ABSTRACT
The world has been stand-still by COVID-19, a respiratory syndrome. Its variable nature of outspread motivated us to study the impact of weather parameters. We emphasized our study on Delhi region. Applied the Pearson's correlation test and obtained the relation between weather conditions and COVID-19 cases then analyzed it for 5 incubation periods. We determined spread, growth, recovery, and transmission rates using popular regression-based machine learning (ML) techniques. The correlation resulted in achieving the best incubation period of 12 days in the city. We found that wind speed and precipitation levels were insignificant, whereas the notable association between Minimum temperature (Min. Temp) and Infra-Red (IR) radiative flux was determined. On stacking the regression-based models, we achieved the best accuracy for predicting the outcome of the pandemic. Our results proposed that certain prime environmental conditions are favourable for the growth of the virus and increase the chances of getting infected. The brief study is beneficial for concerned authorities to adopt adequate measures to flatten the curve. © 2021 IEEE.