A stochastic analysis and bibliometric analysis of COVID-19

---

Original Article

Author Details : Rakshita Chaudhary, Nisha Gaur*, Mohit Yadav, Siddharth Srivastava, Vanshika Chaudhary, Mohd Asif Shah

Volume : 11, Issue : 4, Year : 2024

Article Page : 342-353

https://doi.org/10.18231/j.ijmr.2024.058

Suggest article by email

Get Permission

---

Abstract

Background: COVID-19 a novel Corona Virus Disease which was caused by SARS-CoV-2 (Severe Acute Respiratory Syndrome Corona Virus 2) continues to pose a critical and urgent threat to global health. When an infected person comes in contact with a normal individual or when the infected person sneezes or coughs, the virus that triggers COVID-19 spreads.
Aim and Objective: The spread of novel SARS-CoV-2 was increasing, and the threats caused by it were becoming more severe in 2021. To counter the disease and save countless lives in danger, it is necessary to predict the trend of the number of cases and deaths and then implement the policies accordingly.
Materials and Methods: In this paper, the trends of the growth rate of worldwide cases and deaths were studied, and the future growth for 100 days was predicted using the Neural network model and Polynomial Regression model. For efficient planning, the countries were grouped using Principal Component Analysis and the predictions were made. The cases and deaths in different countries and states were related through the Pearson coefficient, and the heat maps were studied. Additionally, in this paper, a case study to predict the trend of cases, number of deaths and recoveries in India was also performed.
Result: The Indian states were grouped into four groups based on the Principal Component Analysis (PCA) results, and relevant remarks and trends were suggested. The growth of cases and deaths was studied, and the peaks were predicted for the next 200 days. In recent months, COVID-19 has generated a significant deal of anxiety as a global pandemic, and an increasing number of studies have been published in this area.
Conclusion: Consequently, a bibliometric examination of these papers may offer insight into current research hot subjects and trends. We are the first to join stochastic analysis of COVID-19 effects with bibliometric analysis of COVID-19. This prediction, if taken into consideration strategically during the planning of preventive measures of COVID-19 can help to reduce the cases to a great extent.
 

Keywords: Machine learning, COVID- 19, Neural network, Pearson’s correlation, SARS- CoV- 2, Principal component analysis (PCA).

How to cite : Chaudhary R, Gaur N, Yadav M, Srivastava S, Chaudhary V, Shah M A, A stochastic analysis and bibliometric analysis of COVID-19. Indian J Microbiol Res 2024;11(4):342-353

This is an Open Access (OA) journal, and articles are distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 License, which allows others to remix, tweak, and build upon the work non-commercially, as long as appropriate credit is given and the new creations are licensed under the identical terms.