Mathematical Modelling of Transmission of Covid-19 in Indian Context: An Impact of Lockdown

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Authors

  • Priyanka Mandal
     Department of Mathematics, School of Science and Technology, The Neotia University, Kolkata, ,IN
  • Ayan Chatterjee
     Department of Mathematics, School of Science and Technology, The Neotia University, Kolkata, ,IN

DOI:

https://doi.org/10.18311/jmmf/2023/34169

Keywords:

COVID-19, Disease Spreading, Lockdown, Mathematical Modelling.

Abstract

Mathematical Modelling study has been carried out to model the out spread of Covid-19 in a localized society of India. Based on the transmission mechanism of Covid-19 authors established dynamics model of six chambers with population birth rate and mortality. Ordinary differential equations are used to model the system mathematically and solved numerically using ODE23 scheme. India imposes lockdown in very early stage of the Covid-19 transmission and impact of lockdown has been discussed through the mathematical model. Parameters are considered in view of the March scenario in India and the impact we got is quite similar with the present situation.

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Published

2023-07-04

How to Cite

Mandal, P., & Chatterjee, A. (2023). Mathematical Modelling of Transmission of Covid-19 in Indian Context: An Impact of Lockdown. Journal of Mines, Metals and Fuels, 71(5), 678–682. https://doi.org/10.18311/jmmf/2023/34169

Issue

Volume 71, Issue 5, May 2023

Section

Articles

License

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

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References

Berge, T., Lubuma, J. S., Moremedi, G. M., Morris, N., & Kondera-Shava, R. (2017). A simple mathematical model for Ebola in Africa. Journal of biological dynamics, 11(1), 42-74. DOI: https://doi.org/10.1080/17513758.2016.1229817

Cao, J., Jiang, X., & Zhao, B. (2020). Mathematical Modelling and Epidemic Prediction of COVID-19 and its Significance to Epidemic Prevention and Control Measures. J BioMed Res Innov, 1(1), 103.

Chowell, G., Hengartner, N. W., Castillo-Chavez, C., Fenimore, P. W., & Hyman, J. M. (2004). The basic reproductive number of Ebola and the effects of public health measures: the cases of Congo and Uganda. Journal of theoretical biology, 229(1), 119-126. DOI: https://doi.org/10.1016/j.jtbi.2004.03.006

Fisman, D., Khoo, E., & Tuite, A. (2014). Early epidemic dynamics of the West African 2014 Ebola outbreak: estimates derived with a simple two-parameter model. PLoS currents, 6. DOI: https://doi.org/10.1371/currents.outbreaks.89c0d3783f36958d96ebbae97348d571

Wang, X. S., & Zhong, L. (2015). Ebola outbreak in West Africa: real-time estimation and multiple-wave prediction. arXivpreprint arXiv:1503.06908. DOI: https://doi.org/10.3934/mbe.2015.12.1055