tetano
Editor, Senior Moderator
Life Sci
. 2021 Jan 18;119093.
doi: 10.1016/j.lfs.2021.119093. Online ahead of print.
Statistical analysis of COVID-19 infection caused by environmental factors: Evidence from Pakistan
Muhammad Fawad[SUP] 1 [/SUP], Sumaira Mubarik[SUP] 2 [/SUP], Saima Shakil Malik[SUP] 3 [/SUP], Jingli Ren[SUP] 4 [/SUP]
Affiliations
Abstract
Background: Coronavirus disease 2019 (COVID-19) has become a severe public health problem around the globe. Various epidemiological, statistical, and laboratory-based studies have shown that the role of temperature and other environmental factors has important influence in the transmission of coronaviruses. Scientific research is needed to answer the questions about the spread and transmission of the infection, whether people could be avoided from being infected with COVID-19 in next summer.
Aim: We aim to investigate the association of daily average temperature, daily average dew point, daily average humidity, daily average wind speed, and daily average pressure with the infection caused by this novel coronavirus in Pakistan.
Key findings: First, we check the correlation between environmental factors and daily infected cases of COVID-19; among them, temperature and dew point have positive linear relationship with daily infected cases of COVID-19. The thought-provoking findings of the present study suggested that higher temperature and dew point can contribute to a rise in COVID-19 disease in four provinces of Pakistan, possible to genome modifications and viral resistance to harsh environment. Moreover, it is also observed that humidity in Punjab and Sindh, and wind speed in Balochistan and Khyber Pakhtunkhwa have influenced the spreading of daily infected COVID-19 cases.
Significance: Current study will serve as a guideline to develop understanding of environmental factors that influence COVID-19 spread, helping policymakers to prepare and handle a catastrophe resulting from this pandemic.
Keywords: COVID-19; Environmental factors; Principal components regression; Public health.
. 2021 Jan 18;119093.
doi: 10.1016/j.lfs.2021.119093. Online ahead of print.
Statistical analysis of COVID-19 infection caused by environmental factors: Evidence from Pakistan
Muhammad Fawad[SUP] 1 [/SUP], Sumaira Mubarik[SUP] 2 [/SUP], Saima Shakil Malik[SUP] 3 [/SUP], Jingli Ren[SUP] 4 [/SUP]
Affiliations
- PMID: 33476630
- DOI: 10.1016/j.lfs.2021.119093
Abstract
Background: Coronavirus disease 2019 (COVID-19) has become a severe public health problem around the globe. Various epidemiological, statistical, and laboratory-based studies have shown that the role of temperature and other environmental factors has important influence in the transmission of coronaviruses. Scientific research is needed to answer the questions about the spread and transmission of the infection, whether people could be avoided from being infected with COVID-19 in next summer.
Aim: We aim to investigate the association of daily average temperature, daily average dew point, daily average humidity, daily average wind speed, and daily average pressure with the infection caused by this novel coronavirus in Pakistan.
Key findings: First, we check the correlation between environmental factors and daily infected cases of COVID-19; among them, temperature and dew point have positive linear relationship with daily infected cases of COVID-19. The thought-provoking findings of the present study suggested that higher temperature and dew point can contribute to a rise in COVID-19 disease in four provinces of Pakistan, possible to genome modifications and viral resistance to harsh environment. Moreover, it is also observed that humidity in Punjab and Sindh, and wind speed in Balochistan and Khyber Pakhtunkhwa have influenced the spreading of daily infected COVID-19 cases.
Significance: Current study will serve as a guideline to develop understanding of environmental factors that influence COVID-19 spread, helping policymakers to prepare and handle a catastrophe resulting from this pandemic.
Keywords: COVID-19; Environmental factors; Principal components regression; Public health.