tetano
Editor, Senior Moderator
Integr Environ Assess Manag
. 2021 Jun 22.
doi: 10.1002/ieam.4481. Online ahead of print.
Stochastic Analysis of the Relationship between Atmospheric Variables and Coronavirus Disease (COVID-19) in a Hot, Arid Climate
Mohamed F Yassin[SUP] 1 [/SUP], Hassan A Aldashti[SUP] 2 [/SUP]
Affiliations
Abstract
The rapid outbreak of the coronavirus disease (COVID-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 (SARS-CoV-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 SARS-CoV-2 and atmospheric variables are also investigated in this study. Daily data describing the number of COVID-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 SARS-CoV-2. The normal and lognormal probability and cumulative density functions were applied to analyze the relationship between atmospheric variables and COVID-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 SARS-CoV-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 COVID-19 cases, while positive correlations were observed for cases of infection and temperature, wind speed, and visibility. The infection rate for SARS-CoV-2 is directly proportional to the air temperature, wind speed, and visibility, while inversely related to the humidity. The lowest growth rate and highest doubling time of the COVID-19 infection showed in the full lockdown period. The results in this study may help the World Health Organization (WHO) to make specific recommendations about the outbreak of COVID-19 for decision-makers around the world. This article is protected by copyright. All rights reserved.
Keywords: Atmospheric variables; Correlation; Regression; SARS-CoV-2 infection; Stochastic models.
. 2021 Jun 22.
doi: 10.1002/ieam.4481. Online ahead of print.
Stochastic Analysis of the Relationship between Atmospheric Variables and Coronavirus Disease (COVID-19) in a Hot, Arid Climate
Mohamed F Yassin[SUP] 1 [/SUP], Hassan A Aldashti[SUP] 2 [/SUP]
Affiliations
- PMID: 34156152
- DOI: 10.1002/ieam.4481
Abstract
The rapid outbreak of the coronavirus disease (COVID-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 (SARS-CoV-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 SARS-CoV-2 and atmospheric variables are also investigated in this study. Daily data describing the number of COVID-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 SARS-CoV-2. The normal and lognormal probability and cumulative density functions were applied to analyze the relationship between atmospheric variables and COVID-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 SARS-CoV-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 COVID-19 cases, while positive correlations were observed for cases of infection and temperature, wind speed, and visibility. The infection rate for SARS-CoV-2 is directly proportional to the air temperature, wind speed, and visibility, while inversely related to the humidity. The lowest growth rate and highest doubling time of the COVID-19 infection showed in the full lockdown period. The results in this study may help the World Health Organization (WHO) to make specific recommendations about the outbreak of COVID-19 for decision-makers around the world. This article is protected by copyright. All rights reserved.
Keywords: Atmospheric variables; Correlation; Regression; SARS-CoV-2 infection; Stochastic models.