Abstract
This study investigates the influence of climate variables (pressure, relative humidity, temperature and wind speed) in inducing risk due to COVID 19 at rural, urban and total (rural and urban) population scale in 623 pandemic affected districts of India incorporating the socioeconomic vulnerability factors. We employed nonstationary extreme value analysis to model the different quantiles of cumulative COVID 19 cases in the districts by using climatic factors as covariates. Wind speed was the most dominating climatic factor followed by relative humidity, pressure, and temperature in the evolution of the cases. The results reveal that stationarity, i.e., the COVID 19 cases which are independent of pressure, relative humidity, temperature and wind speed, existed only in 148 (23.7%) out of 623 districts. Whereas, strong nonstationarity, i.e., climate dependence, was detected in the cases of 474 (76.08%) districts. 334 (53.6%), 200 (32.1%) and 336 (53.9%) districts out of 623 districts were at high risk (or above) at rural, urban and total population scales respectively. 19 out of 35 states were observed to be under high (or above) Kerala, Maharashtra, Goa and Delhi being the most risked ones. The study provides high-risk maps of COVID 19 pandemic at the district level and is aimed at supporting the decision-makers to identify climatic and socioeconomic factors in augmenting the risks.