Trends and analysing the correlation of population density and percentage of population suffered from COVID-19 - A linear regression model

Abstract

BACKGROUND: Since the emergence in December 2019, coronavirus disease (COVID-19) has impacted several countries and made it a worldwide pandemic. It is assumed that chances of transmission of infection of COVID-19 are increased if the population of a particular area is dense as it is a highly contagious disease and measures like social distancing could not be followed. The objectives of this study were as follows: to compare the trend of confirmed, recovered, deceased cases and recovery and death rate of COVID-19 (severe acute respiratory syndrome coronavirus 2) infection in the top 5 worst-hit states of India with National Capital Territory of Delhi and to analyze the correlation of population density with percentage of population suffered. MATERIALS AND METHODS: This descriptive population study retrieved the data published by daily health bulletins of states and Press Information Bureau, Government of India. The correlational coefficient and linear regression analysis were used to analyze the relation between population density and percentage of population suffered from COVID-19. RESULTS: Maharashtra continued to be the upmost Indian state with the highest number of confirmed, recovered, deceased cases and death rate. Further, it is estimated that population density has a negligible to low positive correlation (correlation coefficient value: 0.30) with the percentage of population suffered from COVID-19 and there is no significant relationship with P > 0.05 between the above two parameters as obtained using linear regression model. CONCLUSION: The population density does not have a strong correlation with the percentage of population suffered from COVID-19 in India.

Keywords

• covid cases
• covid-19
• percentage of population
• population density