Correlations and Hierarchical Clustering Investigation Between Weather and SARS-CoV-2

Background: Humanity today faces a global emergency. It is conceivably the greatest crisis of our generation. The coronavirus pandemic, which has many global implications, has led researchers worldwide to seek solutions to this crisis, including the search for effective treatment in the first place. Objective: This study aims to identify the factors that can have an essential effect on COVID-19 comportment. Having proper management and control of imports of COVID-19 depends on many factors that are highly dependent on a country's sanitary capacity and infrastructure technology. Nevertheless, meteorological parameters can also be a connecting factor to this disease; since temperature and humidity are compatible with a seasonal respiratory virus's behavior. Method: In this work, we analyze the correlation between weather and the COVID-19 epidemic in Casablanca, the economic capital of Morocco. It is based on the primary analysis of COVID-19 surveillance data from the Ministry of Health of the Kingdom of Morocco and weather data from the meteorological data. Weather factors include minimum temperature (°C), maximum temperature (°C), mean temperature (°C), maximum wind speed (Km/h), humidity (%), and rainfall (mm). The Spearman and Kendall rank correlation test is used for data analysis. Between the weather components. Results: The mean temperature, maximum temperature (°C) and Humidity were significantly correlated with the COVID-19 pandemic with respectively (r= -0.432, r = -0.480; r=0.402, and p=- 0.212, p= -0.160, and p= -0.240). Conclusion: This discovery helps reduce the incidence rate of COVID-19 in Morocco, considering the significant correlation between weather and COVID-19, of about more than 40%.