Modelling and Forecasting COVID-19 Transmission Dynamics: A Susceptible-Infected- Recovered (SIR)-based Approach for Informed Decision-Making
Abstract
Background: The global pandemic scenario of COVID-19 disease cases has made it extremely difficult for emerging scientific research to anticipate outbreaks. To accurately anticipate the predictions, more
and more epidemiological mathematical models of spread are being developed daily. The traditional Susceptible-Infected-Recovered (SIR) modelling methodology was used in this research to predict the
outcomes of future pandemics and the importance of interventions to end the pandemic.
Methods: A traditional SIR model was applied to forecast the time trends of upcoming pandemics in India. The data were obtained from the Indian Council of Medical Research (ICMR) from March 2020 to June
2022, for a total period of 801 days. The SIR model was constructed using the Python functioning and parameters were estimated.
Results: The SIR model for COVID-19 confirmed that preventive strategies would change the basic reproductive number, in a way that the pandemic can be contained with less morbidity and mortality. The R0 value of SARS-CoV-2 varies in the presence of interventions and in their absence from 2.89 to 1.92.
Conclusion: Using the SIR model for infectious diseases, it was understood that the natural progression of the disease can become quite dangerous without government interventions, and so policy changes need to be
implemented quickly to flatten the epidemic curve.
How to cite this article:
Danasekaran R, Yenuganti VV. Modelling and Forecasting COVID-19 Transmission Dynamics:
A Susceptible-Infected-Recovered (SIR)-based Approach or Informed Decision-Making. J Commun Dis. 2023;55(3):57-61.
DOI: https://doi.org/10.24321/0019.5138.202338
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