Author: Yassin, Mohamed F.; Aldashti, Hassan A.
Title: Stochastic analysis of the relationship between atmospheric variables and coronavirus disease (COVIDâ€19) in a hot, arid climate Cord-id: ymuqe5eg Document date: 2021_8_18
ID: ymuqe5eg
Snippet: The rapid outbreak of the coronavirus disease (COVIDâ€19) has affected millions of people all over the world and killed hundreds of thousands. Atmospheric conditions can play a fundamental role in the transmission of a virus. The relationship between several atmospheric variables and the transmission of the severe acute respiratory syndrome coronavirus 2 (SARSâ€CoVâ€2) are therefore investigated in this study, in which the State of Kuwait, which has a hot, arid climate, is considered during f
Document: The rapid outbreak of the coronavirus disease (COVIDâ€19) has affected millions of people all over the world and killed hundreds of thousands. Atmospheric conditions can play a fundamental role in the transmission of a virus. The relationship between several atmospheric variables and the transmission of the severe acute respiratory syndrome coronavirus 2 (SARSâ€CoVâ€2) are therefore investigated in this study, in which the State of Kuwait, which has a hot, arid climate, is considered during free movement (without restriction), partial lockdown (partial restrictions), and full lockdown (full restriction). The relationship between the infection rate, growth rate, and doubling time for SARSâ€CoVâ€2 and atmospheric variables are also investigated in this study. Daily data describing the number of COVIDâ€19 cases and atmospheric variables, such as temperature, relative humidity, wind speed, visibility, and solar radiation, were collected for the period February 24 to May 30, 2020. Stochastic models were employed to analyze how atmospheric variables can affect the transmission of SARSâ€CoVâ€2. The normal and lognormal probability and cumulative density functions (PDF and CDF) were applied to analyze the relationship between atmospheric variables and COVIDâ€19 cases. The Spearman's rank correlation test and multiple regression model were used to investigate the correlation of the studied variables with the transmission of SARSâ€CoVâ€2 and to confirm the findings obtained from the stochastic models. The results indicate that relative humidity had a significant negative correlation with the number of COVIDâ€19 cases, whereas positive correlations were observed for cases of infection and temperature, wind speed, and visibility. The infection rate for SARSâ€CoVâ€2 is directly proportional to the air temperature, wind speed, and visibility, whereas inversely related to the humidity. The lowest growth rate and longest doubling time of the COVIDâ€19 infection occurred during the full lockdown period. The results in this study may help the World Health Organization (WHO) make specific recommendations about the outbreak of COVIDâ€19 for decisionâ€makers around the world. Integr Environ Assess Manag 2021;00:1–17. © 2021 SETAC
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