 April 2021, and also examine the dynamic evolution of the epidemic from the beginning of the outbreak. Variations in the effective reproduction number (Rt) are taken as quantifiable measures of virus transmissibility. Rt value for every state, including those with large rural populations, is greater than the self-sustaining threshold of 1. An exponential fit on recent data also shows that the infection rate is much higher than in the first wave. Subsequently, characteristics of COVID-19 spread are analysed region-wise, by estimating test positivity rates (TPRs) and case fatality rates (CFRs). Very high TPR values for several states present an alarming situation. CFR values are lower than those in the first wave, but are recently showing signs of increase as the healthcare system is being over-stretched with the surge in infections. Preliminary estimates with a classical epidemiological model suggest that the peak for the second wave could occur around mid-May 2021, with daily count exceeding 0.4 million. The study strongly suggests that an effective administrative intervention is needed to arrest the rapid growth of the epidemic.

Keywords

Coronavirus, COVID-19, Epidemic Evolution, Reproduction Number, Second Wave.
User
Notifications
Font Size

  • Davies, N. G. et al., Estimated transmissibility and impact of SARS-CoV-2 lineage b. 1.1. 7 in England. Science, 2021, 372, 6538.

  • Volz, E. et al., Assessing transmissibility of SARS-CoV-2 lineage b. 1.1. 7 in England. Nature, 2021, 593, 266–269.

  • Chen, J., Wang, R., Wang, M. and Wei, G.-W., Mutations strengthened SARS-CoV-2 infectivity. J. Mol. Biol., 2020, 432(19), 5212–5226.

  • Leung, K., Shum, M. H., Leung, G. M., Lam, T. T. and Wu, J. T., Early transmissibility assessment of the n501y mutant strains of SARS-CoV-2 in the United Kingdom, October to November 2020. Eurosurveillance, 2021, 26(1), 2002106.

  • Borghesi, A., Golemi, S., Carapella, N., Zigliani, A., Farina, D. and Maroldi, R., Lombardy, northern Italy: COVID-19 second wave less severe and deadly than the first? A preliminary investigation. Inf. Dis., 2021, 53, 370–375..

  • Xu, S. and Li, Y., Beware of the second wave of COVID-19. The Lancet, 2020, 395(10233), 1321–1322.

  • Bagcchi, S., The world’s largest COVID-19 vaccination campaign. The Lancet. Inf. Dis., 2021, 21(3), 323.

  • OWID; https://ourworldindata.org/ (accessed on 19 April 2021).

  • COVID19INDIA; https://www.covid19india.org/ (accessed on 19 April 2021).

  • Fraser, C., Riley, S., Anderson, R. M. and Ferguson, N. M., Factors that make an infectious disease outbreak controllable. Proc. Natl. Acad. Sci., 2004, 101(16), 6146–6151.

  • Cori, A., Ferguson, N. M., Fraser, C. and Cauchemez, S., A new framework and software to estimate time-varying reproduction numbers during epidemics. Am. J. Epidemiol., 2013, 178(9), 1505–1512.

  • Wallinga, J. and Teunis, P., Different epidemic curves for severe acute respiratory syndrome reveal similar impacts of control measures. Am. J. Epidemiol., 2004, 160(6), 509–516.

  • Thompson, R. et al., Improved inference of time-varying reproduction numbers during infectious disease outbreaks. Epidemics, 2019, 29, 100356.

  • Batista, M., estimate_r, Matlab central file exchange, 2021 (accessed on 19 March 2021).

  • Du, Z., Xu, X., Wu, Y., Wang, L., Cowling, B. J. and Meyers, L. A., Serial interval of COVID-19 among publicly reported confirmed cases. Emerging Inf. Dis., 2020, 26(6), 1341.

  • Knight, J. and Mishra, S., Estimating effective reproduction number using generation time versus serial interval, with application to COVID-19 in the greater Toronto area, Canada. Inf. Dis. Modelling, 2020, 5, 889–896.

  • Rai, B., Shukla, A. and Dwivedi, L. K., Estimates of serial interval for COVID-19: A systematic review and meta-analysis. Clin. Epidemiol. Global Health, 2020, 9, 157–161.

  • Nishiura, H., Linton, N. M. and Akhmetzhanov, A. R., Serial interval of novel coronavirus (COVID-19) infections. Int. J. Inf. Dis., 2020, 93, 284–286.

  • Hethcote, H. W., The mathematics of infectious diseases. SIAM Rev., 2000, 42(4), 599–653.

  • Batista, M., Estimation of the final size of the COVID-19 epidemic. medRxiv, doi, 2020, 10, 2020.02, 16–20023606.

  • Ranjan, R., Predictions for COVID-19 outbreak in India using epidemiological models. medRxiv, 2020.

