Numerical Prediction for Spreading Novel Coronavirus Disease in India Using Logistic Growth and SIR Models
Sandip Saha 1 * , Pankaj Biswas 1, Sujit Nath 2
More Detail
1 Department of Mathematics, NIT Silchar, 788010, Assam, India
2 Department of Mechanical Engineering, NIT Silchar, 788010, Assam, India
* Corresponding Author

Abstract

At present, Novel COVID-19 has become the greatest issue in the world which was first detected in the city of Wuhan of Hubei province in China in the month of December 2019. SARS-COV-2 is responsible for the spreading of corona virus disease. Within a very short time period, it has spread very fast throughout the world. Beyond all the boundaries of medical science, nowadays COVID-19 has become a main interesting topic in many research fields such as Applied Mathematics, economy, politics, up to the living room. The aim of this study is to investigate the dynamic behavior of pandemic COVID-19 which based on real-time data. The logistic growth model and SIR model has been employed to study the different four characteristics of COVID-19, such as low growth state, moderate growth state, transition state, and steady-state. The models have been validated with the results of real-time data. Moreover, the model presents a rapid change due to the unavailability of precautions. Furthermore, some parameters have been implemented to predict the COVID-19 status up to 5th Jan 2021. From these models, it is predicted that the total number of infected peoples reaches 10M up to 5th Jan 2021. It has also been revealed that with the support of lockdown, social alertness, increasing testing facility, and social distancing recovery growth rate of infected persons increases with the increase of time.

License

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Article Type: Original Article

https://doi.org/10.30935/ejmets/10848

EUROPEAN J MED ED TE, 2021 - Volume 14 Issue 2, Article No: em2106

Publication date: 15 Apr 2021

Article Views: 394

Article Downloads: 329

Open Access References How to cite this article