PMC:7510993 / 10645-11831 JSONTXT

{"target":"https://pubannotation.org/docs/sourcedb/PMC/sourceid/7510993","sourcedb":"PMC","sourceid":"7510993","source_url":"https://www.ncbi.nlm.nih.gov/pmc/7510993","text":"To control for country/region-specific observation biases, we included the length of time (measured in days) since the first confirmed COVID-19 case in each country/region and the number of COVID-19 tests conducted (as a measure of sampling effort) as covariates. In addition, we applied the trend surface method to take spatial autocorrelation into account as a covariate; we added the first eigenvector of the geo-distance matrix among the countries or regions, which was computed using the geocoordinates of the largest city, as a covariate [16]. The explanatory power of the model was evaluated by the adjusted coefficient of determination (R2). We also calculated the relative importance of each explanatory variable in a regression model according to its partial coefficient of determination and determined the predominant variables that explained the variance in the response variables. The statistical significance of each variable was determined by conducting F-test. All the explanatory variables were standardized to have a mean of zero and a variance of one before these analyses. 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