Structural-Equation-Modelling (SEM) to analyze climatic factor's role on COVID-19 spreading

Purpose Climate seems to influence the COVID-19 spreading, but the results of the published studies are conflicting. Aim of this study was to perform a world-wide investigation to analyze the role of all the main climatic factors (CF), trying to identify the causes that led to the discrepancy of the results. Methods & Materials 134,871 data (from 209 countries) were used for the analysis. These were extrapolated from an initial data-set of 1.200.000 data. To avoid biases present in most of the previously studies, a set of specific requirements was adopted: long observation period (16 weeks),• the use of a relative time scale to synchronize the beginning of the outbreak among the countries,• multiple data collection points (up to 4 cities/per country) to overcome the problem of climate variability within a country,• the use of an appropriate technique to test the relationships among interdependent variables,• the use of a lag-period to compensate the shift between the infection exposure and the diagnosis’ confirmation.Data's analysis was performed with SEM, a flexible statistical technique for modeling causal chain of effects simultaneously. Using hypothesis-testing, this technique examines the relationships between observed variables and latent variables, in turn linked to observed variables, their indicators. With this statistical model it was possible to consider the integrated effects of all the CF on COVID-19 and, at the same time, to investigate the effects of population density (PD) too. Results The results of the analysis showed that both climate and population density significantly influence the spread of COVID-19 (p<0.001; p<0.01, respectively). Overall, climate outweighs population density (path coefficients: climate vs incidence=0.18, climate vs prevalence=0.11, PD vs incidence=0.04, PD 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. Fit indices demonstrated a good fit of the model (determination-coefficient=0.826, Root-Mean-Square-Error-of-Approximation=0.088, Standardized-Root-Mean-Square-Residual=0.078). Conclusion This study demonstrates that CF significantly influence the spread of SARS-CoV-2. However, demographic factors, together with other determinants, can affect the transmission, overcoming the protective effect of climate, where favourable.