Open AccessProgression of COVID-19 Pandemic in India: A Linear Functional Concurrent Regression Analysis Approach
Author(s) -
Brijesh Singh,
AUTHOR_ID
Publication year - 2021
Publication title -
journal of communicable diseases/journal of communicable diseases
Language(s) - English
Resource type - Journals
eISSN - 2581-351X
pISSN - 0019-5138
DOI - 10.24321/0019.5138.202169
Subject(s) - pandemic , covid-19 , regression analysis , regression , value (mathematics) , demography , medicine , disease , ethnic group , geography , statistics , virology , infectious disease (medical specialty) , political science , outbreak , pathology , sociology , mathematics , law
Background: COVID-19 is a disasterous pandemic that the world has ever faced. It is affecting the global health system irrespective of race, ethnicity, environment, and economic status. This study is conducted with the aim of assessing the progression of the COVID-19 pandemic in India. Methods: This article uses the functional concurrent regression analysis approach to describe the pattern of daily reported confirmed cases of COVID-19 in India. The approach provides an excellent fit to the daily reported confirmed cases of the disease. The data used in this study have been taken from covid19india.org. Results: Estimated value of the parameter kbof the model is highly volatile. During the first phase of the pandemic which last up to 31st March 2020, value was very high. During 31st March to 19th July 2020 except for a few exceptions. Its value again increased rapidly from 17th February 2021 to 16th April 2021 and started decreasing after mid-March, 2021 and continued decreasing till present. Conclusion: The data-driven approach used in this study is purely empirical and does not make any assumption about the progression of the pandemic or about the data. The article suggests that based on the parameter of the model, an early warning system may be developed and institutionalised to undertake the necessary measures to control the spread of the disease, thereby controlling the pandemic.