  • Grubaugh, N. D., Hanage, W. P. and Rasmussen, A. L., Making sense of mutation: what D614G means for the COVID-19 pandemic remains unclear. Cell, 2020, 182(4), 794–795.

  • Volz, E. et al., Evaluating the effects of SARS-CoV-2 spike mutation D614G on transmissibility and pathogenicity. Cell, 2021, 184(1), 64–75.

  • Chen, J. et al., Prioritizing allocation of COVID-19 vaccines based on social contacts increases vaccination effectiveness. medRxiv, 2021.

  • Anderson, R. M. and May, R. M., Infectious Diseases of Humans: Dynamics and Control, Oxford University Press, 1992.

  • Ranjan, R., Temporal dynamics of COVID-19 outbreak and future projections: A data-driven approach. Trans. Indian Natl. Acad. Eng., 2020, 5, 109–115.

  • Gopal, R., Chandrasekar, V. and Lakshmanan, M., Dynamical modelling and analysis of COVID-19 in India. Curr. Sci., 2021, 120(8), 1342–1349.

  • Agrawal, M., Kanitkar, M. and Vidyasagar, M., Modelling the spread of SARS-CoV-2 pandemic – impact of lockdowns and interventions. Indian J. Med. Res., 2021, 153(1), 175–181.

  • Ranjan, R., COVID-19 spread in India: Dynamics, modeling, and future projections. J. Indian Statist. Assoc., 2020, 58(2), 47–65.

  • Kotwal, A., Yadav, A. K., Yadav, J., Kotwal, J. and Khune, S., Predictive models of COVID-19 in India: a rapid review. Med. J. Armed Forces India, 2020, 76(4), 377–386.

  • Postnikov, E. B., Estimation of COVID-19 dynamics ‘on a backofenvelope’: does the simplest SIR model provide quantitative parameters and predictions? Chaos, Solitons & Fractals, 2020, 135, 109841.

  • Cooper, I., Mondal, A. and Antonopoulos, C. G., A SIR model assumption for the spread of COVID-19 in different communities. Chaos, Solitons & Fractals, 2020, 139, 110057.

  • Asad, A., Srivastava, S. and Verma, M. K., Evolution of COVID19 pandemic in India. Trans. Indian Natl. Acad. Eng., 2020, 5(4), 711–718.

  • Verma, M. K., Asad, A. and Chatterjee, S., COVID-19 pandemic: Power law spread and flattening of the curve. Trans. Indian Natl. Acad. Eng., 2020, 5, 103–108.

  • Sharma, A., Sapkal, S. and Verma, M. K., Universal epidemic curve for COVID-19 and its usage for forecasting, Trans. Indian Natl. Acad. Eng., 2021, 6, 405–413.

  • Shayak, B., Sharma, M. M., Rand, R. H., Singh, A. K. and Misra, A., Transmission dynamics of COVID-19 and impact on public health policy. medRxiv, 2020.


Abstract Views: 212

PDF Views: 83




  • Characterization of the Second Wave of COVID-19 in India

Abstract Views: 212  |  PDF Views: 83

Authors

Rajesh Ranjan
Department of Aerospace Engineering, Indian Institute of Technology, Kanpur 208 016, India, India
Aryan Sharma
Department of Physics, Indian Institute of Technology, Kanpur 208 016, India, India
Mahendra K. Verma
Department of Physics, Indian Institute of Technology, Kanpur 208 016, India, India

Abstract


The second wave of COVID-19, which began in India around 11 February 2021, has hit the country hard with daily cases reaching nearly triple the first peak value as on 19 April 2021. The epidemic evolution in India is complex due to regional inhomogeneities and the spread of several coronavirus mutants. In this study, we characterize the virus spread in the ongoing second wave in India and its states until 19 April 2021, and also examine the dynamic evolution of the epidemic from the beginning of the outbreak. Variations in the effective reproduction number (Rt) are taken as quantifiable measures of virus transmissibility. Rt value for every state, including those with large rural populations, is greater than the self-sustaining threshold of 1. An exponential fit on recent data also shows that the infection rate is much higher than in the first wave. Subsequently, characteristics of COVID-19 spread are analysed region-wise, by estimating test positivity rates (TPRs) and case fatality rates (CFRs). Very high TPR values for several states present an alarming situation. CFR values are lower than those in the first wave, but are recently showing signs of increase as the healthcare system is being over-stretched with the surge in infections. Preliminary estimates with a classical epidemiological model suggest that the peak for the second wave could occur around mid-May 2021, with daily count exceeding 0.4 million. The study strongly suggests that an effective administrative intervention is needed to arrest the rapid growth of the epidemic.

Keywords


Coronavirus, COVID-19, Epidemic Evolution, Reproduction Number, Second Wave.

References





DOI: https://doi.org/10.18520/cs%2Fv121%2Fi1%2F85-93