Impact of environmental temperature and relative humidity on spread of COVID-19 infection in India: a cross-sectional time-series analysis.

Coronavirus disease 2019 (COVID-19) has become a serious public health problem worldwide. This study sought to examine the associations of daily average temperature (AT) and relative humidity (ARH) with the percent increase in COVID-19 cases. Daily confirmed cases and meteorological factors in 38 districts of India were collected between 1 April 2020 to 30 April 2020. Taking a 5-day time lag of average values of the variables and multiple days-samples, we ran multiple models and performed appropriate hypothesis tests to decide the single preferred model for each sample data. Suitable fixed effects (FE) and random effects (RE) models with cluster-robust standard errors were applied to quantify the district-specific associations of meteorological and other variables with COVID-19 cases. All FE models revealed that every one-degree rise in AT led to a decrease in 3.909 points (on average) in percent increase in COVID-19 cases. All RE models showed that with one unit increase in the malaria annual parasite index, there was a significant increase in 10.835 points (on average) in percent increase in COVID-19 cases. In both FE and RE models, ARH was found to be negatively associated with a percent increase in COVID-19 cases, although in half of these models the association was statistically insignificant. Our results indicate that mean temperature, mean relative humidity, and malaria endemicity might have an essential role in the stability and transmissibility of the 2019 novel coronavirus.