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Abstract
Purpose: 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. Methods: Daily confirmed cases and meteorological factors in 38 districts of India were collected between 1st April 2020 to 30th 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. Results: 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. Conclusion: 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.