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Dynamical Modelling and Analysis of COVID-19 in India


Affiliations
1 Centre for Nonlinear Science and Engineering, School of Electrical and Electronics Engineering, SASTRA Deemed University, Thanjavur 613 401, India
2 Department of Nonlinear Dynamics, School of Physics, Bharathidasan University, Tiruchirappalli 620 014, India
 

We consider the pandemic spreading of COVID-19 in India after the outbreak of the coronavirus in Wuhan city, China. We estimate the transmission rate of the initial infecting individuals of COVID-19 in India using officially reported data at the early stage of the epidemic with the help of the susceptible (S), exposed (E), infected (I), and removed (R) population model, the so-called SEIR dynamical model. Numerical analysis and model verification are performed to calibrate the system parameters with official public information about the number of people infected, and then to evaluate several COVID-19 scenarios potentially applicable to India. Our findings provide an estimation of the number of infected individuals in the pandemic period of timeline, and also demonstrate the importance of governmental and individual efforts to control the effects and time of the pandemic-related critical situations. We also give special emphasis to individual reactions in the containment process.

Keywords

Containment Process, COVID-19 Pandemic, Dynamical Modelling, Numerical Analysis.
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  • Cohen, J. and Normile, D., New SARS like virus in China triggers alarm, Science, 2020, 367, 6475.

  • WHO, 2020; https://www.who.int/emergencies/diseases/novelcoronavirus2019/technical-guid (accessed on 16 May and 5 November 2020).

  • Lin, Q. et al., A conceptual model for the coronavirus disease 2019 (COVID-19) outbreak in Wuhan, China with individual reaction and governmental action. Int. J. Infect. Dis., 2020, 93, 211– 216.

  • Li, Q. et al., Early transmission dynamics in Wuhan, China, of novel coronavirus-infected pneumonia. New Engl. J. Med., 2020, 382, 1199; doi:10.1056/NEJMoa2001316.

  • Ferguson, N. M. et al., Impact of non-pharmaceutical interventions (npis) to reduce COVID-19 mortality and healthcare demand. Imperial College COVID-19 Response Team, London, UK, 2020; doi:10.25561/77482.

  • https://www.worldometers.info/coronavirus/country; https://www.covid19india.org (accessed on 16 May and 5 November 2020).

  • Rothan, H. and Byrareddy, S., The epidemiology and pathogenesis of coronavirus disease (COVID-19) outbreak. J. Autoimmun., 2020, 109, 102433; doi:10.1016/j.jaut.2020.102433.

  • Kermack, W. O. and McKendrick, A. G., Contributions to the mathematical theory of epidemics. Bull. Math. Biol., 1991, 53, 33.

  • arXiv.org, https://arxiv.org; bioRxiv.org, https://www.biorxiv.org.

  • Rihan, F. A., Al-Salti, N. S. and Anwar, M. N. Y., Dynamics of coronavirus infection in human, AIP Conf. Proc., 2018, 1982, 020009; doi:10.1063/1.5045415.

  • Chen, T. M., Rui, J., Wang, W. P., Zhao, Z. Y., Cui, J. A. and Yin, L., A mathematical model for simulating the phase-based transmissibility of a novel coronavirus. Infect. Dis. Poverty, 2020, 9, 24; doi:10.1186/s40249-020-00640-3.

  • Savi, P. V., Savi, M. A. and Borges, B., A mathematical description of the dynamics of coronavirus disease (2019): (COVID-19): a case study of Brazil, 2020; arXiv:2004.03495v2.

  • He, D., Dushoff, J., Day, T., Ma, J. and Earn, D. J. D., Inferring the causes of the three waves of the 1918 influenza pandemic in England and Wales. Proc. R. Soc. London Ser. B, 2013, 280, 20131345; doi:10.1098/rspb.2013.1345.

  • He, D., Ionides E. L. and King, A. A., Plug-and-play inference for disease dynamics: measles in large and small populations as a case study. J. R. Soc. Interface, 2010, 7, 271–283; doi:10.1098/rsif.2009.0151.

  • Das, S., Prediction of COVID-19 disease progression in India. 2020; arXiv:2004.031471v1.

  • https://www.statisticstimes.com/demographics/population-of-india.php (accessed on 16 May 2020).

  • https://www.mygov.in/covid-19 (accessed on 16 May and 5 November 2020).

  • Chae, S. Y. et al., Estimation of infection rate and prediction of initial infected individuals of COVID-19. 2020; arxiv.org/pdf/2004.12665.

  • Lakshmanan, M. and Rajasekar, S., Nonlinear dynamics: Integrability Chaos and Patterns, Springer-Verlag, Berlin, Germany, 2003.

  • Liu, Y., Gayle, A. A., Smith, A. W. and Rocklow, J., The reproductive number of COVID-19 is higher compared to SARS coronovirus. J. Travel Med., 2020, 27(2), 1–4; doi:10.1093/jtm/taaa021.

  • MoHFW, Corona virus disease 2019 (COVID-19). Ministry of Health and Family Welfare, Government of India, 2020; https://www.mohfw.gov.in


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  • Dynamical Modelling and Analysis of COVID-19 in India

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Authors

R. Gopal
Centre for Nonlinear Science and Engineering, School of Electrical and Electronics Engineering, SASTRA Deemed University, Thanjavur 613 401, India
V. K. Chandrasekar
Centre for Nonlinear Science and Engineering, School of Electrical and Electronics Engineering, SASTRA Deemed University, Thanjavur 613 401, India
M. Lakshmanan
Department of Nonlinear Dynamics, School of Physics, Bharathidasan University, Tiruchirappalli 620 014, India

Abstract


We consider the pandemic spreading of COVID-19 in India after the outbreak of the coronavirus in Wuhan city, China. We estimate the transmission rate of the initial infecting individuals of COVID-19 in India using officially reported data at the early stage of the epidemic with the help of the susceptible (S), exposed (E), infected (I), and removed (R) population model, the so-called SEIR dynamical model. Numerical analysis and model verification are performed to calibrate the system parameters with official public information about the number of people infected, and then to evaluate several COVID-19 scenarios potentially applicable to India. Our findings provide an estimation of the number of infected individuals in the pandemic period of timeline, and also demonstrate the importance of governmental and individual efforts to control the effects and time of the pandemic-related critical situations. We also give special emphasis to individual reactions in the containment process.

Keywords


Containment Process, COVID-19 Pandemic, Dynamical Modelling, Numerical Analysis.

References





DOI: https://doi.org/10.18520/cs%2Fv120%2Fi8%2F1342-1349