key: cord-289272-bhq4t850
authors: Rosario, Denes K.A.; Mutz, Yhan S.; Bernardes, Patricia C.; Conte-Junior, Carlos A.
title: Relationship between COVID-19 and weather: Case study in a tropical country
date: 2020-06-19
journal: Int J Hyg Environ Health
DOI: 10.1016/j.ijheh.2020.113587
sha: 
doc_id: 289272
cord_uid: bhq4t850

This study aimed to evaluate the relationship between weather factors (temperature, humidity, solar radiation, wind speed, and rainfall) and COVID-19 infection in the State of Rio de Janeiro, Brazil. Solar radiation showed a strong (-0.609, p < 0.01) negative correlation with the incidence of novel coronavirus (SARS-CoV-2). Temperature (maximum and average) and wind speed showed negative correlation (p < 0.01). Therefore, in this studied tropical state, high solar radiation can be indicated as the main climatic factor that suppress the spread of COVID-19. High temperatures, and wind speed also are potential factors. Therefore, the findings of this study show the ability to improve the organizational system of strategies to combat the pandemic in the State of Rio de Janeiro, Brazil, and other tropical countries around the word.

The meteorological data were obtained from six monitoring centers of the National Shapiro Wilk's test was applied to evaluate the normality of the data. The data on daily 88 cases of the COVID-19 showed non-normal distribution, so the relationship between weather 89 and COVID-19 incidence was studied using the Spearman rank correlation test. A descriptive 90 study of the data was performed using the median and quartiles represented by a box-plot graph.

The arithmetic mean was used to obtain a single and representative data set for the six The correlation coefficients are shown in Table 2 . Between nine climatic factors studied, 117 five were significant (temperature maximum, minimum and average, radiation and wind speed).

In addition, all significative variables show a negative correlation with the number of cases. However, it was not significant (p > 0.05). In the present study, the relationship between these 132 two variables was inverse and significative (p < 0.01). A non-linear correlation tool (Spearman 133 rank correlation) was used to analyze the data from Rio de Janeiro. Therefore, although Iran (an According IBGE (2020) and SHGRJ (2020). 

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