Author: Spada, Alessia; Tucci, Francesco Antonio; Ummarino, Aldo; Ciavarella, Paolo Pio; Calà, Nicholas; Troiano, Vincenzo; Caputo, Michele; Ianzano, Raffaele; Corbo, Silvia; de Biase, Marco; Fascia, Nicola; Forte, Chiara; Gambacorta, Giorgio; Maccione, Gabriele; Prencipe, Giuseppina; Tomaiuolo, Michele; Tucci, Antonio
                    Title: Structural equation modeling to shed light on the controversial role of climate on the spread of SARS-CoV-2  Cord-id: bw9sfudk  Document date: 2021_4_16
                    ID: bw9sfudk
                    
                    Snippet: 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 among 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 evalu
                    
                    
                    
                     
                    
                    
                    
                    
                        
                            
                                Document: 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 among 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 (path coefficients: climate vs. incidence = 0.18, climate vs. prevalence = 0.11, population density vs. incidence = 0.04, population density vs. 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. 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 the transmission, and their influence may overcome the protective effect of climate, where favourable.
 
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