Geospatial multivariate analysis of COVID-19: a global perspective.

This manuscript presents a geospatial and temporal analysis of the COVID'19 along with its mortality rate worldwide and an empirical evaluation of social distance policies on economic activities. Stock Market Indices, Purchasing Manager Index (PMI), and Stringency Index values are evaluated with respect to rising COVID-19 cases based on the collected data from Jan 2020 to June 2021. The findings for the stock market index reveal the highest negative correlation coefficient value, i.e., -0.2, for the Shanghai index, representing a negative relation on stock markets, whereas the value of the correlation coefficient is minimum for Indian markets, i.e., 0.3, indicating the most impact by COVID-19 spread. Further, the results concerning PMI show that the highest value of the correlation coefficient is for the China i.e., -0.52, points to the sharpest pace of contraction. This reflects the lower value of the correlation indicating that the economy is on the way of growth, which can be seen from the PMI value of the various countries. The manuscript presents a novel geospatial model by empirically evaluating the correlation coefficient of COVID-19 with stock market index, PMI, and stringency index to understand the effect of COVID-19 on the global economy.