Figures
- FIGURE 1
Scatterplots of the logarithm of cases per million inhabitants against temperature. Association between temperature and COVID-19 transmissibility (natural logarithm of cases per million inhabitants) across 117 countries. a) Bivariate log-linear regression of COVID-19 prevalence on temperature (p-value=<0.0001; Akaike information criterion (AIC)=439.4; Bayesian information criterion (BIC)=444.9; R-squared=0.32). b) Regression including a quadratic specification for temperature (p-value of linear term=0.008; p-value of quadratic term=0.191; AIC=439.6; BIC=447.9; R-squared=0.33). c) Multiple log-linear regression of COVID-19 prevalence on temperature (p-value=0.005; AIC=189.9; BIC=209.5; R-squared=0.72). d) Multiple regression including a quadratic specification for temperature (p-value of linear term=0.494; p-value of quadratic term=0.201; AIC=189.9; BIC=211.7; R-squared=0.73). Only countries with more than 100 cases reported as of April 10, 2020 were included. In all the multiple regressions we control for “air travel” (i.e. the number of air passengers per capita in a country), “distance from Wuhan” (i.e. the distance of the capital city of a country from Wuhan, the original epicentre of the epidemic, in thousands of kilometres), “vehicle concentration” (i.e. the number of registered vehicles per capita), “urbanisation” (i.e. the percentage of the population living in cities), “testing intensity” (i.e. the number of tests per confirmed case), “cell phone usage” (i.e. the number of cell phones per capita), and “income” (i.e. the purchasing power adjusted per capita gross domestic product in a country).
