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Research Article
Does the morbidity of SARS-CoV-2 depend on climatic factors? A global-scale study using Structural Equation Modeling
Aldo Ummarino
Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini, 4 – 20090 - Pieve Emanuele (MI) - Italy
Corresponding Author
ORCiD: https://orcid.org/0000-0001-6247-5392
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This work is licensed under a CC BY 4.0 License. Read Full License
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Declarations
- The author has confirmed that a statement listing potential conflicts of interest or lack thereof is included in the text.
Climate seems to influence the spread of SARS-CoV-2, but the findings of the studies performed so far are conflicting. To overcome these issues, we performed a global scale study considering 134,871 virologic-climatic-demographic data (209 countries, first 16 weeks of the pandemic). To analyze the relation between Covid-19, population density, and climate, a theoretical path diagram was hypothesized and tested using Structural Equation Modeling (SEM), a powerful statistical technique for the evaluation of causal assumptions. The results of the analysis showed that both climate and population density significantly influence the spread of Covid-19 (p<0.001 and p<0.01, respectively). Overall, climate outweighs population density (b-incidence=0.18, b-prevalence=0.11 versus b-incidence=0.04, b-prevalence=0.05). Among the climatic factors, irradiation plays the most relevant role, with a factor-loading of -0.77, followed by temperature (-0.56), humidity (0.52), precipitation (0.44), and pressure (0.073); for all p<0.001. An interesting outcome was the value of residual variance (ε-incidence=0.97, ε-prevalence=0.98) suggesting that other factors influence the transmission of the infection. In conclusion, this study demonstrates that climatic factors significantly influence the spread of SARS-CoV-2. However, demographic factors, together with other determinants, can affect transmission, and their influence may overcome the protective effect of climate, where favourable.
Keywords
Covid-19, SARS-CoV-2, structural equation modeling, climate, solar irradiation, temperature, humidity
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Original data analyzed
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Research Article
Does the morbidity of SARS-CoV-2 depend on climatic factors? A global-scale study using Structural Equation Modeling
Aldo Ummarino
Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini, 4 – 20090 - Pieve Emanuele (MI) - Italy
Corresponding Author
ORCiD: https://orcid.org/0000-0001-6247-5392
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
History
CURRENT STATUS: Posted
Version 1
Posted 29 Sep, 2020
No community comments so far
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Declarations:
Declarations
- The author has confirmed that a statement listing potential conflicts of interest or lack thereof is included in the text.
Climate seems to influence the spread of SARS-CoV-2, but the findings of the studies performed so far are conflicting. To overcome these issues, we performed a global scale study considering 134,871 virologic-climatic-demographic data (209 countries, first 16 weeks of the pandemic). To analyze the relation between Covid-19, population density, and climate, a theoretical path diagram was hypothesized and tested using Structural Equation Modeling (SEM), a powerful statistical technique for the evaluation of causal assumptions. The results of the analysis showed that both climate and population density significantly influence the spread of Covid-19 (p<0.001 and p<0.01, respectively). Overall, climate outweighs population density (b-incidence=0.18, b-prevalence=0.11 versus b-incidence=0.04, b-prevalence=0.05). Among the climatic factors, irradiation plays the most relevant role, with a factor-loading of -0.77, followed by temperature (-0.56), humidity (0.52), precipitation (0.44), and pressure (0.073); for all p<0.001. An interesting outcome was the value of residual variance (ε-incidence=0.97, ε-prevalence=0.98) suggesting that other factors influence the transmission of the infection. In conclusion, this study demonstrates that climatic factors significantly influence the spread of SARS-CoV-2. However, demographic factors, together with other determinants, can affect transmission, and their influence may overcome the protective effect of climate, where favourable.
Figure 1
Figure 2